Search

The vacuum ultraviolet photoionization of the methyl peroxy radical, CH3O2, and unimolecular dissociation of internal energy selected CH3O2+ cations were investigated in the 9.7–12.0 eV energy range by synchrotron-based double imaging photoelectron photoion coincidence. A microwave discharge flow tube was employed to produce CH3O2 via the reaction of methyl radicals (CH3) with oxygen gas. After identifying and separating the different sources of CH3+ from photoionization of CH3 or dissociative photoionization of CH3O2, the high resolution slow photoelectron spectrum (SPES) of CH3O2 was obtained, exhibiting two broad bands superimposed with a complex vibrational structure. The first band of the SPES is attributed to the X3A″ and a1A′ overlapped electronic states of CH3O2+ and the second is assigned to the b1A′ electronic state with the help of theoretical calculations. The adiabatic ionization energy of CH3O2 is derived as 10.215 ± 0.015 eV, in good agreement with high-accuracy theoretical data from the literature. The vertical ionization energy of the b1A′ electronic state is measured to be 11.5 eV and this state fully dissociates into CH3+ and O2 fragments. The 0 K adiabatic appearance energy (AE0K) of the CH3+ fragment ion is determined to be 11.15 ± 0.02 eV. [ABSTRACT FROM AUTHOR]

Purpose: Many radiotherapy modalities can deliver concentrated radiation in the form of spots, such as Gamma Knife (GK), GammaPod (GP), intensity‐modulated proton therapy, and brachytherapy, and can be generalized as spot‐based treatments. These treatments have a great therapeutic advantage of creating potent target dose while sparing the surrounding normal tissues. However, global optimization to determine the spot positions, shapes, and intensities is an intractable combinatorial problem for any real 3D problem. The conventional approach adopts heuristic spot selection and intensity optimization in a sequential manner to mitigate the problem complexity. In this work, we propose a novel framework that enables global optimization of spot‐based treatment planning. Methods: The framework is based on kernel decomposition (KD) dose calculation, which models each spot dose as a scaled shift‐invariant kernel, with the reference kernels and scales pre‐calculated. During optimization, the framework incorporates Fast Fourier Transform (FFT) for objective and derivative evaluations and accommodates all spot candidates in optimization search with a temporal complexity of O(N3log N) as opposed to O(N6) complexity in the conventional beamlet framework for volume dimensions of N × N × N. We demonstrated the FFT framework using simulations with different objectives. The framework's planning performance was illustrated using clinical GK and GP cases. Results: Pre‐processing involves only a small number of reference kernels and a scale map for the KD model with marginal spatial and temporal overheads. For simulations with 512 × 512 image dimensions, plan optimization finished in ∼2 seconds with FFT, whereas it took 100× longer with the beamlet approach. For clinical cases, the FFT attained solutions within a minute with improved plan quality compared to clinical plans: better conformity and less integral dose because of using a global fine search space for optimal spots. Conclusions: The scaled shift‐invariance and FFT framework opens a new paradigm for spot‐based treatment planning, as it can substantially reduce both the spatial and temporal complexities. The framework makes global optimization for spot‐based treatment planning clinically feasible. [ABSTRACT FROM AUTHOR]

Objective: Pregnant women experience enormous psychological pressure, particularly during the late trimester. Symptoms of depression in late pregnancy may persist postpartum, increasing the incidence of postpartum depression. This study is aimed to investigate the factors influencing depressive symptoms among pregnant women in their third trimester at a Chinese tertiary hospital and provide information for effective intervention. Methods: Pregnant women in their third trimester who visited the Ningbo Women and Children's Hospital between January 1, 2020 and June 30, 2022 participated in this study. A score of ≥ 13 on the Edinburgh Postnatal Depression Scale (EPDS) was considered as positive for depressive symptom. Potential influencing factors were examined by using an online questionnaire and analyzed using multivariate logistic regression. Results: A total of 1196 participants were recruited. The mean EPDS score was 7.12 ± 4.22. The positive screening rate for depressive symptom was 9.9%. Univariate analysis showed that living with partner, annual family income, planned pregnancy, sleep quality, and partner's drinking habits were related to positive screening for depression(P < 0.05). Furthermore, multivariate logistic regression analysis showed that living away from the partner (odds ratio [OR]: 2.054, 95% confidence interval [CI]: 1.094–3.696, P = 0.02), annual family income < 150,000 Chinese Yuan (CNY; OR: 1.762, 95% CI: 1.170–2.678, P = 0.007), poor sleep quality (OR: 4.123, 95% CI: 2.764–6.163, P < 0.001), and partner's frequent drinking habit (OR: 2.227, 95% CI: 1.129–4.323, P = 0.019) were independent influencing factors for positive depression screening (P < 0.05). Conclusion: Family's economic condition, sleep quality, living with partner, and partner's drinking habits were related to positive depression screening in late pregnancy. Pregnant women with these risk factors should be given more attention and supported to avoid developing depression. [ABSTRACT FROM AUTHOR]

Objective: Evolutionary stochastic global optimization algorithms are widely used in large-scale, nonconvex problems. However, enhancing the search efficiency and repeatability of these techniques often requires well-customized approaches. This study investigates one such approach. Methods: We use particle swarm optimization (PSO) algorithm to solve a 4D radiation therapy (RT) inverse planning problem, where the key idea is to use respiratory motion as an additional degree of freedom in lung cancer RT. The primary goal is to administer a lethal dose to the tumor target while sparing surrounding healthy tissue. Our optimization iteratively adjusts radiation fluence-weights for all beam apertures across all respiratory phases. We implement three PSO-based approaches: conventionally used unconstrained, hard-constrained, and our proposed virtual search. As proof of concept, five lung cancer patient cases are optimized over ten runs using each PSO approach. For comparison, a dynamically penalized likelihood (DPL) algorithm—a popular RT optimization technique is also implemented and used. Results: The proposed technique significantly improves the robustness to random initialization while requiring fewer iteration cycles to converge across all cases. DPL manages to find the global optimum in 2 out of 5 RT cases over significantly more iterations. Conclusion: The proposed virtual search approach boosts the swarm search efficiency, and consequently, improves the optimization convergence rate and robustness for PSO. Significance: RT planning is a large-scale, nonconvex optimization problem, where finding optimal solutions in a clinically practical time is critical. Our proposed approach can potentially improve the optimization efficiency in similar time-sensitive problems. [ABSTRACT FROM AUTHOR]

Purpose To evaluate the performance of a 4-dimensional (4-D) cone-beam computed tomographic (CBCT) reconstruction scheme based on simultaneous motion estimation and image reconstruction (SMEIR) through patient studies. Methods and Materials The SMEIR algorithm contains 2 alternating steps: (1) motion-compensated CBCT reconstruction using projections from all phases to reconstruct a reference phase 4D-CBCT by explicitly considering the motion models between each different phase and (2) estimation of motion models directly from projections by matching the measured projections to the forward projection of the deformed reference phase 4D-CBCT. Four lung cancer patients were scanned for 4 to 6 minutes to obtain approximately 2000 projections for each patient. To evaluate the performance of the SMEIR algorithm on a conventional 1-minute CBCT scan, the number of projections at each phase was reduced by a factor of 5, 8, or 10 for each patient. Then, 4D-CBCTs were reconstructed from the down-sampled projections using Feldkamp-Davis-Kress, total variation (TV) minimization, prior image constrained compressive sensing (PICCS), and SMEIR. Using the 4D-CBCT reconstructed from the fully sampled projections as a reference, the relative error (RE) of reconstructed images, root mean square error (RMSE), and maximum error (MaxE) of estimated tumor positions were analyzed to quantify the performance of the SMEIR algorithm. Results The SMEIR algorithm can achieve results consistent with the reference 4D-CBCT reconstructed with many more projections per phase. With an average of 30 to 40 projections per phase, the MaxE in tumor position detection is less than 1 mm in SMEIR for all 4 patients. Conclusion The results from a limited number of patients show that SMEIR is a promising tool for high-quality 4D-CBCT reconstruction and tumor motion modeling. [ABSTRACT FROM AUTHOR]

We present a vacuum ultraviolet (VUV) lamp-based photoionization time-of-flight (TOF) mass spectrometer coupled with a capillary inlet and an aerodynamic lens to online analyze the chemical compositions of the gas- and particulate-phase of cigarette smoke of a heated tobacco product (HTP). Both phase compositions of the fresh cigarette smoke, without dilution and pretreatment, are softly photoionized and their mass spectra are measured with a time resolution of 1 s. It is shown that the gas-phase compositions with low mass are volatile organic compounds (VOCs), and the particulate-phase compositions are also clearly identified and cover the full mass range of the mass spectrometer. The time- or puff-by-puff resolved dynamic data are obtained for each species and provide abundant information to unravel the chemistry of the HTP smoke. In addition, the present results show that besides thermal vaporization, a couple of chemical reactions including pyrolysis and degradation have also occurred in the HTP smoking process, although its operation temperature is less than 350 °C. Even if not done here, this study paves the way to analyze the gas- and particulate-phase chemical compositions of a complex system in real time, like the cigarette smoke presented here, by using advanced soft ionization mass spectrometry. [Display omitted] • Particle-phase compositions of the HTP smoke measured online for the first time. • Gas-/particle-phase compositions of the HTP smoke analyzed with a resolution of 1 s. • Glycerol and nicotine mainly populated into the particle phase in the HTP smoking. • Besides vaporization, pyrolysis and degradation also occur in the HTP smoking. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) to cone-beam CT (CBCT) deformable image registration (DIR) is a crucial step in adaptive radiation therapy. Current intensity-based registration algorithms, such as demons, may fail in the context of CT-CBCT DIR because of inconsistent intensities between the two modalities. In this paper, we propose a variant of demons, called deformation with intensity simultaneously corrected (DISC), to deal with CT-CBCT DIR. DISC distinguishes itself from the original demons algorithm by performing an adaptive intensity correction step on the CBCT image at every iteration step of the demons registration. Specifically, the intensity correction of a voxel in CBCT is achieved by matching the first and the second moments of the voxel intensities inside a patch around the voxel with those on the CT image. It is expected that such a strategy can remove artifacts in the CBCT image, as well as ensuring the intensity consistency between the two modalities. DISC is implemented on computer graphics processing units in compute unified device architecture (CUDA) programming environment. The performance of DISC is evaluated on a simulated patient case and six clinical head-and-neck cancer patient data. It is found that DISC is robust against the CBCT artifacts and intensity inconsistency and significantly improves the registration accuracy when compared with the original demons. [ABSTRACT FROM AUTHOR]

