Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy.

PURPOSE: To investigate the potential benefits of FDG PET radiomic feature maps (RFMs) for target delineation in non-small cell lung cancer (NSCLC) radiotherapy. METHODS: Thirty-two NSCLC patients undergoing FDG PET/CT imaging were included. For each patient, nine grey-level co-occurrence matrix (GLCM) RFMs were generated. gross target volume (GTV) and clinical target volume (CTV) were contoured on CT (GTVCT , CTVCT ), PET (GTVPET40 , CTVPET40 ), and RFMs (GTVRFM , CTVRFM ,). Intratumoral heterogeneity areas were segmented as GTVPET50-Boost and radiomic boost target volume (RTVBoost ) on PET and RFMs, respectively. GTVCT in homogenous tumors and GTVPET40 in heterogeneous tumors were considered as GTVgold standard (GTVGS ). One-way analysis of variance was conducted to determine the threshold that finds the best conformity for GTVRFM with GTVGS . Dice similarity coefficient (DSC) and mean absolute percent error (MAPE) were calculated. Linear regression analysis was employed to report the correlations between the gold standard and RFM-derived target volumes. RESULTS: Entropy, contrast, and Haralick correlation (H-correlation) were selected for tumor segmentation. The threshold values of 80%, 50%, and 10% have the best conformity of GTVRFM-entropy , GTVRFM-contrast , and GTVRFM-H-correlation with GTVGS , respectively. The linear regression results showed a positive correlation between GTVGS and GTVRFM-entropy (r = 0.98, p < 0.001), between GTVGS and GTVRFM-contrast (r = 0.93, p < 0.001), and between GTVGS and GTVRFM-H-correlation (r = 0.91, p < 0.001). The average threshold values of 45% and 15% were resulted in the best segmentation matching between CTVRFM-entropy and CTVRFM-contrast with CTVGS , respectively. Moreover, we used RFM to determine RTVBoost in the heterogeneous tumors. Comparison of RTVBoost with GTVPET50-Boost MAPE showed the volume error differences of 31.7%, 36%, and 34.7% in RTVBoost-entropy , RTVBoost-contrast , and RTVBoost-H-correlation , respectively. CONCLUSIONS: FDG PET-based radiomics features in NSCLC demonstrated a promising potential for decision support in radiotherapy, helping radiation oncologists delineate tumors and generate accurate segmentation for heterogeneous region of tumors.

Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks.

INTRODUCTION: The purpose of this work was to assess the feasibility of acquisition time reduction in MPI-SPECT imaging using deep leering techniques through two main approaches, namely reduction of the acquisition time per projection and reduction of the number of angular projections. METHODS: SPECT imaging was performed using a fixed 90° angle dedicated dual-head cardiac SPECT camera. This study included a prospective cohort of 363 patients with various clinical indications (normal, ischemia, and infarct) referred for MPI-SPECT. For each patient, 32 projections for 20 seconds per projection were acquired using a step and shoot protocol from the right anterior oblique to the left posterior oblique view. SPECT projection data were reconstructed using the OSEM algorithm (6 iterations, 4 subsets, Butterworth post-reconstruction filter). For each patient, four different datasets were generated, namely full time (20 seconds) projections (FT), half-time (10 seconds) acquisition per projection (HT), 32 full projections (FP), and 16 half projections (HP). The image-to-image transformation via the residual network was implemented to predict FT from HT and predict FP from HP images in the projection domain. Qualitative and quantitative evaluations of the proposed framework was performed using a tenfold cross validation scheme using the root mean square error (RMSE), absolute relative error (ARE), structural similarity index, peak signal-to-noise ratio (PSNR) metrics, and clinical quantitative parameters. RESULTS: The results demonstrated that the predicted FT had better image quality than the predicted FP images. Among the generated images, predicted FT images resulted in the lowest error metrics (RMSE = 6.8 ± 2.7, ARE = 3.1 ± 1.1%) and highest similarity index and signal-to-noise ratio (SSIM = 0.97 ± 1.1, PSNR = 36.0 ± 1.4). The highest error metrics (RMSE = 32.8 ± 12.8, ARE = 16.2 ± 4.9%) and the lowest similarity and signal-to-noise ratio (SSIM = 0.93 ± 2.6, PSNR = 31.7 ± 2.9) were observed for HT images. The RMSE decreased significantly (P value < .05) for predicted FT (8.0 ± 3.6) relative to predicted FP (6.8 ± 2.7). CONCLUSION: Reducing the acquisition time per projection significantly increased the error metrics. The deep neural network effectively recovers image quality and reduces bias in quantification metrics. Further research should be undertaken to explore the impact of time reduction in gated MPI-SPECT.

Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach.

PURPOSE: Functional magnetic resonance imaging (fMRI) in resting state can be used to evaluate the functional organization of the human brain in the absence of any task or stimulus. The functional connectivity (FC) has non-stationary nature and consented to be varying over time. By considering the dynamic characteristics of the FC and using graph theoretical analysis and a machine learning approach, we aim to identify the laterality in cases of temporal lobe epilepsy (TLE). METHODS: Six global graph measures are extracted from static and dynamic functional connectivity matrices using fMRI data of 35 unilateral TLE subjects. Alterations in the time trend of the graph measures are quantified. The random forest (RF) method is used for the determination of feature importance and selection of dynamic graph features including mean, variance, skewness, kurtosis, and Shannon entropy. The selected features are used in the support vector machine (SVM) classifier to identify the left and right epileptogenic sides in patients with TLE. RESULTS: Our results for the performance of SVM demonstrate that the utility of dynamic features improves the classification outcome in terms of accuracy (88.5% for dynamic features compared with 82% for static features). Selecting the best dynamic features also elevates the accuracy to 91.5%. CONCLUSION: Accounting for the non-stationary characteristics of functional connectivity, dynamic connectivity analysis of graph measures along with machine learning approach can identify the temporal trend of some specific network features. These network features may be used as potential imaging markers in determining the epileptogenic hemisphere in patients with TLE.

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.

Impact of image reconstruction methods on quantitative accuracy and variability of FDG-PET volumetric and textural measures in solid tumors.

OBJECTIVE: This study aims to assess the impact of different image reconstruction methods on PET/CT quantitative volumetric and textural parameters and the inter-reconstruction variability of these measurements. METHODS: A total of 25 oncology patients with 65 lesions (between 2017 and 2018) and a phantom with signal-to-background ratios (SBR) of 2 and 4 were included. All images were retrospectively reconstructed using OSEM, PSF only, TOF only, and TOFPSF with 3-, 5-, and 6.4-mm Gaussian filters. The metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were measured. The relative percent error (ΔMTV and ΔTLG) with respect to true values, volume recovery coefficients, and Dice similarity coefficient, as well as inter-reconstruction variabilities were quantified and assessed. In clinical scans, textural features (coefficient of variation, skewness, and kurtosis) were determined. RESULTS: Among reconstruction methods, mean ΔMTV differed by -163.5 ± 14.1% to 6.3 ± 6.2% at SBR2 and -42.7 ± 36.7% to 8.6 ± 3.1 at SBR4. Dice similarity coefficient significantly increased by increasing SBR from 2 to 4, ranging from 25.7 to 83.4% between reconstruction methods. Mean ΔTLG was -12.0 ± 1.7 for diameters > 17 mm and -17.8 ± 7.8 for diameters ≤ 17 mm at SBR4. It was -31.7 ± 4.3 for diameters > 17 mm and -14.2 ± 5.8 for diameters ≤ 17 mm at SBR2. Textural features were prone to variations by reconstruction methods (p < 0.05). CONCLUSIONS: Inter-reconstruction variability was significantly affected by the target size, SBR, and cut-off threshold value. In small tumors, inter-reconstruction variability was noteworthy, and quantitative parameters were strongly affected. TOFPSF reconstruction with small filter size produced greater improvements in performance and accuracy in quantitative PET/CT imaging. KEY POINTS: • Quantitative volumetric PET evaluation is critical for the analysis of tumors. • However, volumetric and textural evaluation is prone to important variations according to different image reconstruction settings. • TOFPSF reconstruction with small filter size improves quantitative analysis.

Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC).

