Search

Background: The improvement of in vitro assessment of targeted alpha therapy (reproducibility, comparability of experiments...) requires precise evaluation of the dose delivered to the cells. To answer this need, a previous study proposed an innovative dosimetry method based on α‐spectroscopy and a specific deconvolution process to recover the spatial distribution of 212Pb isotopes inside in vitro culture wells. Nevertheless, although promising, the deconvolution method was time consuming and only tested for a simple isotope decay chain. Purpose: The purpose of this work is to propose a new matrix deconvolution method of α spectra based on a constrained‐non‐negative‐maximum‐likelihood decomposition, both faster and offering a greater modelling flexibility, allowing to study independently the kinetics of each of the daughter nuclides of complex decay chains (illustrated here with 223Ra) in in vitro culture wells. Methods: Firstly, the performance of the new method was fully evaluated through Monte Carlo simulations of in vitro irradiations. Different spatial distributions of 212Pb and 223Ra, the corresponding α spectra measured by a silicon detector and the doses delivered to the cells were simulated with Geant4. The deconvolution results were then compared to the simulation results. Secondly, measurements were carried out in culture wells without cells containing 15 kBq of 212Pb or 9.3 kBq of 223Ra, placed above silicon detectors recording α spectra in real time. The matrix deconvolution was then applied to determine the spatial and temporal distribution of all α‐emitting daughters of studied isotopes. Results: The matrix deconvolution was proved to recover the simulated distribution gradients, ensuring simulated doses within 3 % for both tested radionuclides, with errors on dose normally distributed around the reference value (consequently not exhibiting any bias), even in the case of complex decay chains as 223Ra. The experimental study of 212Pb and 223Ra showed highly inhomogeneous distributions and time evolution of the concentration gradients, consistent with the previous study. Furthermore, it highlighted the complex kinetics of 223Ra with different distributions of its α‐emitting daughters (219Rn, 215Po, 215At, 211Bi, 211Po). Conclusions: This study validates a new deconvolution method, fast and flexible, that proved to be accurate and reliable. This method allowed to reveal the complexity of isotopes kinetics in in vitro experiments, especially with complex decay chains. Experimental dosimetry, necessary to improve reliability of in vitro studies in targeted alpha therapy, is demonstrated to be feasible with the proposed method. [ABSTRACT FROM AUTHOR]

Given the constant pressure to increase patient throughput while respecting radiation protection, global body PET image quality (IQ) is not satisfactory in all patients. We first studied the association between IQ and other variables, in particular body habitus, on a digital PET/CT. Second, to improve and homogenize IQ, we evaluated a deep learning PET denoising solution (Subtle PETTM) using convolutional neural networks. We analysed retrospectively in 113 patients visual IQ (by a 5-point Likert score in two readers) and semi-quantitative IQ (by the coefficient of variation in the liver, CVliv) as well as lesion detection and quantification in native and denoised PET. In native PET, visual and semi-quantitative IQ were lower in patients with larger body habitus (p < 0.0001 for both) and in men vs. women (p ≤ 0.03 for CVliv). After PET denoising, visual IQ scores increased and became more homogeneous between patients (4.8 ± 0.3 in denoised vs. 3.6 ± 0.6 in native PET; p < 0.0001). CVliv were lower in denoised PET than in native PET, 6.9 ± 0.9% vs. 12.2 ± 1.6%; p < 0.0001. The slope calculated by linear regression of CVliv according to weight was significantly lower in denoised than in native PET (p = 0.0002), demonstrating more uniform CVliv. Lesion concordance rate between both PET series was 369/371 (99.5%), with two lesions exclusively detected in native PET. SUVmax and SUVpeak of up to the five most intense native PET lesions per patient were lower in denoised PET (p < 0.001), with an average relative bias of −7.7% and −2.8%, respectively. DL-based PET denoising by Subtle PETTM allowed [18F]FDG PET global image quality to be improved and homogenized, while maintaining satisfactory lesion detection and quantification. DL-based denoising may render body habitus adaptive PET protocols unnecessary, and pave the way for the improvement and homogenization of PET modalities. [ABSTRACT FROM AUTHOR]