We describe an imaging technique that combines near-infrared diffuse optical tomography and reconstructive ultrasound tomography (RUT). In this technique, both absorption and scattering images of turbid media are reconstructed by use of the a priori structural information obtained from computational based ultrasound tomography. Under the guidance of a RUT, the absorption and scattering images with high spatial resolution are obtained using a finite element based reconstruction algorithm. With the combined approach, we experimentally demonstrate that 2–3 mm size targets embedded in a 5 cm diameter background can be reconstructed quantitatively in terms of the absorption and reduced scattering coefficients of the target, whereas it is impossible to quantitatively resolve the targets without the ultrasonic guidance. [Copyright &y& Elsevier]

A near-infrared continuous-wave optical tomographic system for in vivo breast imaging is presented. The system primarily consists of three diode lasers at 785, 808, and 830 nm, 64×64 channel source/detector optic fibers for light delivery/receiving, and 16 computer gain controlled photomultiplier tubes for parallel detection. Central to the system is a fiber optic probe that is composed of four layers of illuminating/receiving fibers arranged in a frustum of cone-shaped configuration. This probe provides data sets for both two-dimensional (2D) and three-dimensional (3D) imaging. The system is computer controlled via Labview software. A full set of tomographic data (64×64) at one wavelength can be obtained in 4 min. All hardware components are contained in a single frame under an exam table which makes the entire hardware a compact and mobile system. The design, calibration, and performance of the imaging system are described in detail. The system is tested using tissue-like phantom experiments. Both 2D and 3D images of optical absorption and reduced scattering coefficients are obtained using powerful finite element based reconstruction algorithms. © 2003 American Institute of Physics. [ABSTRACT FROM AUTHOR]

Cigarette smoke and ozone (O 3) are two main pollutants in indoor environments and their mutual reaction can generate substantial detrimental substances having synergistic influences on the environment and human health. The aging process of cigarette smoke in the atmosphere is mainly gone through ozonolysis reaction attracting increasing attention in recent years. Herein, the ozonolysis reaction of cigarette smoke with indoor O 3 concentrations has been investigated in a Teflon chamber. The gas- and particle-phases of cigarette smoke are divided and their individual ozonolysis process can generate ultrafine particles (UFPs) with high number concentrations. The chemical compositions of the gas- and particle-phases of cigarette smoke as well as their nascent UFPs are measured and their differences during the ozonolysis have been clearly observed with vacuum ultraviolet (VUV) photoionization mass spectrometry. In particular, several representative key species of cigarette smoke, i.e. , the gas-phase furan and 2,5-dimethylfuran and the particle-phase nicotine, as well as their ozonolysis products are definitely identified and play an important role in the nucleation formation of UFPs. Their ozonolysis reaction mechanisms are also discussed. In addition, this work demonstrates that the ozonolysis of the particle-phase of cigarette smoke contributes more UFPs than that of the gas-phase. [Display omitted] • Ozonolysis reactions of cigarette smoke with indoor O 3 concentrations investigated. • Gas- and particle-phases of smoke generate ultrafine particles (UFPs) via ozonolysis. • Ozonolysis of particle-phase of smoke generates more UFPs than that of gas-phase. • Key species of smoke and their ozonolysis products are identified in mass spectra. [ABSTRACT FROM AUTHOR]

Ultrahigh-dose-rate (FLASH) irradiation has been reported to reduce normal tissue damage compared with conventional dose rate (CONV) irradiation without compromising tumor control. This proof-of-concept study aims to develop a deep learning (DL) approach to quantify the FLASH isoeffective dose (dose of CONV that would be required to produce the same effect as the given physical FLASH dose) with postirradiation mouse intestinal histology images. Eighty-four healthy C57BL/6J female mice underwent 16 MeV electron CONV (0.12 Gy/s; n = 41) or FLASH (200 Gy/s; n = 43) single fraction whole abdominal irradiation. Physical dose ranged from 12 to 16 Gy for FLASH and 11 to 15 Gy for CONV in 1 Gy increments. Four days after irradiation, 9 jejunum cross-sections from each mouse were hematoxylin and eosin stained and digitized for histological analysis. CONV data set was randomly split into training (n = 33) and testing (n = 8) data sets. ResNet101-based DL models were retrained using the CONV training data set to estimate the dose based on histological features. The classical manual crypt counting (CC) approach was implemented for model comparison. Cross-section-wise mean squared error was computed to evaluate the dose estimation accuracy of both approaches. The validated DL model was applied to the FLASH data set to map the physical FLASH dose into the isoeffective dose. The DL model achieved a cross-section-wise mean squared error of 0.20 Gy2 on the CONV testing data set compared with 0.40 Gy2 of the CC approach. Isoeffective doses estimated by the DL model for FLASH doses of 12, 13, 14, 15, and 16 Gy were 12.19 ± 0.46, 12.54 ± 0.37, 12.69 ± 0.26, 12.84 ± 0.26, and 13.03 ± 0.28 Gy, respectively. Our proposed DL model achieved accurate CONV dose estimation. The DL model results indicate that in the physical dose range of 13 to 16 Gy, the biologic dose response of small intestinal tissue to FLASH irradiation is represented by a lower isoeffective dose compared with the physical dose. Our DL approach can be a tool for studying isoeffective doses of other radiation dose modifying interventions. [ABSTRACT FROM AUTHOR]

Presents a study which highlights an analytical system for rapid assessment of site-specific microheterogeneity, of the two potential N-linked glycosylation sites of recombinant human interferon-...(IFN...). Purification of target protein from culture supernatant by immunoaffinity chromatography; Performance of on-line proteolysis by immobilized trypsin cartridge.

Purpose: To develop a method that can extend dose from two isodose surfaces (isosurfaces) to the entire patient volume, and to demonstrate its application in radiotherapy plan isodose tuning. Methods: We hypothesized that volumetric dose distribution can be extended from two isosurfaces—the 100% isosurface and a reference isosurface—with the distances to these two surfaces (L100${L_{100}}$ and Lref${L_{{\rm{ref}}}}$) as extension variables. The extension function is modeled by a three‐dimensional lookup table (LUT), where voxel dose values from clinical plans are binned by three indexes: L100${L_{100}}$, Lref${L_{{\rm{ref}}}}$, and Dref${D_{{\rm{ref}}}}$ (reference dose level). The mean and standard deviation of voxel doses in each bin are calculated and stored in LUT. Volumetric dose extension is performed voxel‐wisely by indexing the LUT with the L100${L_{100}}$, Lref${L_{{\rm{ref}}}}$, and Dref${D_{{\rm{ref}}}}$ of each query voxel. The mean dose stored in the corresponding bin is filled into the query voxel as extended dose, and the standard deviation be filled voxel‐wisely as the uncertainty of extension result. We applied dose extension in isodose tuning, which aims to tune volumetric dose distribution by isosurface dragging. We adopted extended dose as an approximate dose estimation, and combined it with dose correction strategy to achieve accurate dose tuning. Results: We collected 32 post‐operative prostate volumetric‐modulated arc therapy (VMAT) cases and built the LUT and its associated uncertainties from the doses of 27 cases. The dose extension method was tested on five cases, whose dose distributions were defined as ground truth (GT). We extended the doses from 100% and 50% GT isosurfaces to the entire volume, and evaluated the accuracy of extended doses. The 5 mm/5% gamma passing rate (GPR) of extended doses are 92.0%. The mean error is 4.5%, which is consistent to the uncertainty estimated by LUT. The dose difference in 90.5% of voxels is within two sigma and 97.5% in three sigma. The calculation time is less than 2 seconds. To simulate plan isodose tuning, we optimized a dose with less sparing on rectum (than GT dose) and defined it as a "base dose"—the dose awaiting isosurface dragging. In front‐end, the simulated isodose tuning is conducted as such that the base dose was given to plan tuner, and its 50% isosurface would be dragged to the desired position (position of 50% isosurface in GT dose). In back‐end, the output of isodose tuning is obtained by (1) extending dose from the desired isosurfaces and viewed the extended dose as an approximate dose, (2) obtaining a correction map from the base dose, and (3) applying the correction map to the extended dose. The accuracy of output—extended dose with correction—was 97.2% in GPR (3 mm/3%) and less than 1% in mean dose difference. The total calculation time is less than 2 seconds, which allows for interactive isodose tuning. Conclusions: We developed a dose extension method that generates volumetric dose distribution from two surfaces. The application of dose extension is in interactive isodose tuning. The distance‐based LUT fashion and correction strategy guarantee the computation efficiency and accuracy in isodose tuning. [ABSTRACT FROM AUTHOR]

