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• A simulation-based method was proposed to predict stopping power ratio (SPR). • The practical error in SPR was evaluated using an ICRP110 human phantom. • kV-MV dual-energy CT can provide more accurate predictions than kV-kV dual-energy CT. Purpose: This study aims to estimate the proton stopping power ratio (SPR) by using 80–120 kV and 120 kV–6 MV dual-energy CT (DECT) in a fully simulation-based approach for proton therapy dose calculations. Methods: Based on a virtual CT system, a two-step approach is applied to obtain the reference attenuation coefficient for image reconstruction. The effective atomic number (EAN) and electron density ratio (EDR) are estimated from two CT scans. The SPR is estimated using a calibration based on known materials to obtain a piecewise linear relationship between the EAN and the logarithm of the mean excitation energy, ln I m . The calibration phantoms are constructed from reference tissue materials taken from ICRU Report 44. Our approach is evaluated through using the ICRP110 human phantom. The respective influences of noise and beam hardening effects are studied. Results: With the beam hardening correction applied, the results of 120 kV–6 MV DECT are comparable to those of 80–120 kV DECT in predicting the EAN, but the former demonstrated a clear improvement in predicting the EDR and SPR. The 120 kV–6 MV DECT is able to predict the SPR within an accuracy of 10% for lung tissue and 5% for pelvis tissue, thereby outperforming the 80–120 kV DECT. Conclusions: The 120 kV–6 MV DECT is less sensitive to noise but more susceptible to beam hardening effects. By applying beam hardening correction, the 120 kV–6 MV DECT can predict the SPR more accurately than the 80–120 kV DECT. To utilize our DECT approach most effectively, high-quality reconstructed images are required. [ABSTRACT FROM AUTHOR]

This study investigated whether a deep convolutional neural network (DCNN) could provide improved detection of regional wall motion abnormalities (RWMAs) and differentiate among groups of coronary infarction territories from conventional 2-dimensional echocardiographic images compared with that of cardiologists, sonographers, and resident readers. An effective intervention for reduction of misreading of RWMAs is needed. The hypothesis was that a DCNN trained using echocardiographic images would provide improved detection of RWMAs in the clinical setting. A total of 300 patients with a history of myocardial infarction were enrolled. From this cohort, 3 groups of 100 patients each had infarctions of the left anterior descending (LAD) artery, the left circumflex (LCX) branch, and the right coronary artery (RCA). A total of 100 age-matched control patients with normal wall motion were selected from a database. Each case contained cardiac ultrasonographs from short-axis views at end-diastolic, mid-systolic, and end-systolic phases. After the DCNN underwent 100 steps of training, diagnostic accuracies were calculated from the test set. Independently, 10 versions of the same model were trained, and ensemble predictions were performed using those versions. For detection of the presence of WMAs, the area under the receiver-operating characteristic curve (AUC) produced by the deep learning algorithm was similar to that produced by the cardiologists and sonographer readers (0.99 vs. 0.98, respectively; p = 0.15) and significantly higher than the AUC result of the resident readers (0.99 vs. 0.90, respectively; p = 0.002). For detection of territories of WMAs, the AUC by the deep learning algorithm was similar to the AUC by the cardiologist and sonographer readers (0.97 vs. 0.95, respectively; p = 0.61) and significantly higher than the AUC by resident readers (0.97 vs. 0.83, respectively; p = 0.003). From a validation group at an independent site (n = 40), the AUC by the deep learning algorithm was 0.90. The present results support the possibility of using DCNN for automated diagnosis of RWMAs in the field of echocardiography. [ABSTRACT FROM AUTHOR]