Background: Arc therapy allows for better dose deposition conformation, but the radiotherapy plans (RT plans) are more complex, requiring patient-specific pre-treatment quality assurance (QA). In turn, pre-treatment QA adds to the workload. The objective of this study was to develop a predictive model of Delta4-QA results based on RT-plan complexity indices to reduce QA workload. Methods. Six complexity indices were extracted from 1632 RT VMAT plans. A machine learning (ML) model was developed for classification purpose (two classes: compliance with the QA plan or not). For more complex locations (breast, pelvis and head and neck), innovative deep hybrid learning (DHL) was trained to achieve better performance. Results. For not complex RT plans (with brain and thorax tumor locations), the ML model achieved 100% specificity and 98.9% sensitivity. However, for more complex RT plans, specificity falls to 87%. For these complex RT plans, an innovative QA classification method using DHL was developed and achieved a sensitivity of 100% and a specificity of 97.72%. Conclusions. The ML and DHL models predicted QA results with a high degree of accuracy. Our predictive QA online platform is offering substantial time savings in terms of accelerator occupancy and working time. [ABSTRACT FROM AUTHOR]

Purpose: We investigated whether artificial intelligence (AI)-based denoising halves PET acquisition time in digital PET/CT. Methods: One hundred ninety-five patients referred for [18F]FDG PET/CT were prospectively included. Body PET acquisitions were performed in list mode. Original "PET90" (90 s/bed position) was compared to reconstructed ½-duration PET (45 s/bed position) with and without AI-denoising, "PET45AI and PET45". Denoising was performed by SubtlePET™ using deep convolutional neural networks. Visual global image quality (IQ) 3-point scores and lesion detectability were evaluated. Lesion maximal and peak standardized uptake values using lean body mass (SULmax and SULpeak), metabolic volumes (MV), and liver SULmean were measured, including both standard and EARL1 (European Association of Nuclear Medicine Research Ltd) compliant SUL. Lesion-to-liver SUL ratios (LLR) and liver coefficients of variation (CVliv) were calculated. Results: PET45 showed mediocre IQ (scored poor in 8% and moderate in 68%) and lesion concordance rate with PET90 (88.7%). In PET45AI, IQ scores were similar to PET90 (P = 0.80), good in 92% and moderate in 8% for both. The lesion concordance rate between PET90 and PET45AI was 836/856 (97.7%), with 7 lesions (0.8%) only detected in PET90 and 13 (1.5%) exclusively in PET45AI. Lesion EARL1 SULpeak was not significantly different between both PET (P = 0.09). Lesion standard SULpeak, standard and EARL1 SULmax, LLR and CVliv were lower in PET45AI than in PET90 (P < 0.0001), while lesion MV and liver SULmean were higher (P < 0.0001). Good to excellent intraclass correlation coefficients (ICC) between PET90 and PET45AI were observed for lesion SUL and MV (ICC ≥ 0.97) and for liver SULmean (ICC ≥ 0.87). Conclusion: AI allows [18F]FDG PET duration in digital PET/CT to be halved, while restoring degraded ½-duration PET image quality. Future multicentric studies, including other PET radiopharmaceuticals, are warranted. [ABSTRACT FROM AUTHOR]

Brain metastases (BM) are frequently detected during the follow-up of patients with malignant tumors, particularly in those with advanced disease. Despite a major progress in systemic anti-cancer treatments, the average overall survival of these patients remains limited (6 months from diagnosis). Also, cognitive decline is regularly reported especially in patients treated with whole brain external beam radiotherapy (WBRT), due to the absorbed radiation dose in healthy brain tissue. New targeted therapies, for an earlier and/or more specific treatment of the tumor and its microenvironment, are needed. Radioimmunotherapy (RIT), a combination of a radionuclide to a specific antibody, appears to be a promising tool. Inflammation, which is involved in multiple steps, including the early phase, of BM development is attractive as a relevant target for RIT. This review will focus on the (1) early biomarkers of inflammation in BM pertinent for RIT, (2) state of the art studies on RIT for BM, and (3) the importance of dosimetry to RIT in BM. These two last points will be addressed in comparison to the conventional EBRT treatment, particularly with respect to the balance between tumor control and healthy tissue complications. Finally, because new diagnostic imaging techniques show a potential for the detection of BM at an early stage of the disease, we focus particularly on this therapeutic window. [ABSTRACT FROM AUTHOR]