Cigarette smoke is a dynamically changing aerosol system and the operando measurement of its complex chemical compositions is challenging. In this work, the gas- and particle-phase components of combustion cigarette smoke aerosols, which usually have different adverse influences on environment and human health, have been separated and their individual compositions are measured online by using a novel vacuum ultraviolet (VUV) photoionization time-of-flight mass spectrometer. The gas-phase compositions are clearly identified and ascribed to low-mass volatile organic compounds (VOCs), while the particle-phase are more complicated and dominated with abundant semi- or non-volatile organics. The present results demonstrate that some compositions only present in gas- or particle-phase, but some can exist in both phases. In addition, the signals of the gas- and particle-phases increase with the puff number of smoking and their dynamical processes are discussed. The puff-by-puff increase of the gas-phase components is more obvious than that of the particle-phase, implying their different mechanisms involved in the smoking. • Operando measurements of gas/particle phases of combustion cigarette smoke aerosol. • Gas-phase dominates with VOCs, while particle-phase to semi-/non-volatile organics. • Some species only present in gas or particle phase, while some exist in both phases. • Gas and particle phase releases increased with puff number, but at different rates. [ABSTRACT FROM AUTHOR]

Background/Objectives: Medulloblastoma is the most common malignant brain tumor in children. In recent decades, the therapeutic landscape has undergone significant changes, with stereotactic radiosurgery (SRS) emerging as a promising treatment for recurrent cases. Our study provides a comprehensive analysis of the long-term efficacy and safety of SRS in recurrent medulloblastomas across both pediatric and adult patients at a single institution. Methods: We retrospectively reviewed the clinical and radiological records of patients who underwent CyberKnife SRS for recurrent cranial medulloblastomas at our institution between 1998 and 2023. Follow-up data were available for 15 medulloblastomas in 10 patients. The cohort comprised eight pediatric patients (ages 3–18) and two adult patients (ages 19–75). The median age at the time of SRS was 13 years, the median tumor volume accounted for 1.9 cc, the median biologically equivalent dose (BED) was 126 Gy, and the single-fraction equivalent dose (SFED) was 18 Gy. The SRS was administered at 75% of the median isodose line. Results: Following a median follow-up of 39 months (range: 6–78), 53.3% of the medulloblastomas progressed, 13.3% regressed, and 33.3% remained stable. The 3-year local tumor control (LTC) rate for all medulloblastomas was 65%, with lower rates observed in the adult cohort (50%) and higher rates in pediatric patients (67%). The 3-year overall survival (OS) rate was 70%, with significantly higher rates in pediatric patients (75%) compared to adult patients (50%). The 3-year progression-free survival (PFS) rate was 58.3%, with higher rates in pediatric patients (60%) compared to adult patients (50%). Two pediatric patients developed radiation-induced edema, while two adult patients experienced radiation necrosis at the latest follow-up, with both adult patients passing away. Conclusions: Our study provides a complex perspective on the efficacy and safety of CyberKnife SRS in treating recurrent cranial medulloblastomas across pediatric and adult populations. The rarity of adverse radiation events (AREs) underscores the safety profile of SRS, reinforcing its role in enhancing treatment outcomes. The intricacies of symptomatic outcomes, intertwined with factors such as age, tumor location, and prior surgeries, emphasize the need for personalized treatment approaches. Our findings underscore the imperative for ongoing research and the development of more refined treatment strategies for recurrent medulloblastomas. Given the observed disparities in treatment outcomes, a more meticulous tailoring of treatment approaches becomes crucial. [ABSTRACT FROM AUTHOR]

Detection and segmentation of brain metastases (BMs) play a pivotal role in diagnosis, treatment planning, and follow-up evaluations for effective BM management. Given the rising prevalence of BM cases and its predominantly multiple onsets, automated segmentation is becoming necessary in stereotactic radiosurgery. It not only alleviates the clinician's manual workload and improves clinical workflow efficiency but also ensures treatment safety, ultimately improving patient care. Recent strides in machine learning, particularly in deep learning (DL), have revolutionized medical image segmentation, achieving state-of-the-art results. This review aims to analyze auto-segmentation strategies, characterize the utilized data, and assess the performance of cutting-edge BM segmentation methodologies. Additionally, we delve into the challenges confronting BM segmentation and share insights gleaned from our algorithmic and clinical implementation experiences. [ABSTRACT FROM AUTHOR]

[Display omitted] • This review provides a systematic summary of FDA-approved biologic-device combination products regarding their device configurations, routes of administration, formulations, instructions for use, etc. • While prefilled syringes, autoinjectors and pen injectors remain as the mainstay of medical devices, innovative modifications like dual-chamber design and novel devices like on-body injector also emerged as promising presentations. • Insulin related combination products are all delivered by pen injectors with multi-dose cartridges subcutaneously. Vaccine-device combination products are presented with PFS and administered through the intramuscular route. • Around 40 % of products employ high-concentration formulations (≥ 100 mg/mL), and 78 % of mAb products are at high concentrations. With the remarkably strong growth of the biopharmaceutical market, an increasing demand for self-administration and rising competitions attract substantial interest to the biologic-device combination products. The ease-of-use of biologic-device combination products can minimize dosing error, improve patient compliance and add value to the life-cycle management of biological products. As listed in the purple book issued by the U.S. Food and Drug Administration (FDA), a total of 98 brand biologic-device combination products have been approved with Biologic License Application from January 2000 to August 2023, where this review mainly focused on 63 products containing neither insulin nor vaccine. Prefilled syringes (PFS) and autoinjectors are the most widely adopted devices, whereas innovative modifications like needle safety guard and dual-chamber design and novel devices like on-body injector also emerged as promising presentations. All 16 insulin products employ pen injectors, while all 19 vaccine products are delivered by a PFS. This review provides a systematic summary of FDA-approved biologic-device combination products regarding their device configurations, routes of administration, formulations, instructions for use, etc. In addition, challenges and opportunities associated with biologic-device compatibility, regulatory complexity, and smart connected devices are also discussed. It is believed that evolving technologies will definitely move the boundaries of biologic-device combination product development even further. [ABSTRACT FROM AUTHOR]

We developed a fluence map prediction method that directly generates fluence maps for a given desired dose distribution without optimization for volumetric modulated arc therapy (VMAT) planning. The prediction consists of two steps. First, projections of the desired dose are calculated and then inversely mapped to fluence maps in the phantom geometry by a deep neural network. Second, a plan scaling technique is applied to scale fluence maps from phantom to patient geometry. We evaluated the performance of the proposed fluence map prediction method for 102 head and neck (H&N) and 14 prostate cancer VMAT plans by comparing the patient doses calculated from the predicted fluence maps with the given desired dose distributions. The mean dose differences were 1.42% ± 0.37%, 1.53% ± 0.44% and 1.25% ± 0.44% for the planning target volume (PTV), the region from the PTV boundary to the 50% isodose line, and the region from the 50% to the 20% isodose line, respectively. The gamma passing rate was 98.06% ± 2.64% with the 3 mm/3% criterion. The prediction time for a single VMAT plan was less than one second. In conclusion, we developed an inverse mapping-based method that predicts fluence maps for desired dose distributions with high accuracy. Our method is effectively an optimization-free inverse planning approach, which was orders of magnitude faster than fluence map optimization. Combining the proposed method with leaf sequencing has the potential to dramatically speed up VMAT treatment planning. [ABSTRACT FROM AUTHOR]

• Swallow motion may compromise the successful delivery of glottic larynx SBRT. • We developed a motion management workflow using surface image guidance. • It was found that swallow motion was common during treatment. • Our method was feasible and straightforward to implement in clinic. Involuntary motion due to swallowing cause inaccurate dose delivery during larynx radiotherapy, a deviation that may be particularly problematic during stereotactic body radiation therapy (SBRT). The goal of this study was to develop a motion management solution for larynx SBRT using surface imaging. Ten patients were recently treated on a phase II study of larynx SBRT on a LINAC equipped with a surface guidance system. A small region of the immobilization mask was manually cut open to allow surface tracking. Pre-treatment and intra-fractional CBCTs were acquired to verify internal anatomy. Patients were verbally instructed not to swallow during treatment. During treatment delivery, beam hold was initiated by the Motion Management Interface if surface motion exceeded a patient-specific threshold. Patient motion was recorded in log files and analyzed. We also performed phantom studies to assess the theoretical impact of gating on dose delivery. The frequency (6.5 ± 5.2 times per fraction) and duration (3.9 ± 2.5 seconds per swallow) of swallowing varied both between patients and fractions. The magnitude of each swallow showed mean peak amplitude at 5.8 ± 3.8 mm above baseline, mostly in the longitudinal direction. Beam duty cycle was 95.0% ± 7.0% (absolute range: 76–100%), with inefficiency most prominent in the early fractions. The 95th percentile residual motion was reduced from 3.4 mm to 2.3 mm with both verbal instruction and gating. Phantom studies confirmed dose delivery accuracy represented by gamma pass rate was improved by 5% using this approach. Laryngeal motion management using surface imaging is feasible and efficacious. Uncontrolled movement of the larynx was not uncommon during treatment, with gating reducing potential for unplanned dose deviations. Additional research is needed to determine the clinical benefit with this system. [ABSTRACT FROM AUTHOR]

