Deep-JASC: joint attenuation and scatter correction in whole-body 18F-FDG PET using a deep residual network

Objective: We demonstrate the feasibility of direct generation of attenuation and scatter-corrected images from uncorrected images (PET-nonASC) using deep residual networks in whole-body 18F-FDG PET imaging. Methods: Two- and three-dimensional deep residual networks using 2D successive slices (DL-2DS), 3D slices (DL-3DS) and 3D patches (DL-3DP) as input were constructed to perform joint attenuation and scatter correction on uncorrected whole-body images in an end-to-end fashion. We included 1150 clinical whole-body 18F-FDG PET/CT studies, among which 900, 100 and 150 patients were randomly partitioned into training, validation and independent validation sets, respectively. The images generated by the proposed approach were assessed using various evaluation metrics, including the root-mean-squared-error (RMSE) and absolute relative error (ARE ) using CT-based attenuation and scatter-corrected (CTAC) PET images as reference. PET image quantification variability was also assessed through voxel-wise standardized uptake value (SUV) bias calculation in different regions of the body (head, neck, chest, liver-lung, abdomen and pelvis). Results: Our proposed attenuation and scatter correction (Deep-JASC) algorithm provided good image quality, comparable with those produced by CTAC. Across the 150 patients of the independent external validation set, the voxel-wise REs () were � 1.72 ± 4.22, 3.75 ± 6.91 and � 3.08 ± 5.64 for DL-2DS, DL-3DS and DL-3DP, respectively. Overall, the DL-2DS approach led to superior performance compared with the other two 3D approaches. The brain and neck regions had the highest and lowest RMSE values between Deep-JASC and CTAC images, respectively. However, the largest ARE was observed in the chest (15.16 ± 3.96) and liver/lung (11.18 ± 3.23) regions for DL-2DS. DL-3DS and DL-3DP performed slightly better in the chest region, leading to AREs of 11.16 ± 3.42 and 11.69 ± 2.71, respectively (p value < 0.05). The joint histogram analysis resulted in correlation coefficients of 0.985, 0.980 and 0.981 for DL-2DS, DL-3DS and DL-3DP approaches, respectively. Conclusion: This work demonstrated the feasibility of direct attenuation and scatter correction of whole-body 18F-FDG PET images using emission-only data via a deep residual network. The proposed approach achieved accurate attenuation and scatter correction without the need for anatomical images, such as CT and MRI. The technique is applicable in a clinical setting on standalone PET or PET/MRI systems. Nevertheless, Deep-JASC showing promising quantitative accuracy, vulnerability to noise was observed, leading to pseudo hot/cold spots and/or poor organ boundary definition in the resulting PET images. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature

Monte Carlo-based evaluation of inter-crystal scatter and penetration in the PET subsystem of three GE Discovery PET/CT scanners

a b s t r a c t While there is continuing demand for higher resolution in PET systems the technological improvements are still challenged by the presence of inter-crystal scatter (ICS) and inter-crystal penetration phenomena in PET detectors, which play an important role in deterioration of the spatial resolution. Both ICS and penetration have deteriorative impact on spatial resolution of PET scanners because they can lead to inaccurate incident crystal assignments. As such, an understanding of the quantitative behavior of ICS and penetration can be beneficial whether for design of a more optimized PET detection system or for more accurate modeling of ICS and penetration effects within the image reconstruction system matrix in order to enhance the quality of reconstructed images. In this work we analyzed the quantity of ICS and penetrated events in the form of coincidences, in contrast with the other studies that have assessed ICS and penetration in the form of single photons. This was performed in the PET subsystem of three GE whole-body PET/CT scanners: Discovery RX (DRX), Discovery ST (DST), and Discovery STE (DSTE). Furthermore, as a novel study, we discriminated between ICS vs. penetration events. In order to do this, we employed the GATE (Geant4 Application for Tomographic Emission) Monte Carlo (MC) toolkit for our simulations and used our previously validated GATE models of the scanners. Developing an algorithm, purely true coincidences were discriminated from ICS-and/or penetration-induced (ICS-P) coincidences. ICS-P coincidences were also categorized into three groups: group-1 consisted of coincidence event(s) only affected by penetration (one or both). Group-2 includes coincidences where one event is affected by ICS (possibly including penetration), while the other event is not affected by ICS (i.e. penetration or no mispositioning at all). Finally in group-3, both events are affected by ICS (possibly also including penetration). The results showed that the most magnificent quantitative variations of ICS-P occur along radial direction. In DRX, more than 55% of the true coincidences are mispositioned due to ICS and/or penetration when the source is located at the end of the transaxial field of view (FOV). This value for DST and DSTE is about 45%. Incidentally, the results revealed that the quantities of ICS-P coincidences in the DST and DSTE are almost equal, while there is much smaller ICS-P in the DRX

Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients

Objectives: The aim of this study was to investigate and validate the performance of individual and ensemble machine learning models (EMLMs) based on magnetic resonance imaging (MRI) to predict neo-adjuvant chemoradiation therapy (nCRT) response in rectal cancer patients. We also aimed to study the effect of Laplacian of Gaussian (LOG) filter on EMLMs predictive performance. Methods: 98 rectal cancer patients were divided into a training (n = 53) and a validation set (n = 45). All patients underwent MRI a week before nCRT. Several features from intensity, shape and texture feature sets were extracted from MR images. SVM, Bayesian network, neural network and KNN classifiers were used individually and together for response prediction. Predictive performance was evaluated using the area under the receiver operator characteristic (ROC) curve (AUC). Results: Patients' nCRT responses included 17 patients with Grade 0, 28 with Grade 1, 34 with Grade 2, and 19 with Grade 3 according to AJCC/CAP pathologic grading. In without preprocessing MR Image the best result was for Bayesian network classifier with AUC and accuracy of 75.2 and 80.9 respectively, which was confirmed in the validation set with an AUC and accuracy of 74 and 79 respectively. In EMLMs the best result was for 4 (SVM.NN.BN.KNN) classifier EMLM with AUC and accuracy of 97.8 and 92.8 in testing and 95 and 90 in validation set respectively. Conclusions: In conclusion, we observed that machine learning methods can used to predict nCRT response in patients with rectal cancer. Preprocessing LOG filters and EL models can improve the prediction process. © 2019 Associazione Italiana di Fisica Medic

Investigation of time-of-flight benefits in an LYSO-based PET/CT scanner: A Monte Carlo study using GATE

a b s t r a c t The advent of fast scintillators yielding great light yield and/or stopping power, along with advances in photomultiplier tubes and electronics, have rekindled interest in time-of-flight (TOF) PET. Because the potential performance improvements offered by TOF PET are substantial, efforts to improve PET timing should prove very fruitful. In this study, we performed Monte Carlo simulations to explore what gains in PET performance could be achieved if the coincidence resolving time (CRT) in the LYSO-based PET component of Discovery RX PET/CT scanner were improved. For this purpose, the GATE Monte Carlo package was utilized, providing the ability to model and characterize various physical phenomena in PET imaging. For the present investigation, count rate performance and signal to noise ratio (SNR) values in different activity concentrations were simulated for different coincidence timing windows of 4, 5.85, 6, 6.5, 8, 10 and 12 ns and with different CRTs of 100-900 ps FWHM involving 50 ps FWHM increments using the NEMA scatter phantom. Strong evidence supporting robustness of the simulations was found as observed in the good agreement between measured and simulated data for the cases of estimating axial sensitivity, axial and transaxial detection position, gamma non-collinearity angle distribution and positron annihilation distance. In the non-TOF context, the results show that the random event rate can be reduced by using narrower coincidence timing window widths, demonstrating considerable enhancements in the peak noise equivalent count rate (NECR) performance. The peak NECR had increased by $ 50% when utilizing the coincidence window width of 4 ns. At the same time, utilization of TOF information resulted in improved NECR and SNR with the dramatic reduction of random coincidences as a function of CRT. For example, with CRT of 500 ps FWHM, a factor of 2.3 reduction in random rates, factor of 1.5 increase in NECR and factor of 2.1 improvement in SNR is achievable. The results of this study show that in addition to the high sensitivity of Discovery RX PET/CT scanner, the implementation of TOF with proper CRT can efficiently improve the image quality in this scanner. Having successfully simulated the DRX scanner and utilization of TOF information, our research goal is to use the Monte Carlo simulation technique to arrive at powerful, accurate and feasible reconstruction algorithms

Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test�retest and image registration analyses