Background: With a constantly increasing number of diagnostic images performed each year, Artificial Intelligence (AI) denoising methods offer an opportunity to respond to the growing demand. However, it may affect information in the image in an unknown manner. This study quantifies the effect of AI-based denoising on FDG PET textural information in comparison to a convolution with a standard gaussian postfilter (EARL1). Methods: The study was carried out on 113 patients who underwent a digital FDG PET/CT (VEREOS, Philips Healthcare). 101 FDG avid lesions were segmented semi-automatically by a nuclear medicine physician. VOIs in the liver and lung as reference organs were contoured. PET textural features were extracted with pyradiomics. Texture features from AI denoised and EARL1 versus original PET images were compared with a Concordance Correlation Coefficient (CCC). Features with CCC values ≥ 0.85 threshold were considered concordant. Scatter plots of variable pairs with R2 coefficients of the more relevant features were computed. A Wilcoxon signed rank test to compare the absolute values between AI denoised and original images was performed. Results: The ratio of concordant features was 90/104 (86.5%) in AI denoised versus 46/104 (44.2%) with EARL1 denoising. In the reference organs, the concordant ratio for AI and EARL1 denoised images was low, respectively 12/104 (11.5%) and 7/104 (6.7%) in the liver, 26/104 (25%) and 24/104 (23.1%) in the lung. SUVpeak was stable after the application of both algorithms in comparison to SUVmax. Scatter plots of variable pairs showed that AI filtering affected more lower versus high intensity regions unlike EARL1 gaussian post filters, affecting both in a similar way. In lesions, the majority of texture features 79/100 (79%) were significantly (p<0.05) different between AI denoised and original PET images. Conclusions: Applying an AI-based denoising on FDG PET images maintains most of the lesion's texture information in contrast to EARL1-compatible Gaussian filter. Predictive features of a trained model could be thus the same, however with an adapted threshold. Artificial intelligence based denoising in PET is a very promising approach as it adapts the denoising in function of the tissue type, preserving information where it should. [ABSTRACT FROM AUTHOR]

Spirometric gating devices (SGDs) can measure the respiratory signal with high temporal resolution and accuracy. The primary objective of this study was to assess the feasibility and tolerance of a gated lung PET/CT acquisition using an SGD. The secondary objective was to compare the technical quality, accuracy, and interoperability of the SGD with that of a standard respiratory gating device, Real-Time Position Management (RPM), based on measurement of vertical thoracoabdominal displacement. Methods: A prospective phase I monocentric clinical study was performed on patients undergoing 18F-FDG PET/CT for assessment of a solitary lung nodule, staging of lung malignancy, or planning of radiotherapy. After whole-body PET/CT, a centered gated acquisition of both PET and CT was simultaneously obtained with the SGD and RPM during normal breathing. Results: Of the 46 patients who were included, 6 were prematurely excluded (1 because of hyperglycemia and 5 because of distant metastases revealed by whole-body PET/CT, leading to an unjustified extra gated acquisition). No serious adverse events were observed. Of the 40 remaining patients, the gated acquisition was prematurely stopped in 1 patient because of mask discomfort (2.5%; confidence interval [CI], 0.1%-13.2%). This event was considered patient tolerance failure. The SGD generated accurately gated PET/CT images, with more than 95% of the breathing cycle detected and high temporal resolution, in 34 of the 39 patients (87.2%; 95% CI, 60.0%-100.0%) and failed to generate a biologic tumor volume in 1 of 21 patients with increased 18F-FDG uptake (4.8%; 95% CI, 0.1%-26.5%). The quality and accuracy of respiratory signal detection and synchronization were significantly better than those obtained with RPM (P < 0.05). Conclusion: This trial supports the use of an SGD for gated lung PET/CT because of its high patient tolerance and accuracy. Although this technique seems to technically outperform RPM for gated PET/CT, further assessment of its superiority and the clinical benefit is warranted. We believe that this technique could be used as a gold standard to develop innovative approaches to eliminate respiration-induced blurring artifacts. [ABSTRACT FROM AUTHOR]