Purpose: GammaPod, a stereotactic partial breast irradiator allowing highly conformal radiation dose delivery, has its unique mechanical design and treatment planning system (TPS). However, the uniqueness of the system poses challenges regarding initial GammaPod system commissioning and routine quality assurance (QA). In this study, we report POD‐DOSI, a dedicated dosimetry system for accurate and efficient commissioning and QA of GammaPod. Materials and methods: The POD‐DOSI system consists of two subsystems, POD‐Scanner and POD‐Calculator. The POD‐Scanner is an automatic ion‐chamber positioning system driven by two translational stepper motors for anterior–posterior, longitudinal, and lateral beam scanning. The stepper motors are controlled by a microcomputer through an in‐house‐developed graphical user interface, which can be remotely accessed by a laptop via wireless connection. The POD‐Calculator is a commissioned GPU‐based Monte Carlo dose calculation engine, which calculates dose by transporting particles from phase space constructed for GammaPod. In our institution, the POD‐DOSI system was used for GammaPod TPS commissioning and dose verification. The POD‐Calculator was further developed as a secondary dose calculation tool performing patient‐specific plan QA before each treatment. Results: The POD‐DOSI system has been fully evaluated and tested, both mechanically and dosimetrically, and applied successfully to drive the commissioning of our GammaPod system. The POD‐Scanner achieved 0.1 mm accuracy in ion‐chamber positioning tests. The POD‐Calculator generated dose profiles matched well with water phantom measurements and TPS calculations to <0.5 mm accuracy. For end‐to‐end test on 56 different treatment plans, in‐water point dose measurements by POD‐Scanner were within ±2.20% of the doses calculated by POD‐Calculator (range: −2.01% to 2.20%, mean: 0.04%, std_dev: 1.10%). Correspondingly, when switching the calculation medium from water to breast tissue, the point doses calculated by the POD‐Calculator were within ±1.60% of the point doses calculated by the GammaPOD TPS (range: −1.59% to 1.51%, mean: −0.02%, std_dev: 0.73%). The average three‐dimensional gamma passing rate between the GammaPod TPS doses and the POD‐Calculator doses was 97.10% under the 2%/1 mm gamma criteria. The POD‐DOSI system substantially shortened the GammaPod dosimetry commissioning time from weeks to days. Conclusion: The developed POD‐DOSI system resolves the challenges and streamlines the process of GammaPod commissioning and QA. It improves the efficiency and accuracy for both GammaPod commissioning and routine patient‐specific QA. [ABSTRACT FROM AUTHOR]

Iron ion is the common transition metal ion in atmospheric aerosol, which can affect the components and optics of secondary organic aerosol (SOA). In the current study, the atmospheric photooxidation of toluene to produce SOA in the presence of ferric chloride fine particles is simulated in a smog chamber; on-line and off-line mass spectrometry and spectroscopic instruments are used to characterize constituents and optics of SOA. Compare with SOA formed in the absence of fine particles, the laser desorption/ionization mass spectra of toluene SOA generated in the presence of ferric chloride fine particles show ion peaks of m/z = 163 and 178, the UV-Vis spectra of the extracting solution for toluene SOA have peaks near 400 and 700 nm, and the electrospray ionization mass spectra contain peaks at m/z = 248 and 300. Based on this spectral information, it is shown that gaseous methylcatechol formed from photooxidation of toluene may react with iron ion on the surface of fine particles by complexing and oxidation–reduction, resulting in methylbenzoquinone products and metallo-organic complex ions such as [Fe(III)(CH3C6H3OO)]+, [Fe(III)(CH3C6H3 OO)2]− and [Fe(III)(CH3C6H3OO)Cl2]−. These products have strong light absorption ability, resulting in an increase in the averaged mass absorption coefficient () in the 200~1000 nm range and the MAC at 365 nm (MAC365) for toluene SOA, while and MAC365 progressively increase with an increasing concentration of ferric chloride fine particles. These results serve as experimental references for the study of the formation mechanism and optical properties of metallo-organic complexes in atmospheric aerosol particles in regions experiencing high levels of fine particles of metal and automobile exhaust pollution. [ABSTRACT FROM AUTHOR]

Purpose: To assess whether the optimal gating window for each beam during lung radiation therapy with respiratory gating will be dependent on a variety of patient-specific factors, such as tumor size and location and the extent of relative tumor and organ motion.Methods and Materials: To create optimal gating treatment plans, we started from an optimized clinical plan, created a plan per respiratory phase using the same beam arrangements, and used an inverse planning optimization approach to determine the optimal gating window for each beam and optimal beam weights (ie, monitor units). Two pieces of information were used for optimization: (1) the state of the anatomy at each phase, extracted from 4-dimensional computed tomography scans; and (2) the time spent in each state, estimated from a 2-minute monitoring of the patient's breathing motion. We retrospectively studied 15 lung cancer patients clinically treated by hypofractionated conformal radiation therapy, for whom 45 to 60 Gy was administered over 3 to 15 fractions using 7 to 13 beams. Mean gross tumor volume and respiratory-induced tumor motion were 82.5 cm3 and 1.0 cm, respectively.Results: Although patients spent most of their respiratory cycle in end-exhalation (EE), our optimal gating plans used EE for only 34% of the beams. Using optimal gating, maximum and mean doses to the esophagus, heart, and spinal cord were reduced by an average of 15% to 26%, and the beam-on times were reduced by an average of 23% compared with equivalent single-phase EE gated plans (P<.034, paired 2-tailed t test).Conclusions: We introduce a personalized respiratory-gating technique in which inverse planning optimization is used to determine patient- and beam-specific gating phases toward enhancing dosimetric quality of radiation therapy treatment plans. [ABSTRACT FROM AUTHOR]

We report a synchrotron radiation vacuum ultraviolet photoionization study of the hydroperoxyl radical (HO2), a key reaction intermediate in combustion and atmospheric chemistry as well as astrochemistry, using double imaging photoelectron photoion coincidence spectroscopy. The HO2 radical is formed in a microwave discharge flow tube reactor through a set of reactions initiated by F atoms in a CH4/O2/He gas mixture. The high-resolution threshold photoelectron spectrum of HO2 in the 11 eV–12 eV energy range is acquired without interferences from other species and assigned with the aid of theoretically calculated adiabatic ionization energies (AIEs) and Franck–Condon factors. The three vibrational modes of the radical cation HO2+, the H–O stretch, the H–O–O bend, and the O–O stretch, have been identified, and their individual frequencies are measured. In addition, the AIEs of the X3A″ ground state and the a1A′ first excited electronic state of HO2+ are experimentally determined at 11.359 ± 0.003 eV and 11.639 ± 0.005 eV, respectively, in agreement with high-level theoretically computed results. Furthermore, the former AIE value provides validation of thermochemical networks used to extract the enthalpy of formation of the HO2 radical. [ABSTRACT FROM AUTHOR]

We present here a synchrotron radiation vacuum ultraviolet photoionization study of the simplest alkoxy radical, CH3O, a key reaction intermediate in atmospheric and combustion chemistry. A microwave discharge fast flow tube connected to a molecular beam sampling system is employed as a chemical reactor to initiate reactions and generate radicals. The CH3O+ cation from direct ionization of the CH3O radical is detected successfully in the photoionization mass spectrum close to its ionization threshold. In addition, after identifying and removing the contribution of the 13C-isotopic formaldehyde H213CO with the same isobaric mass m/z = 31, the high-resolution threshold photoelectron spectrum of CH3O is obtained and assigned with the aid of calculated Franck–Condon factors. The adiabatic ionization energy of CH3O is determined at 10.701 eV with an accuracy of 0.005 eV. [ABSTRACT FROM AUTHOR]

Purpose: Image reconstruction and motion model estimation in four-dimensional cone-beam CT (4D-CBCT) are conventionally handled as two sequential steps. Due to the limited number of projections at each phase, the image quality of 4D-CBCT is degraded by view aliasing artifacts, and the accuracy of subsequent motion modeling is decreased by the inferior 4D-CBCT. The objective of this work is to enhance both the image quality of 4D-CBCT and the accuracy of motion model estimation with a novel strategy enabling simultaneous motion estimation and image reconstruction (SMEIR). Methods: The proposed SMEIR algorithm consists of two alternating steps: (1) model-based iterative image reconstruction to obtain a motion-compensated primary CBCT (m-pCBCT) and (2) motion model estimation to obtain an optimal set of deformation vector fields (DVFs) between the m-pCBCT and other 4D-CBCT phases. The motion-compensated image reconstruction is based on the simultaneous algebraic reconstruction technique (SART) coupled with total variation minimization. During the forward- and backprojection of SART, measured projections from an entire set of 4D-CBCT are used for reconstruction of the m-pCBCT by utilizing the updated DVF. The DVF is estimated by matching the forward projection of the deformed m-pCBCT and measured projections of other phases of 4D-CBCT. The performance of the SMEIR algorithm is quantitatively evaluated on a 4D NCAT phantom. The quality of reconstructed 4D images and the accuracy of tumor motion trajectory are assessed by comparing with those resulting from conventional sequential 4D-CBCT reconstructions (FDK and total variation minimization) and motion estimation (demons algorithm). The performance of the SMEIR algorithm is further evaluated by reconstructing a lung cancer patient 4D-CBCT. Results: Image quality of 4D-CBCT is greatly improved by the SMEIR algorithm in both phantom and patient studies. When all projections are used to reconstruct a 3D-CBCT by FDK, motion-blurring artifacts are present, leading to a 24.4% relative reconstruction error in the NACT phantom. View aliasing artifacts are present in 4D-CBCT reconstructed by FDK from 20 projections, with a relative error of 32.1%. When total variation minimization is used to reconstruct 4D-CBCT, the relative error is 18.9%. Image quality of 4D-CBCT is substantially improved by using the SMEIR algorithm and relative error is reduced to 7.6%. The maximum error (MaxE) of tumor motion determined from the DVF obtained by demons registration on a FDK-reconstructed 4D-CBCT is 3.0, 2.3, and 7.1 mm along left-right (L-R), anterior-posterior (A-P), and superior-inferior (S-I) directions, respectively. From the DVF obtained by demons registration on 4D-CBCT reconstructed by total variation minimization, the MaxE of tumor motion is reduced to 1.5, 0.5, and 5.5 mm along L-R, A-P, and S-I directions. From the DVF estimated by SMEIR algorithm, the MaxE of tumor motion is further reduced to 0.8, 0.4, and 1.5 mm along L-R, A-P, and S-I directions, respectively. Conclusions: The proposed SMEIR algorithm is able to estimate a motion model and reconstruct motion-compensated 4D-CBCT. The SMEIR algorithm improves image reconstruction accuracy of 4D-CBCT and tumor motion trajectory estimation accuracy as compared to conventional sequential 4D-CBCT reconstruction and motion estimation. [ABSTRACT FROM AUTHOR]