• A virtual CBCT simulator with 36 head and neck human phantoms was developed. • Virtual CBCT images were generated by direct and scattered X-ray on a flat panel detector. • Based on the virtual CBCT images, an elemental material decomposition (EMD) was developed using Deep Learning-based EMD model. • Successful prediction of EMD for real patients/phantoms were given. • This study showed a potential of using computer vision for medical data preparation and analysis in adaptive radiation therapy. The purpose of this study is to develop a virtual CBCT simulator with a head and neck (HN) human phantom library and to demonstrate the feasibility of elemental material decomposition (EMD) for quantitative CBCT imaging using this virtual simulator. The library of 36 HN human phantoms were developed by extending the ICRP 110 adult phantoms based on human age, height, and weight statistics. To create the CBCT database for the library, a virtual CBCT simulator that simulated the direct and scattered X-ray on a flat panel detector using ray-tracing and deep-learning (DL) models was used. Gaussian distributed noise was also included on the flat panel detector, which was evaluated using a real CBCT system. The usefulness of the virtual CBCT system was demonstrated through the application of the developed DL-based EMD model for case involving virtual phantom and real patient. The virtual simulator could generate various virtual CBCT images based on the human phantom library, and the prediction of the EMD could be successfully performed by preparing the CBCT database from the proposed virtual system, even for a real patient. The CBCT image degradation owing to the scattered X-ray and the statistical noise affected the prediction accuracy, although these effects were minimal. Furthermore, the elemental distribution using the real CBCT image was also predictable. This study demonstrated the potential of using computer vision for medical data preparation and analysis, which could have important implications for improving patient outcomes, especially in adaptive radiation therapy. [ABSTRACT FROM AUTHOR]

Recent developments in Radiation Oncology treatment planning have led to the development of software packages that facilitate automated intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT) planning. Such solutions include site-specific modules, plan library methods, and algorithm-based methods. In this study, the plan quality for prostate cancer generated by the Auto-Planning module of the Pinnacle 3 radiation therapy treatment planning system (v9.10, Fitchburg, WI) is retrospectively evaluated. The Auto-Planning module of Pinnacle 3 uses a progressive optimization algorithm. Twenty-three prostate cancer cases, which had previously been planned and treated without lymph node irradiation, were replanned using the Auto-Planning module. Dose distributions were statistically compared with those of manual planning by the paired t-test at 5% significance level. Auto-Planning was performed without any manual intervention. Planning target volume (PTV) dose and dose to rectum were comparable between Auto-Planning and manual planning. The former, however, significantly reduced the dose to the bladder and femurs. Regression analysis was performed to examine the correlation between volume overlap between bladder and PTV divided by the total bladder volume and resultant V70. The findings showed that manual planning typically exhibits a logistic way for dose constraint, whereas Auto-Planning shows a more linear tendency. By calculating the Akaike information criterion (AIC) to validate the statistical model, a reduction of interoperator variation in Auto-Planning was shown. We showed that, for prostate cancer, the Auto-Planning module provided plans that are better than or comparable with those of manual planning. By comparing our results with those previously reported for head and neck cancer treatment, we recommend the homogeneous plan quality generated by the Auto-Planning module, which exhibits less dependence on anatomic complexity. [ABSTRACT FROM AUTHOR]

Dual-energy computed tomography (DECT) imaging can measure the effective atomic number (EAN) as well as the electron density, and thus its adoption may improve dose calculations in brachytherapy and external photon/particle therapy. An expanded energy gap in dual-energy sources is expected to yield more accurate EAN estimations than conventional DECT systems, which typically span less than 100 kV. The aim of this paper is to assess a larger energy gap DECT by using a linear accelerator (LINAC) radiotherapy system with a kV X-ray imaging device, which are combined to provide X-rays in both the kV- and MV-energy ranges. Traditionally, the EAN is determined by parameterising the Hounsfield Unit; however, this is difficult in a kV-MV DECT due to different uncertainties in the reconstructed attenuation coefficient at each end of the energy spectrum. To overcome this problem, we included a new calibration step to produce the most likely linear attenuation coefficients, based upon the X-ray spectrum. To determine the X-ray spectrum, Monte Carlo calculations using GEANT4 were performed. Then the images were calibrated using information from eight inserts of known materials in a CIRS phantom (CIRS Inc., Norfolk, VA). Agreement between the estimated and empirical EANs in these inserts was within 11%. Validation was subsequently performed with the CatPhan500 phantom (The Phantom Laboratory, Salem). The estimated EAN for seven inserts agreed with the empirical values to within 3%. Accordingly, it can be concluded that, given properly reconstructed images based upon a well-determined X-ray spectrum, kV-MV DECT provides an excellent prediction for the EAN. [ABSTRACT FROM AUTHOR]