Purpose: To assess the repeatability of radiomic features in magnetic resonance (MR) imaging of glioblastoma (GBM) tumors with respect to test�retest, different image registration approaches and inhomogeneity bias field correction. Methods: We analyzed MR images of 17 GBM patients including T1- and T2-weighted images (performed within the same imaging unit on two consecutive days). For image segmentation, we used a comprehensive segmentation approach including entire tumor, active area of tumor, necrotic regions in T1-weighted images, and edema regions in T2-weighted images (test studies only; registration to retest studies is discussed next). Analysis included N3, N4 as well as no bias correction performed on raw MR images. We evaluated 20 image registration approaches, generated by cross-combination of four transformation and five cost function methods. In total, 714 images (17 patients � 2 images � ((4 transformations � 5 cost functions) + 1 test image) and 2856 segmentations (714 images � 4 segmentations) were prepared for feature extraction. Various radiomic features were extracted, including the use of preprocessing filters, specifically wavelet (WAV) and Laplacian of Gaussian (LOG), as well as discretization into fixed bin width and fixed bin count (16, 32, 64, 128, and 256), Exponential, Gradient, Logarithm, Square and Square Root scales. Intraclass correlation coefficients (ICC) were calculated to assess the repeatability of MRI radiomic features (high repeatability defined as ICC � 95). Results: In our ICC results, we observed high repeatability (ICC � 95) with respect to image preprocessing, different image registration algorithms, and test�retest analysis, for example: RLNU and GLNU from GLRLM, GLNU and DNU from GLDM, Coarseness and Busyness from NGTDM, GLNU and ZP from GLSZM, and Energy and RMS from first order. Highest fraction (percent) of repeatable features was observed, among registration techniques, for the method Full Affine transformation with 12 degrees of freedom using Mutual Information cost function (mean 32.4), and among image processing methods, for the method Laplacian of Gaussian (LOG) with Sigma (2.5�4.5 mm) (mean 78.9). The trends were relatively consistent for N4, N3, or no bias correction. Conclusion: Our results showed varying performances in repeatability of MR radiomic features for GBM tumors due to test�retest and image registration. The findings have implications for appropriate usage in diagnostic and predictive models. © 2020 American Association of Physicists in Medicin

Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: test-retest and image registration analyses

To assess repeatability of radiomic features in magnetic resonance (MR) imaging of glioblastoma (GBM) tumors with respect to test-retest, different image registration approaches and inhomogeneity bias field correction

Evaluation of 99mTc-TRODAT-1 SPECT in the diagnosis of Parkinson�s disease versus other progressive movement disorders

Objective: Parkinson disease (PD), parkinsonian syndromes (PS) and essential tremor (ET) are different types of movement disorders which share some symptoms resulting in a difficulty of certain diagnosis. This study was conducted to determine the value of 99mTc-TRODAT-1 scan to differentiate PD from ET and other PS cases. Methods: Totally, 75 patients were studied including 29 PD, 6 possible PD, 22 ET and 18 PS cases. A dual-head SPECT-CT was used to perform basal ganglia (BG) imaging following administration of 99mTc-TRODAT-1. The BG uptake values were normalized to whole brain and occipital activity. All patients were followed for 2�22 months to reach a certain diagnosis. Results: Patients with ET and drug-induced parkinsonism show significantly higher normalized BG uptake as compared to the other subgroups; however, no significant difference was noted between PD and PS patients. The sensitivity and specificity of the findings for the differentiation between patients with the disease associated versus not associated with BG dysfunction were 80 and 83.3 , respectively. A predictive positive value of 82.6 was obtained using an additive scaling index defined as asymmetry and unevenness of uptake in putamen and/or caudate contralateral to the dominant side of current symptoms. Conclusions: 99mTc-TRODAT-1 scan is an appropriate method to differentiate PD or PS versus ET. A combination of scan pattern including asymmetry of BG uptake and unevenness of activity in caudate and putamen along with the side of dominant symptoms may be valuable for the differentiation of Parkinson�s disease from the other parkinsonian syndromes. © 2015, The Japanese Society of Nuclear Medicine

Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients

Objective: To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images. Methods: Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients� history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets. Results: For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95CI: 0.95�0.96), accuracy = 0.88 ± 0.046 (95 CI: 0.88�0.89), sensitivity = 0.88 ± 0.066 (95 CI = 0.87�0.9) and specificity = 0.89 ± 0.07 (95 CI = 0.87�0.9)). Conclusion: Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients. © 2021 The Author(s