Purpose: To investigate whether MRI (RECIST 1.1, WHO criteria and the volumetric approach) or F-FDG PET/CT (PERCIST 1.0) are able to predict long-term outcome in nonsurgical patients with giant cell tumour of the tendon sheath or of the diffuse type (GCT-TS/DT). Methods: Fifteen 'nonsurgical' patients with a histological diagnosis of GCT-TS/DT were divided into two groups: symptomatic patients receiving targeted therapy and asymptomatic untreated patients. All 15 patients were evaluated by MRI of whom 10 were treated, and a subgroup of 7 patients were evaluated by PET/CT of whom 4 were treated. Early evolution was assessed according to MRI and PET/CT scans at baseline and during follow-up. Cohen's kappa coefficient was used to evaluate the degree of agreement between PERCIST 1.0, RECIST 1.1, WHO criteria, volumetric approaches and the reference standard (long-term outcome, delay 505 ± 457 days). The response rate in symptomatic patients with GCT-TS/DT receiving targeted therapy was also assessed in a larger population that included additional patients obtained from a review of the literature. Results: The kappa coefficients for agreement between RECIST/WHO/volumetric criteria and outcome (15 patients) were respectively: 0.35 ( p = 0.06), 0.26 ( p = 0.17) and 0.26 ( p = 0.17). In the PET/CT subgroup (7 patients), PERCIST was in perfect agreement with the late symptomatic evolution (kappa = 1, p < 0.05). In the treated symptomatic group including the additional patients from the literature the response rates to targeted therapies according to late symptomatic assessment, and PERCIST and RECIST criteria were: 65 % (22/34), 77 % (10/13) and 26 % (10/39). Conclusion: F-FDG PET/CT with PERCIST is a promising approach to the prediction of the long-term outcome in GCT-TS/DT and may avoid unnecessary treatments, toxicity and costs. On MRI, WHO and volumetric approaches are not more effective than RECIST using the current thresholds. [ABSTRACT FROM AUTHOR]

Purpose: Respiratory motion is a source of artifacts that reduce image quality in PET. Four dimensional (4D) PET/CT is one approach to overcome this problem. Existing techniques to limiting the effects of respiratory motions are based on prospective phase binning which requires a long acquisition duration (15-25 min). This time is uncomfortable for the patients and limits the clinical exploitation of 4D PET/CT. In this work, the authors evaluated an existing method and an alternative retrospective binning method to reduce the acquisition duration of 4D PET/CT. Methods: The authors studied an existing mixed-amplitude binning (MAB) method and an alternative binning method by mixed-phases (MPhB). Before implementing MPhB, they analyzed the regularity of the breathing patterns in patients. They studied the breathing signal drift and missing CT slices that could be challenging for implementing MAB. They compared the performance of MAB and MPhB with current binning methods to measure the maximum uptake, internal volume, and maximal range of tumor motion. Results: MPhB can be implemented depending on an optimal phase (in average, the exhalation peak phase -4.1% of the entire breathing cycle duration). Signal drift of patients was in average 35% relative to the breathing amplitude. Even after correcting this drift, MAB was feasible in 4D CT for only 64% of patients. No significant differences appeared between the different binning methods to measure the maximum uptake, internal volume, and maximal range of tumor motion. The authors also determined the inaccuracies of MAB and MPhB to measure the maximum amplitude of tumor motion with three bins (less than 3 mm for movement inferior to 12 mm, up to 6.4 mm for a 21 mm movement). Conclusions: The authors proposed an alternative binning method by mixed-phase binning that halves the acquisition duration of 4D PET/CT. Mixed-amplitude binning was challenging because of signal drift and missing CT slices. They showed that more than three bins were necessary for a more accurate measurement of the maximum amplitude of the tumor motion. However, the current 4D-CT technology limits the increase of the number of bins in 4D PET/CT because of missing CT slices. One can reconstruct 4D PET images with more bins but without attenuation/scatter correction. [ABSTRACT FROM AUTHOR]