Purpose: Megavoltage computed tomographic (MVCT) imaging has been widely used for the 3-dimensional (3-D) setup of patients treated with helical tomotherapy (HT). One drawback of MVCT is its very long imaging time, the result of slow couch speeds of approximately 1 mm/s, which can be difficult for the patient to tolerate. We sought to develop an MVCT imaging method allowing faster couch speeds and to assess its accuracy for image guidance for HT.Methods and Materials: Three cadavers were scanned 4 times with couch speeds of 1, 2, 3, and 4 mm/s. The resulting MVCT images were reconstructed using an iterative reconstruction (IR) algorithm with a penalty term of total variation and with a conventional filtered back projection (FBP) algorithm. The MVCT images were registered with kilovoltage CT images, and the registration errors from the 2 reconstruction algorithms were compared. This fast MVCT imaging was tested in 3 cases of total marrow irradiation as a clinical trial.Results: The 3-D registration errors of the MVCT images reconstructed with the IR algorithm were smaller than the errors of images reconstructed with the FBP algorithm at fast couch speeds (2, 3, 4 mm/s). The scan time and imaging dose at a speed of 4 mm/s were reduced to 30% of those from a conventional coarse mode scan. For the patient imaging, faster MVCT (3 mm/s couch speed) scanning reduced the imaging time and still generated images useful for anatomic registration.Conclusions: Fast MVCT with the IR algorithm is clinically feasible for large 3-D target localization, which may reduce the overall time for the treatment procedure. This technique may also be useful for calculating daily dose distributions or organ motion analyses in HT treatment over a wide area. Automated integration of this imaging is at least needed to further assess its clinical benefits. [ABSTRACT FROM AUTHOR]

Purpose: A noninvasive diagnostic method to predict the degree of malignancy accurately would be of great help in glioma management. This study aimed to create a highly accurate machine learning model to perform glioma grading.Methods and Materials: Preoperative magnetic resonance imaging acquired for cases of glioma operated on at our institution from October 2014 through January 2018 were obtained retrospectively. Six types of magnetic resonance imaging sequences (T2-weighted image, diffusion-weighted image, apparent diffusion coefficient [ADC], fractional anisotropy, and mean kurtosis [MK]) were chosen for analysis; 476 features were extracted semiautomatically for each sequence (2856 features in total). Recursive feature elimination was used to select significant features for a machine learning model that distinguishes glioblastoma from lower-grade glioma (grades 2 and 3).Results: Fifty-five data sets from 54 cases were obtained (14 grade 2 gliomas, 12 grade 3 gliomas, and 29 glioblastomas), of which 44 and 11 data sets were used for machine learning and independent testing, respectively. We detected 504 features with significant differences (false discovery rate <0.05) between glioblastoma and lower-grade glioma. The most accurate machine learning model was created using 6 features extracted from the ADC and MK images. In the logistic regression, the area under the curve was 0.90 ± 0.05, and the accuracy of the test data set was 0.91 (10 out of 11); using a support vector machine, they were 0.93 ± 0.03 and 0.91 (10 out of 11), respectively (kernel, radial basis function; c = 1.0).Conclusions: Our machine learning model accurately predicted glioma tumor grade. The ADC and MK sequences produced particularly useful features. [ABSTRACT FROM AUTHOR]