Objective : Gliomas are the most common primary brain tumors. Approximately 70% of the glioma patients diagnosed with glioblastoma have an averaged overall survival (OS) of only ∼16 months. Early survival prediction is essential for treatment decision-making in glioma patients. Here we proposed an ensemble learning approach to predict the post-operative OS of glioma patients using only pre-operative MRIs. Approach : Our dataset was from the Medical Image Computing and Computer Assisted Intervention Brain Tumor Segmentation challenge 2020, which consists of multimodal pre-operative MRI scans of 235 glioma patients with survival days recorded. The backbone of our approach was a Siamese network consisting of twinned ResNet-based feature extractors followed by a 3-layer classifier. During training, the feature extractors explored traits of intra and inter-class by minimizing contrastive loss of randomly paired 2D pre-operative MRIs, and the classifier utilized the extracted features to generate labels with cost defined by cross-entropy loss. During testing, the extracted features were also utilized to define distance between the test sample and the reference composed of training data, to generate an additional predictor via K-NN classification. The final label was the ensemble classification from both the Siamese model and the K-NN model. Main results : Our approach classifies the glioma patients into 3 OS classes: long-survivors (>15 months), mid-survivors (between 10 and 15 months) and short-survivors (<10 months). The performance is assessed by the accuracy (ACC) and the area under the curve (AUC) of 3-class classification. The final result achieved an ACC of 65.22% and AUC of 0.81. Significance : Our Siamese network based ensemble learning approach demonstrated promising ability in mining discriminative features with minimal manual processing and generalization requirement. This prediction strategy can be potentially applied to assist timely clinical decision-making. [ABSTRACT FROM AUTHOR]

Purpose: Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART process is accurately and efficiently delineating organs at risk (OARs) and targets on online images, such as cone beam computed tomography (CBCT). Direct application of deep learning (DL)‐based segmentation to CBCT images suffered from issues such as low image quality and limited available contour labels for training. To overcome these obstacles to online CBCT segmentation, we propose a registration‐guided DL (RgDL) segmentation framework that integrates image registration algorithms and DL segmentation models. Methods: The RgDL framework is composed of two components: image registration and RgDL segmentation. The image registration algorithm transforms/deforms planning contours that were subsequently used as guidance by the DL model to obtain accurate final segmentations. We had two implementations of the proposed framework—Rig‐RgDL (Rig for rigid body) and Def‐RgDL (Def for deformable)—with rigid body (RB) registration or deformable image registration (DIR) as the registration algorithm, respectively, and U‐Net as the DL model architecture. The two implementations of RgDL framework were trained and evaluated on seven OARs in an institutional clinical head‐and‐neck dataset. Results: Compared to the baseline approaches using the registration or the DL alone, RgDLs achieved more accurate segmentation, as measured by higher mean Dice similarity coefficients (DSCs) and other distance‐based metrics. Rig‐RgDL achieved a DSC of 84.5% on seven OARs on average, higher than RB or DL alone by 4.5% and 4.7%. The average DSC of Def‐RgDL was 86.5%, higher than DIR or DL alone by 2.4% and 6.7%. The inference time required by the DL model component to generate final segmentations of seven OARs was less than 1 s in RgDL. By examining the contours from RgDLs and DL case by case, we found that RgDL was less susceptible to image artifacts. We also studied how the performances of RgDL and DL vary with the size of the training dataset. The DSC of DL dropped by 12.1% as the number of training data decreased from 22 to 5, whereas RgDL only dropped by 3.4%. Conclusion: By incorporating the patient‐specific registration guidance to a population‐based DL segmentation model, RgDL framework overcame the obstacles associated with online CBCT segmentation, including low image quality and insufficient training data, and achieved better segmentation accuracy than baseline methods. The resulting segmentation accuracy and efficiency show promise for applying this RgDL framework for online ART. [ABSTRACT FROM AUTHOR]

BACKGROUND: High quality 4D-CBCT can be obtained by deforming a planning CT (pCT), where the deformation vector fields (DVF) are estimated by matching the forward projections of pCT and 4D-CBCT projections. The matching metric used in the previous study is the sum of squared intensity differences (SSID). The scatter signal level in CBCT projections is much higher than pCT, the SSID metric may not lead to optimal DVF.OBJECTIVE: To improve the DVF estimation accuracy, we develop a new matching metric that is less sensitive to the intensity level difference caused by the scatter signal.METHODS: The negative logarithm of correlation coefficient (NLCC) is used as the matching metric. A non-linear conjugate gradient optimization algorithm is used to estimate the DVF. A 4D NCAT phantom and an anthropomorphic thoracic phantom were used to evaluate the NLCC-based algorithm.RESULTS: In the NCAT phantom study, the relative reconstruction error is reduced from 18.0% in SSID to 14.13% in NLCC. In the thoracic phantom study, the root mean square error of the tumor motion is reduced from 1.16 mm in SSID to 0.43 mm in NLCC.CONCLUSION: NLCC metric can improve the image reconstruction and motion estimation accuracy of DVF-driven image reconstruction for 4D-CBCT. [ABSTRACT FROM AUTHOR]

Imidazole compounds are important constituents of atmospheric brown carbon. The imidazole components of aqueous secondary organic aerosol (aqSOA) that are generated from the evaporation of droplets containing pyruvaldehyde and inorganic ammonium are on-line characterized by an aerosol laser time-of-flight mass spectrometer (ALTOFMS) and off-line detected by optical spectrometry in this study. The results demonstrated that the laser desorption/ionization mass spectra of aqSOA particles that were detected by ALTOFMS contained the characteristic mass peaks of imidazoles at m/z = 28 (CH2N+), m/z = 41 (C2H3N+) and m/z = 67 (C3H4N2+). Meanwhile, the extraction solution of the aqSOA particles that were measured by off-line techniques showed that the characteristic absorption peaks at 217 nm and 282 nm appeared in the UV-Vis spectrum, and the stretching vibration peaks of C-N bond and C=N bond emerged in the infrared spectrum. Based on these spectral information, 4-methyl-imidazole and 4-methyl-imidazole-2-carboxaldehyde are identified as the main products of the reaction between pyruvaldehyde and ammonium ions. The water evaporation accelerates the formation of imidazoles inside the droplets, possibly owing to the highly concentrated environment. Anions, such as F−, CO32−, NO3−, SO42− and Cl− in the aqueous phase promote the reaction of pyruvaldehyde and ammonium ions to produce imidazole products, resulting in the averaged mass absorption coefficient () in the range of 200–600 nm of aqSOA increases, and the order of promotion is: F− > CO32− > SO42− ≈ NO3− ≈ Cl−. These results will help to analyze the constituents and optics of imidazoles and provide a useful basis for evaluating the formation process and radiative forcing of aqSOA particles. [ABSTRACT FROM AUTHOR]

Purpose: We report on our early experience of our prospective multicenter phase 1 dose- escalation study of single-fraction stereotactic partial breast irradiation (S-PBI) for early stage breast cancer after partial mastectomy using a robotic stereotactic radiation system.Methods and Materials: Thirty women with in situ or invasive breast cancer stage 0, I, or II with tumor size <3 cm treated with lumpectomy were enrolled in this phase 1 single-fraction S-PBI dose-escalation trial. Women received either 22.5, 26.5, or 30 Gy in a single fraction using a robotic stereotactic radiation system. The primary outcome was to reach tumoricidal dose of 30 Gy in a single fraction to the lumpectomy cavity without exceeding the maximum tolerated dose. Secondary outcomes were to determine dose-limiting toxicity and cosmesis. Tertiary goals were ipsilateral breast recurrence rate, distant disease-free interval, recurrence-free survival, and overall survival.Results: From June 2016 to January 2021, 11, 8, and 10 patients were treated to doses of 22.5, 26.5, or 30 Gy in a single fraction, respectively, with median follow-up being 47.9, 25.1, and 16.2 months. No patients experienced acute (<90 days) grade 3 or higher treatment-related toxicity, and maximum tolerated dose was not reached. There were 2 delayed grade 3 toxicities. Four patients (13.8%) developed fat necrosis across all 3 cohorts, which compares favorably with results from other PBI trials. No dose cohort had a statistically significant cosmetic detriment from baseline to 12 months or 24 months follow-up by patient- or physician-reported global cosmetic scores. There were no reports of disease recurrence.Conclusions: This phase 1 trial demonstrates that S-PBI can be used to safely escalate dose to 30 Gy in a single fraction with low toxicity and without detriment in cosmesis relative to baseline. [ABSTRACT FROM AUTHOR]