Simple Summary: Due to the central role of magnetic resonance Imaging (MRI) in the management of patients with cancer, waiting lists exceed clinically relevant delays. For this reason, many research groups and MRI manufacturers develop algorithms as resampling and denoising models to allow faster acquisition time without deterioration in image quality. Whereas these algorithms are available in all new MRI, it is not clear how they will impact image features as well as the validity of statistical model of radiomics which use deep images characteristics to predict treatment outcome. The aim of this study was to develop resampling and denoising deep learning (DL) models and evaluate their impact on radiomics from post-Gd-T1w-MRI brain images with brain metastases. We show that resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast acquisition loses most of the radiomic-features and invalidates predictive radiomic models, DL models restore these parameters. Background: Magnetic resonance imaging (MRI) is predominant in the therapeutic management of cancer patients, unfortunately, patients have to wait a long time to get an appointment for examination. Therefore, new MRI devices include deep-learning (DL) solutions to save acquisition time. However, the impact of these algorithms on intensity and texture parameters has been poorly studied. The aim of this study was to evaluate the impact of resampling and denoising DL models on radiomics. Methods: Resampling and denoising DL model was developed on 14,243 T1 brain images from 1.5T-MRI. Radiomics were extracted from 40 brain metastases from 11 patients (2049 images). A total of 104 texture features of DL images were compared to original images with paired t-test, Pearson correlation and concordance-correlation-coefficient (CCC). Results: When two times shorter image acquisition shows strong disparities with the originals concerning the radiomics, with significant differences and loss of correlation of 79.81% and 48.08%, respectively. Interestingly, DL models restore textures with 46.15% of unstable parameters and 25.96% of low CCC and without difference for the first-order intensity parameters. Conclusions: Resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast MRI acquisition loses most of the radiomic features, DL models restore these parameters. [ABSTRACT FROM AUTHOR]

Purpose: Respiratory motion creates artifacts in positon emission tomography with computed tomography (PET/CT) images especially for lung tumors, and can alter diagnosis. To account for motion effects, respiratory gating techniques have been developed. However, the lack of measures strongly correlated with tumor motion limits their accuracy. The authors developed a real-time pneumotachograph device (SPI) allowing to sort PET and CT images depending on lung volumes. Methods: The performance of this innovative respiratory tracking system was characterized and compared to a standard system. Our experimental setup consisted in a movable platform and a thorax phantom with six fillable spheres simulating lung tumors. The accuracy of SPI to detect inhalation peaks was also determined on volunteers. A comparison with the real-time position management (RPM) device, that relies on abdominal height measurement, was then investigated. Results: Experiments showed a high accuracy of the measured signal compared to the input signal (R = 0.88 to 0.99), and of the detection of the inhalation peaks (error of 0.1 +/- 5.8 ms) necessary for prospective binning mode. Activity recovery coefficient was improved (until +39%) and the smearing effect was reduced (until 2.74 times lower) with SPI compared to ungated PET/CT acquisition. The spatial distribution of activity in spheres was similar for 4D PET gated with SPI and RPM. Significant improvement of the binning stability and matching between PET and CT were highlighted for irregular breathing patterns with SPI. Conclusions: SPI is an innovative device that provides better binning performance than the current gating device on phantom experiments. Future works will focus on patients where the authors expect a significant improvement of specificity and sensitivity of PET/CT examinations. [ABSTRACT FROM AUTHOR]