Purpose: To develop an imaging method to characterize and map marrow composition in the entire skeletal system, and to simulate differential targeted marrow irradiation based on marrow composition.Methods and Materials: Whole-body dual energy computed tomography (DECT) images of cadavers and leukemia patients were acquired, segmented to separate bone marrow components, namely, bone, red marrow (RM), and yellow marrow (YM). DECT-derived marrow fat fraction was validated using histology of lumbar vertebrae obtained from cadavers. The fractions of RM (RMF = RM/total marrow) and YMF were calculated in each skeletal region to assess the correlation of marrow composition with sites and ages. Treatment planning was simulated to target irradiation differentially at a higher dose (18 Gy) to either RM or YM and a lower dose (12 Gy) to the rest of the skeleton.Results: A significant correlation between fat fractions obtained from DECT and cadaver histology samples was observed (r=0.861, P<.0001, Pearson). The RMF decreased in the head, neck, and chest was significantly inversely correlated with age but did not show any significant age-related changes in the abdomen and pelvis regions. Conformity of radiation to targets (RM, YM) was significantly dependent on skeletal sites. The radiation exposure was significantly reduced (P<.05, t test) to organs at risk (OARs) in RM and YM irradiation compared with standard total marrow irradiation (TMI).Conclusions: Whole-body DECT offers a new imaging technique to visualize and measure skeletal-wide marrow composition. The DECT-based treatment planning offers volumetric and site-specific precise radiation dosimetry of RM and YM, which varies with aging. Our proposed method could be used as a functional compartment of TMI for further targeted radiation to specific bone marrow environment, dose escalation, reduction of doses to OARs, or a combination of these factors. [ABSTRACT FROM AUTHOR]

Purpose: We developed a novel method which is applicable to visualize contrast according to myelin components in the human brain using relaxation time derived from quantitative parameter mapping magnetic resonance imaging (QPM-MRI).Materials and Methods: Using healthy volunteer data (n = 10), we verified that our method demonstrated that the myelin-weighted contrast increased proportionally by products R1 and R2*, i.e., QPM-myelin-weighted image, in which modified T1-weighted/T2-weighted (T1w/T2w) ratio mapping method was applied. We compared measurement values in white matter (WM) and gray matter (GM) regions of the T1w/T2w ratio and R1·R2* product maps of healthy volunteers. Linear regression analysis between each value. Mann Whitney U test between WM and GM signals in each myelin map. In addition, Additionally, QPM-myelin-weighted image was applied to a 32-year-old female MS patient.Results: Linear regression analysis showed a highly significant correlation between conventional T1w/T2w ratios and R1·R2* products derived from QPM (R = 0.73, P < 0.0001). Moreover, there is a significant difference between WM and GM structures in each myelin images (both, P < 0.0001). Additionally, in a clinical case, MS lesions enabled observation of not only MS plaques but also heterogeneous myelin signal loss associated with demyelination more clearly than T2w image and conventional T1w/T2w ratio image.Conclusion: Our myelin-weighted imaging technique using QPM may be useful for myelin visualization and is expected to become independent of measurement conditions due to having quantitative characteristics of QPM itself. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate setup error and tumor motion during beam delivery by using 4-dimensional cone beam computed tomography (4D CBCT) and to assess the adequacy of the planning target volume (PTV) margin for lung cancer patients undergoing volumetric modulated arc therapy for stereotactic body radiation therapy (VMAT-SBRT). Methods and Materials: Fifteen lung cancer patients treated by single-arc VMAT-SBRT were selected in this analysis. All patients were treated with an abdominal compressor. The gross tumor volumes were contoured on maximum inspiration and maximum expiration CT datasets from 4D CT respiratory sorting and merged into internal target volumes (ITVs). The PTV margin was isotropically taken as 5 mm. Registration was automatically performed using “pre-3D” CBCT. Treatment was performed with a D95 prescription of 50 Gy delivered in 4 fractions. The 4D tumor locations during beam delivery were determined using in-treatment 4D CBCT images acquired in each fraction. Then, the discrepancy between the actual tumor location and the ITV was evaluated in the lateral, vertical, and longitudinal directions. Results: Overall, 55 4D CBCT sets during VMAT-SBRT were successfully obtained. The amplitude of tumor motion was less than 10 mm in all directions. The average displacements between ITV and actual tumor location during treatment were 0.41 ± 0.93 mm, 0.15 ± 0.58 mm, and 0.60 ± 0.99 mm for the craniocaudal, left-right, and anteroposterior directions, respectively. The discrepancy in each phase did not exceed 5 mm in any direction. Conclusions: With in-treatment 4D CBCT, we confirmed the required PTV margins when the registration for moving target was performed using pre-3D CBCT. In-treatment 4D CBCT is a direct method for quantitatively assessing the intrafractional location of a moving target. [ABSTRACT FROM AUTHOR]