Purpose: Pre‐calculation of accurate dose deposition kernels for treatment planning of spot‐based radiotherapies, such as Gamma Knife (GK) and Gamma Pod (GP), can be very time‐consuming and may require large data storage with an enormous number of possible spots. We proposed a novel kernel decomposition (KD) model to address accurate and fast (real‐time) dose calculation with reduced data storage requirements for spot‐based treatment planning. The application of the KD model was demonstrated for clinical GK and GP radiotherapy platforms. Methods: The dose deposition kernel at each spot (shot position) is modeled as the product of a shift‐invariant kernel based on a reference kernel and spatially variant scale factor. The reference kernel, one for each collimator, is defined at the center of the commissioning phantom for GK and at the center of the treatment target for GP and calculated using the Monte Carlo (MC) method. The spatially variant scale factor is defined as the ratio of the mean tissue maximum ratio (TMR) at the candidate shot position to that at the reference kernel position, and the mean TMR map is calculated within the entire volume through parallel beam ray tracing on the density image followed by averaging over all source directions. The proposed KD dose calculations were compared with the MC method and with the GK and GP treatment planning system (TPS) computations for various shot positions and collimator sizes utilizing a phantom and 14 and 12 clinical plans for GK and GP, respectively. Results: For the phantom study, the KD Gamma index (3%/1 mm) passing rates were greater than 99% (median 100%) relative to the MC doses, except for the shots close to the boundary. The passing rates dropped below 90% for 8 mm (16 mm) shots positioned within ∼1 cm (∼2 cm) of the boundary. For the clinical GK plans, the KD Gamma passing rates were greater than 99% (median 100%) compared to the MC and greater than 92% (median 99%) compared to the TPS. For the clinical GP plans, the KD Gamma passing rates were greater than 95% (median 98%) compared to the MC and greater than 91% (median 97%) compared to the TPS. The scale factors were calculated in sub‐seconds with GPU implementation and only need to be calculated once before treatment plan optimization. The calculation of the dose kernel was also within sub‐seconds without requiring beam‐by‐beam calculation commonly done in the TPS. Conclusion: The proposed model can provide an accurate dose and enables real‐time dose and derivative calculations by kernel shifting and scaling without pre‐calculating or requiring large data storage for GK and GP dose deposition kernels during treatment planning. This model could be useful for spot‐based radiotherapy treatment planning by allowing an efficient global fine search for optimal spots. [ABSTRACT FROM AUTHOR]

Abstract: Persimmon waste was chemically modified by crosslinking with concentrated sulfuric acid to obtain a novel kind of adsorption gel, which was termed as crosslinked persimmon tannin (CPT), hereinafter. The adsorption behaviors of Mo(VI) with other coexisting metal ions onto the CPT gel were investigated. The gel exhibited selectivity only for Mo(VI) ions evidenced by the high value of separation factor of molybdenum and rhenium (βMo/Re =164.37), and the adsorption mechanism of Mo(VI) as a multispecies was studied. The molybdenum adsorption behavior conforms to the Langmuir model with a remarkably high adsorption capacity of 0.56mol/kg. A kinetic study for the adsorption of molybdenum at various temperatures confirmed that the endothermic adsorption process followed pseudo-second order kinetics. Moreover, its excellent adsorption properties and applicability for Mo(VI) were demonstrated by the removal and separation of Mo(VI) from different Mo–Re containing industrial wastewaters. [Copyright &y& Elsevier]

Efficient, reliable and reproducible target volume delineation is a key step in the effective planning of breast radiotherapy. However, post-operative breast target delineation is challenging as the contrast between the tumor bed volume (TBV) and normal breast tissue is relatively low in CT images. In this study, we propose to mimic the marker-guidance procedure in manual target delineation. We developed a saliency-based deep learning segmentation (SDL-Seg) algorithm for accurate TBV segmentation in post-operative breast irradiation. The SDL-Seg algorithm incorporates saliency information in the form of markers' location cues into a U-Net model. The design forces the model to encode the location-related features, which underscores regions with high saliency levels and suppresses low saliency regions. The saliency maps were generated by identifying markers on CT images. Markers' location were then converted to probability maps using a distance transformation coupled with a Gaussian filter. Subsequently, the CT images and the corresponding saliency maps formed a multi-channel input for the SDL-Seg network. Our in-house dataset was comprised of 145 prone CT images from 29 post-operative breast cancer patients, who received 5-fraction partial breast irradiation (PBI) regimen on GammaPod. The 29 patients were randomly split into training (19), validation (5) and test (5) sets. The performance of the proposed method was compared against basic U-Net. Our model achieved mean (standard deviation) of 76.4(±2.7) %, 6.76(±1.83) mm, and 1.9(±0.66) mm for Dice similarity coefficient, 95 percentile Hausdorff distance, and average symmetric surface distance respectively on the test set with computation time of below 11 seconds per one CT volume. SDL-Seg showed superior performance relative to basic U-Net for all the evaluation metrics while preserving low computation cost. The findings demonstrate that SDL-Seg is a promising approach for improving the efficiency and accuracy of the on-line treatment planning procedure of PBI, such as GammaPod based PBI. [ABSTRACT FROM AUTHOR]

Purpose: Stereotactic radiosurgery (SRS) has become a primary treatment for multiple brain metastases (BM) but may require distribution of BMs over several sessions to make delivery time and radiation toxicity manageable. Contrasting to equal fraction dose in conventional fractionation, distributed SRS delivers full dose to a subset of BMs in each session while avoiding adjacent BMs in the same session to reduce toxicity from overlapping radiation. However, current clinical treatment planning for distributed SRS relies on manual BM assignment, which can be tedious and error prone. This work describes a novel approach to automate the distribution of BM in the Gamma Knife (GK) clinical workflow. Methods: We represent each BM as an electrostatic field of the same polarity that exerts repulsive forces on other BMs in the same session. This representation naturally leads to separation of close BMs into different sessions to lower the potential energy. Indeed, the BM distribution problem can be formulated as minimization of the total potential energy from all treatment sessions subject to delivery time constraints in mixed‐integer quadratic programming (MIQP). We retrospectively studied eight clinical GK cases of multiple BM and compared the automated MIQP solution with clinically used BM distribution to demonstrate the efficacy of the proposed approach. Results: With the problem size equal to the number of BMs times the number of sessions, this MIQP can be solved in a minute on a personal workstation. The MIQP solution effectively separated BMs for a given number of treatment sessions and evened out the delivery time distribution among sessions. Compared to the clinically used manual BM distributions in paired t‐test for a similar range of delivery time variation, the automated BM distributions had lower energy objectives (range of decrease: [11% 89%]; median: 25%; P=.073), more uniformly distributed treatment volumes (range of decrease for the normalized standard deviation of volume distribution: [0.02 0.95]; median: 0.16; P=.013), more scattered BMs in each treatment session (range of increase for the mean minimum BM distance: [0 14] mm; median: 6 mm; P=.008), and lower overall V12 (range of decrease: [0.0 1.6] cc; median: 0.2 cc; P=.052). Moreover, without distribution, that is, with all BMs treated in the same session, V12 was substantially larger compared to both manual and automated BM distributions; the increase ranged from 0.1 to 16.6 cc with a median of 1.3 cc. Conclusions: The proposed approach models the clinical practice and provides an efficient solution for optimal selection of BM subsets for distributed SRS. Further evaluations are underway to establish this approach as a tool for improving clinical workflow and to facilitate systematic study on the benefits of distributed SRS treatments. [ABSTRACT FROM AUTHOR]

Organic aerosol (OA) particles, as important parts of biomass pyrolysis tar, have a negative effect on the quality of pyrolysis oil by coking, while their physical and chemical properties are rarely reported. In this work, the flash and fast pyrolysis of lignocellulosic biomass and its individual components (cellulose, xylan and lignin) were conducted in a microfluidized-bed reactor (MFBR) and a U-shaped fixed-bed reactor (U-FBR), respectively. A vacuum ultraviolet photoionization aerosol mass spectrometer (VUV-PI-AMS) was used for the online characterization of chemical components of OA particles, and their size distributions were determined by a scanning mobility particle size spectrometer (SMPS). The effect of feedstock types, pyrolysis temperatures and heating rates on the properties of OA particles from biomass pyrolysis was investigated and discussed. The experimental results show that the chemical components of OA particles from biomass pyrolysis dominated by carboxylic acids, furans, pyrans, lignin monomers and oligomers. The sizes of biomass-derived OA particles center at about 110 and 240 nm for the flash and fast pyrolysis, respectively. All detected OA particles exist in the form of liquid state. The high heating rate would improve the formation of lignin oligomers in particle phase. • The properties of organic aerosol (OA) from biomass pyrolysis were studied. • MFBR was used to obtain the OA from flash pyrolysis of biomass. • The chemical characterization of OA was conducted by online VUV-PI-AMS. • The effect of pyrolysis conditions on the formation of OA was discussed. [ABSTRACT FROM AUTHOR]

The two isomers of the propylperoxy radical 1-C3H7O2 and 2-C3H7O2, together with their individual rotamers, are identified and assigned by threshold photoelectron spectroscopy with the aid of high-level theoretical computations, from which their accurate adiabatic ionization energies are derived. This study paves the way to probing elusive peroxy radicals and their isomers in advanced mass spectrometry analysis of combustion and atmospheric reactions. [ABSTRACT FROM AUTHOR]

Deep learning (DL)-based auto-segmentation has the potential for accurate organ delineation in radiotherapy applications but requires large amounts of clean labeled data to train a robust model. However, annotating medical images is extremely time-consuming and requires clinical expertise, especially for segmentation that demands voxel-wise labels. On the other hand, medical images without annotations are abundant and highly accessible. To alleviate the influence of the limited number of clean labels, we propose a weakly supervised DL training approach using deformable image registration (DIR)-based annotations, leveraging the abundance of unlabeled data. We generate pseudo-contours by utilizing DIR to propagate atlas contours onto abundant unlabeled images and train a robust DL-based segmentation model. With 10 labeled TCIA dataset and 50 unlabeled CT scans from our institution, our model achieved Dice similarity coefficient of 87.9%, 73.4%, 73.4%, 63.2% and 61.0% on mandible, left & right parotid glands and left & right submandibular glands of TCIA test set and competitive performance on our institutional clinical dataset and a third party (PDDCA) dataset. Experimental results demonstrated the proposed method outperformed traditional multi-atlas DIR methods and fully supervised limited data training and is promising for DL-based medical image segmentation application with limited annotated data. [ABSTRACT FROM AUTHOR]