Purpose: To study the three-dimensional movement of internal tumor bed fiducial and breast skin markers, using 320-multislice computed tomography (CT); and to analyze intrafractional errors for breast cancer patients undergoing breast irradiation. Methods and Materials: This study examined 280 markers on the skin of the breast (200 markers) and on the primary tumor bed (80 markers) of 20 patients treated by external-beam photon radiotherapy. Motion assessment was analyzed in 41 respiratory phases during 20 s of cine CT in the radiotherapy position. To assess intrafractional errors resulting from respiratory motion, four-dimensional CT scans were acquired for 20 patients. Results: Motion in the anterior–posterior (A/P) and superior–inferior (S/I) directions showed a strong correlation (|r| > 0.7) with the respiratory curve for most markers (79% and 70%, respectively). The average marker displacements between maximum and minimum value during 20 s for the 200 breast skin metal markers were 1.1 ± 0.3 mm, 2.1 ± 0.6 mm, and 1.6 ± 0.4 mm in the left–right, A/P, and S/I directions, respectively. For the 80 tumor bed clips, displacements were 0.9 ± 0.2 mm in left–right, 1.7 ± 0.5 mm in A/P, and 1.1 ± 0.3 mm in S/I. There was no significant difference in the motion between breast quadrant regions or between the primary site and the other regions. Conclusions: Motion in primary breast tumors was evaluated with 320-multislice CT. Very little change was detected during individual radiation treatment fractions. [Copyright &y& Elsevier]

Abstract: Background: Recording target motion during treatment is important for verifying the irradiated region. Recently, cone-beam computed tomography (CBCT) reconstruction from portal images acquired during volumetric modulated arc therapy (VMAT), known as VMAT-CBCT, has been investigated. In this study, we developed a four-dimensional (4D) version of the VMAT-CBCT. Materials and methods: The MV portal images were sequentially acquired from an electronic portal imaging device. The flex, background, monitor unit, field size, and multi-leaf collimator masking corrections were considered during image reconstruction. A 4D VMAT-CBCT requires a respiratory signal during image acquisition. An image-based phase recognition (IBPR) method was performed using normalised cross correlation to extract a respiratory signal from the series of portal images. Results: Our original IBPR method enabled us to reconstruct 4D VMAT-CBCT with no external devices. We confirmed that 4D VMAT-CBCT was feasible for two patients and in good agreement with in-treatment 4D kV-CBCT. Conclusion: The visibility of the anatomy in 4D VMAT-CBCT reconstruction for lung cancer patients has the potential of using 4D VMAT-CBCT as a tool for verifying relative positions of tumour for each respiratory phase. [Copyright &y& Elsevier]

Purpose: To investigate the three-dimensional movement of internal fiducial markers placed near esophageal cancers using 320-multislice CT. Methods and Materials: This study examined 22 metal markers in the esophageal wall near the primary tumors of 12 patients treated with external-beam photon radiotherapy. Motion assessment was analyzed in 41 respiratory phases during 20 s of cine CT in the radiotherapy position. Results: Motion in the cranial–caudal (CC) direction showed a strong correlation (R 2 > 0.4) with the respiratory curve in most markers (73%). The average absolute amplitude of the marker movement was 1.5 ± 1.6 mm, 1.6 ± 1.7 mm, and 3.3 ± 3.3 mm in the left–right (LR), anterior–posterior (AP), and CC directions, respectively. The average marker displacements in the CC direction between peak exhalation and inhalation for the 22 clips were 1.1 mm (maximum, 5.5 mm), 3.0 mm (14.5 mm), and 5.1 mm (16.3 mm) for the upper, middle, and lower thoracic esophagus, respectively. Conclusions: Motion in primary esophagus tumor was evaluated with 320-multislice CT. According to this study, 4.3 mm CC, 1.5 mm AP, and 2.0 mm LR in the upper, 7.4 mm CC, 3.0 mm AP, and 2.4 mm LR in the middle, and 13.8 mm CC, 6.6 mm AP, and 6.8 mm LR in the lower thoracic esophagus provided coverage of tumor motion in 95% of the cases in our study population. [ABSTRACT FROM AUTHOR]