Purpose: This study reports predictive dosimetric and physiologic factors for fat necrosis after stereotactic-partial breast irradiation (S-PBI).Methods and Materials: Seventy-five patients with ductal carcinoma-in situ or invasive nonlobular epithelial histologies stage 0, I, or II, with tumor size <3 cm were enrolled in a dose-escalation, phase I S-PBI trial between January 2011 and July 2015. Fat necrosis was evaluated clinically at each follow-up. Treatment data were extracted from the Multiplan Treatment Planning System (Cyberknife, Accuray). Univariate and stepwise logistic regression analyses were conducted to identify factors associated with palpable fat necrosis.Results: With a median follow-up of 61 months (range: 4.3-99.5 months), 11 patients experienced palpable fat necrosis, 5 cases of which were painful. The median time to development of fat necrosis was 12.7 months (range, 3-42 months). On univariate analyses, higher V32.5-47.5 Gy (P < .05) and larger breast volume (P < .01) were predictive of any fat necrosis; higher V35-50 Gy (P < .05), receiving 2 treatments on consecutive days (P = .02), and higher Dmax (P = .01) were predictive of painful fat necrosis. On multivariate analyses, breast volume larger than 1063 cm3 remained a predictive factor for any fat necrosis; receiving 2 treatments on consecutive days and higher V45 Gy were predictive of painful fat necrosis. Breast laterality, planning target volume (PTV), race, body mass index, diabetic status, and tobacco or drug use were not significantly associated with fat necrosis on univariate analysis.Conclusions: Early-stage breast cancer patients treated with breast conserving surgery and S-PBI in our study had a fat necrosis rate comparable to other accelerated partial breast irradiation modalities, but S-PBI is less invasive. To reduce risk of painful fat necrosis, we recommend not delivering fractions on consecutive days; limiting V42.5 < 50 cm3, V45 < 20 cm3, V47.5 < 1 cm3, Dmax ≤ 48 Gy and PTV < 100 cm3 when feasible; and counseling patients about the increased risk for fat necrosis when constraints are not met and for those with breast volume >1000 cm3. [ABSTRACT FROM AUTHOR]

Partly due to the use of exhaustive-annotated data, deep networks have achieved impressive performance on medical image segmentation. Medical imaging data paired with noisy annotation are, however, ubiquitous, but little is known about the effect of noisy annotation on deep learning based medical image segmentation. We studied the effect of noisy annotation in the context of mandible segmentation from CT images. First, 202 images of head and neck cancer patients were collected from our clinical database, where the organs-at-risk were annotated by one of twelve planning dosimetrists. The mandibles were roughly annotated as the planning avoiding structure. Then, mandible labels were checked and corrected by a head and neck specialist to get the reference standard. At last, by varying the ratios of noisy labels in the training set, deep networks were trained and tested for mandible segmentation. The trained models were further tested on other two public datasets. Experimental results indicated that the network trained with noisy labels had worse segmentation than that trained with reference standard, and in general, fewer noisy labels led to better performance. When using 20% or less noisy cases for training, no significant difference was found on the segmentation results between the models trained by noisy or reference annotation. Cross-dataset validation results verified that the models trained with noisy data achieved competitive performance to that trained with reference standard. This study suggests that the involved network is robust to noisy annotation to some extent in mandible segmentation from CT images. It also highlights the importance of labeling quality in deep learning. In the future work, extra attention should be paid to how to utilize a small number of reference standard samples to improve the performance of deep learning with noisy annotation. [ABSTRACT FROM AUTHOR]

Purpose: Stereotactic radiosurgery (SRS) has become a standard of care for patients' with brain metastases (BMs). However, the manual multiple BMs delineation can be time‐consuming and could create an efficiency bottleneck in SRS workflow. There is a clinical need for automatic delineation and quantitative evaluation tools. In this study, building on our previous developed deep learning‐based segmentation algorithms, we developed a web‐based automated BMs segmentation and labeling platform to assist the SRS clinical workflow. Method: This platform was developed based on the Django framework, including a web client and a back‐end server. The web client enables interactions as database access, data import, and image viewing. The server performs the segmentation and labeling tasks including: skull stripping; deep learning‐based BMs segmentation; and affine registration‐based BMs labeling. Additionally, the client can display BMs contours with corresponding atlas labels, and allows further postprocessing tasks including: (a) adjusting window levels; (b) displaying/hiding specific contours; (c) removing false‐positive contours; (d) exporting contours as DICOM RTStruct files; etc. Results: We evaluated this platform on 10 clinical cases with BMs number varied from 12‐81 per case. The overall operation took about 4–5 min per patient. The segmentation accuracy was evaluated between the manual contour and automatic segmentation with several metrics. The averaged center of mass shift was 1.55 ± 0.36 mm, the Hausdorff distance was 2.98 ± 0.63 mm, the mean of surface‐to‐surface distance (SSD) was 1.06 ± 0.31 mm, and the standard deviation of SSD was 0.80 ± 0.16 mm. In addition, the initial averaged false‐positive over union (FPoU) and false‐negative rate (FNR) were 0.43 ± 0.19 and 0.15 ± 0.10 respectively. After case‐specific postprocessing, the averaged FPoU and FNR were 0.19 ± 0.10 and 0.15 ± 0.10 respectively. Conclusion: The evaluated web‐based BMs segmentation and labeling platform can substantially improve the clinical efficiency compared to manual contouring. This platform can be a useful tool for assisting SRS treatment planning and treatment follow‐up. [ABSTRACT FROM AUTHOR]

We propose a novel BIRADS-SSDL network that integrates clinically-approved breast lesion characteristics (BIRADS features) into task-oriented semi-supervised deep learning (SSDL) for accurate diagnosis of ultrasound (US) images with a small training dataset. Breast US images are converted to BIRADS-oriented feature maps (BFMs) using a distance-transformation coupled with a Gaussian filter. Then, the converted BFMs are used as the input of an SSDL network, which performs unsupervised stacked convolutional auto-encoder (SCAE) image reconstruction guided by lesion classification. This integrated multi-task learning allows SCAE to extract image features with the constraints from the lesion classification task, while the lesion classification is achieved by utilizing the SCAE encoder features with a convolutional network. We trained the BIRADS-SSDL network with an alternative learning strategy by balancing the reconstruction error and classification label prediction error. To demonstrate the effectiveness of our approach, we evaluated it using two breast US image datasets. We compared the performance of the BIRADS-SSDL network with conventional SCAE and SSDL methods that use the original images as inputs, as well as with an SCAE that use BFMs as inputs. The experimental results on two breast US datasets show that BIRADS-SSDL ranked the best among the four networks, with a classification accuracy of around 94.23 ± 3.33% and 84.38 ± 3.11% on two datasets. In the case of experiments across two datasets collected from two different institutions/and US devices, the developed BIRADS-SSDL is generalizable across the different US devices and institutions without overfitting to a single dataset and achieved satisfactory results. Furthermore, we investigate the performance of the proposed method by varying the model training strategies, lesion boundary accuracy, and Gaussian filter parameters. The experimental results showed that a pre-training strategy can help to speed up model convergence during training but with no improvement of the classification accuracy on the testing dataset. The classification accuracy decreases as the segmentation accuracy decreases. The proposed BIRADS-SSDL achieves the best results among the compared methods in each case and has the capacity to deal with multiple different datasets under one model. Compared with state-of-the-art methods, BIRADS-SSDL could be promising for effective breast US computer-aided diagnosis using small datasets. [ABSTRACT FROM AUTHOR]

A home-made vacuum ultraviolet photoionization time-of-flight mass spectrometer has been developed and coupled to an atmospheric simulation chamber operated at atmospheric pressure and to a fast flow tube at low pressure (1–10 Torr). Gas sampling from the simulation chamber is realized directly via a capillary effusive beam, and sampling from the flow tube is via a continuous molecular beam inlet. Both devices are connected simultaneously to the ionization chamber of the mass spectrometer and can be switched in-between within minutes to study gas-phase radical reactions of atmospheric interest in a large range of reaction conditions and reaction times (from milliseconds in the flow tube to hours in the simulation chamber). A cage-shaped photoionization source combined with a commercial 10.6 eV krypton lamp has been developed to provide a high ion collection efficiency along the long light path in the cage. This way, a multiplexed detection with high sensitivity down to the sub-parts per billion volume concentration range, e.g., a limit of detection of 0.3 ppbv with an accumulation time of 60 s for benzene and 1.3 ppbv for the methyl radical, is obtained. The performance and suitability of the setup are illustrated by the study of the chlorine-initiated oxidation reaction of toluene in the atmospheric simulation chamber and in the fast flow tube. Stable products and reactive intermediates have been well-determined and their reaction dynamics are discussed. [ABSTRACT FROM AUTHOR]

• A MCDM based classifier fusion method (MCF) was proposed. • A hierarchical predictive scheme embedded with MCF (H-MCF) was established. • Information from diversified modalities and different classifiers can be integrated. • Superior predictive performance achieved on ten UCI datasets and two clinical datasets. Radiomics has great prospects in terms of tumour grading, diagnosis and prediction of prognosis by analysing multifaceted data from sources such as clinical treatments, medical images, and pathology. However, exploring an effective way to manage miscellaneous clinical information, as well as to select an appropriate classifier for prediction modelling, is still demanding in a practical clinical context. In this study, we propose a multi-criterion decision-making (MCDM) based classifier fusion (MCF) strategy to combine different classifiers within an MCDM framework. A hierarchical predictive scheme (H-MCF) based on the proposed MCF is also investigated to reliably link the multi-modality features and multi-classifiers. Ten public UCI datasets and two clinical datasets were used to validate the proposed MCF and H-MCF. The experimental results showed that H-MCF has superior predictive performance when compared with the traditional fusion strategies and other fusion architectures, thus demonstrating the feasibility of the proposed H-MCF in integrating information from features of diversified modalities and different classifiers. [ABSTRACT FROM AUTHOR]

The aim of this work is to develop a novel recursive ensemble OARs segmentation (REOS) framework for accurate organs-at-risk (OARs) automatic segmentation. The REOS recursively segment individual OARs by ensembling images features extracted from an organ localization module and a contour detection module. Both modules are based on a 3D U-Net architecture. The organ localization module is trained for rough segmentation to localize a region of interest (ROI) that encompasses the to-be-delineated OAR, while the contour detection module is trained to segment the OAR within the identified ROI. In this study, the developed REOS framework is applied for brain radiotherapy on segmenting six OARs including the eyes, the brainstem (BS), the optical nerves and the chiasm. Eighty T1-weighted magnetic resonance images (MRI) from 80 brain cancer patients' cases with OARs' gold standard contours were collected for training and testing REOS. On 20 testing cases, the REOS achieve a high segmentation accuracy with Dice similarity coefficient (DSC) mean and standard deviation of 93.9% ± 1.4%, 94.5% ± 2.0%, 90.6% ± 2.7%, on the left and right eyes and the BS, respectively. On small and segmentation-challenging organs, the left and right optical nerves and the chiasm, the REOS achieves DSC of 78.0% ± 10.5%, 82.2% ± 5.9% and 71.1% ± 9.1%. The satisfactory performances demonstrated the effectiveness of the REOS in OARs segmentation. [ABSTRACT FROM AUTHOR]

Purpose: The flattening filter (FF) has traditionally been used to flatten beams or create uniform fields in conformal and intensity‐modulated radiation therapy (IMRT) but reduces the dose rate. Many studies have confirmed improvement in delivery efficiency by removing the FF, also known as flattening filter free (FFF), especially for small field stereotactic body radiation therapy (SBRT); but it is unclear if large treatment fields still favor the FFF beam. We propose a novel, unified approach to quantify delivery efficiency of the FFF and flattened beams. Methods: We modeled the FF effect by inverse conical filters and systematically studied delivery efficiency (beam‐on time, BOT) by varying the filter thickness, including the FF and FFF mode. We formulated the BOT of different beams for any arbitrary fluence map in linear programming to solve the optimal inverse conical filter that minimizes the BOT. One‐dimensional optimal filters of minimum BOT were also derived in closed form for conical fluence to gain insight for arbitrary clinical fluence maps. We evaluated the BOT of the FFF beam and flattened beam for conformal treatment fields of various dimensions ranging from 5 cm × 5 cm to 25 cm × 25 cm. We also analyzed the BOT for 698 clinical IMRT prostate fluence maps of field size 10 cm × 10 cm, 17 head‐and‐neck fluence maps of field size 15 cm × 15 cm, and additional realistic test data from 90° rotation and up to 40 cm × 40 cm enlargement of these clinical fluence maps, which were all initially generated with flattened beams. Results: The FFF beam minimized the BOT for A field size less than 20 cm in single leaf pair cases and for conformal fields of dimension less than 20 cm × 20 cm. The FFF beam also minimized the BOT for all tested prostate and head‐and‐neck cases. The median BOT ratios of the FFF beam to the flattened beam were 0.56 and 0.61 for prostate and head‐and‐neck cases, respectively. The FFF beam minimized the BOT for field size up to 30 cm × 30 cm and had similar BOTs to those of the flattened beam for field size greater than 30 cm × 30 cm in those clinically realistic test data. Conclusion: The filter modeling and BOT calculation enable us to quantify delivery efficiency of the FFF beam and flattened beam in a unified approach. The FFF beam minimized the BOT both theoretically and in simulations for all clinically relevant field sizes and fluence maps in IMRT. The results for conformal fields imply that the FFF beam requires less BOT than the flattened beam for volumetric modulated arc therapy (VMAT) treatments. The delivery efficiency consideration favors the FFF beam in intensity‐modulated treatments and may eventually lead to removal of the FF in all future linear accelerator head designs. [ABSTRACT FROM AUTHOR]

Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases. [ABSTRACT FROM AUTHOR]

Background: Recently developed stereotactic partial breast irradiation (S-PBI) allows delivery of a high biologically potent dose to the target while sparing adjacent critical organs and normal tissue. With S-PBI tumoricidal doses, accurate and precise dose delivery is critical to achieve high treatment quality. This study is to investigate both rigid and non-rigid components of target geometric error and their corresponding margins in S-PBI and identify correlated clinical factors.Methods: Forty-three early-stage breast cancer patients with implanted gold fiducial markers were enrolled in the study. Fiducial positions recorded on the orthogonal kV images on a Cyberknife system during treatment were used to estimate intra-fraction errors and composite errors (including intra-fraction errors and residual errors after patient setup). Both rigid and non-rigid components of intra-fraction and composite errors were analyzed and used to estimate rigid and non-rigid margins, respectively. Univariate and multivariate linear regressions were conducted to evaluate correlations between clinical factors and errors.Results: For the study group, the intra-fraction rigid and non-rigid errors are 2.0 ± 0.6 mm and 0.3 ± 0.2 mm, respectively. The composite rigid and non-rigid errors are 2.3 ± 0.5 mm and 1.3 ± 0.8 mm, respectively. The rigid margins in the left-right, anterior-posterior, and superior-inferior directions are estimated as 2.1, 2.4, and 2.3 mm, respectively. The estimated non-rigid margin, assumed to be isotropic, is 1.7 mm. The outer breast quadrants are more susceptible to composite errors occurrence than the inner breast quadrants. The target to chest wall distance is the clinical factor correlated with target geometric errors.Conclusions: This is the first comprehensive analysis of breast target geometric rigid and non-rigid errors in S-PBI. Upon the estimation, the non-rigid margin is comparable to rigid margin, and therefore should be included in planning target volume as it cannot be accounted for by the Cyberknife system. Treatment margins selection also need to consider the impact of relevant clinical factor. [ABSTRACT FROM AUTHOR]

Purpose To design and construct a three-dimensional (3D) anthropomorphic abdominal phantom for geometric accuracy and dose summation accuracy evaluations of deformable image registration ( DIR) algorithms for adaptive radiation therapy ( ART). Method Organ molds, including liver, kidney, spleen, stomach, vertebra, and two metastasis tumors, were 3D printed using contours from an ovarian cancer patient. The organ molds were molded with deformable gels made of different mixtures of polyvinyl chloride ( PVC) and the softener dioctyl terephthalate. Gels with different densities were obtained by a polynomial fitting curve that described the relation between the Hounsfield unit ( HU) and PVC-softener blending ratio. The rigid vertebras were constructed by molding of white cement and cellulose pulp. The final abdominal phantom was assembled by arranging all the fabricated organs inside a hollow dummy according to their anatomies, and sealed by deformable gel with averaged HU of muscle and fat. Fiducial landmarks were embedded inside the phantom for spatial accuracy and dose accumulation accuracy studies. Two channels were excavated to facilitate ionization chamber insertion for dosimetric measurements. Phantom properties such as deformable gel elasticity and HU stability were studied. The dosimetric measurement accuracy in the phantom was performed, and the DIR accuracies of three DIR algorithms available in the open source DIR toolkit- DIRART were also validated. Results The constructed deformable gel showed elastic behavior and was stable in HU values over times, proving to be a practical material for the deformable phantom. The constructed abdominal phantom consisted of realistic anatomies in terms of both anatomical shapes and densities when compared with its reference patient. The dosimetric measurements showed a good agreement with the calculated doses from the treatment planning system. Fiducial-based accuracy analysis conducted on the constructed phantom demonstrated the feasibility of applying the phantom for organ-wise DIR accuracy assessment. Conclusions We have designed and constructed an anthropomorphic abdominal deformable phantom with satisfactory elastic property, realistic organ density, and anatomy. This physical phantom can be used for routine validations of DIR geometric accuracy and dose accumulation accuracy in ART. [ABSTRACT FROM AUTHOR]

Deformable image registration (DIR) is a critical technic in adaptive radiotherapy (ART) for propagating contours between planning computerized tomography (CT) images and treatment CT/cone-beam CT (CBCT) images to account for organ deformation for treatment re-planning. To validate the ability and accuracy of DIR algorithms in organ at risk (OAR) contour mapping, ten intensity-based DIR strategies, which were classified into four categories—optical flow-based, demons-based, level-set-based and spline-based—were tested on planning CT and fractional CBCT images acquired from twenty-one head & neck (H&N) cancer patients who underwent 6~7-week intensity-modulated radiation therapy (IMRT). Three similarity metrics, i.e., the Dice similarity coefficient (DSC), the percentage error (PE) and the Hausdorff distance (HD), were employed to measure the agreement between the propagated contours and the physician-delineated ground truths of four OARs, including the vertebra (VTB), the vertebral foramen (VF), the parotid gland (PG) and the submandibular gland (SMG). It was found that the evaluated DIRs in this work did not necessarily outperform rigid registration. DIR performed better for bony structures than soft-tissue organs, and the DIR performance tended to vary for different ROIs with different degrees of deformation as the treatment proceeded. Generally, the optical flow-based DIR performed best, while the demons-based DIR usually ranked last except for a modified demons-based DISC used for CT-CBCT DIR. These experimental results suggest that the choice of a specific DIR algorithm depends on the image modality, anatomic site, magnitude of deformation and application. Therefore, careful examinations and modifications are required before accepting the auto-propagated contours, especially for automatic re-planning ART systems. [ABSTRACT FROM AUTHOR]