OBJECTIVE: To obtain attenuation-corrected PET images directly from non-attenuation-corrected images using a convolutional encoder-decoder network. METHODS: Brain PET images from 129 patients were evaluated. The network was designed to map non-attenuation-corrected (NAC) images to pixel-wise continuously valued measured attenuation-corrected (MAC) PET images via an encoder-decoder architecture. Image quality was evaluated using various evaluation metrics. Image quantification was assessed for 19 radiomic features in 83 brain regions as delineated using the Hammersmith atlas (n30r83). Reliability of measurements was determined using pixel-wise relative errors (RE; %) for radiomic feature values in reference MAC PET images. RESULTS: Peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) values were 39.2 ± 3.65 and 0.989 ± 0.006 for the external validation set, respectively. RE (%) of SUVmean was - 0.10 ± 2.14 for all regions, and only 3 of 83 regions depicted significant differences. However, the mean RE (%) of this region was 0.02 (range, - 0.83 to 1.18). SUVmax had mean RE (%) of - 3.87 ± 2.84 for all brain regions, and 17 regions in the brain depicted significant differences with respect to MAC images with a mean RE of - 3.99 ± 2.11 (range, - 8.46 to 0.76). Homogeneity amongst Haralick-based radiomic features had the highest number (20) of regions with significant differences with a mean RE (%) of 7.22 ± 2.99. CONCLUSIONS: Direct AC of PET images using deep convolutional encoder-decoder networks is a promising technique for brain PET images. The proposed deep learning method shows significant potential for emission-based AC in PET images with applications in PET/MRI and dedicated brain PET scanners. KEY POINTS: • We demonstrate direct emission-based attenuation correction of PET images without using anatomical information. • We performed radiomics analysis of 83 brain regions to show robustness of direct attenuation correction of PET images. • Deep learning methods have significant promise for emission-based attenuation correction in PET images with potential applications in PET/MRI and dedicated brain PET scanners.

Capturing Bone Signal in MRI of Pelvis, as a Large FOV Region, Using TWIST Sequence and Generating a 5-Class Attenuation Map for Prostate PET/MRI Imaging.

PURPOSE: Prostate imaging is a major application of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI). Currently, MRI-based attenuation correction (MRAC) for whole-body PET/MRI in which the bony structures are ignored is the main obstacle to successful implementation of the hybrid modality in the clinical work flow. Ultrashort echo time sequence captures bone signal but needs specific hardware-software and is challenging in large field of view (FOV) regions, such as pelvis. The main aims of the work are (1) to capture a part of the bone signal in pelvis using short echo time (STE) imaging based on time-resolved angiography with interleaved stochastic trajectories (TWIST) sequence and (2) to consider the bone in pelvis attenuation map (µ-map) to MRAC for PET/MRI systems. PROCEDURES: Time-resolved angiography with interleaved stochastic trajectories, which is routinely used for MR angiography with high temporal and spatial resolution, was employed for fast/STE MR imaging. Data acquisition was performed in a TE of 0.88 milliseconds (STE) and 4.86 milliseconds (long echo time [LTE]) in pelvis region. Region of interest (ROI)-based analysis was used for comparing the signal-to-noise ratio (SNR) of cortical bone in STE and LTE images. A hybrid segmentation protocol, which is comprised of image subtraction, a Fuzzy-based segmentation, and a dedicated morphologic operation, was used for generating a 5-class µ-map consisting of cortical bone, air cavity, fat, soft tissue, and background (µ-mapMR-5c). A MR-based 4-class µ-map (µ-mapMR-4c) that considered soft tissue rather than bone was generated. As such, a bilinear (µ-mapCT-ref), 5 (µ-mapCT-5c), and 4 class µ-map (µ-mapCT-4c) based on computed tomography (CT) images were generated. Finally, simulated PET data were corrected using µ-mapMR-5c (PET-MRAC5c), µ-mapMR-4c (PET-MRAC4c), µ-mapCT-5c (PET-CTAC5c), and µ-mapCT-ref (PET-CTAC). RESULTS: The ratio of SNRbone to SNRair cavity in LTE images was 0.8, this factor was increased to 4.4 in STE images. The Dice, Sensitivity, and Accuracy metrics for bone segmentation in proposed method were 72.4% ± 5.5%, 69.6% ± 7.5%, and 96.5% ± 3.5%, respectively, where the segmented CT served as reference. The mean relative error in bone regions in the simulated PET images were -13.98% ± 15%, -35.59% ± 15.41%, and 1.81% ± 12.2%, respectively, in PET-MRAC5c, PET-MRAC4c, and PET-CTAC5c where PET-CTAC served as the reference. Despite poor correlation in the joint histogram of µ-mapMR-4c versus µ-mapCT-5c (R2 > 0.78) and PET-MRAC4c versus PET-CTAC5c (R2 = 0.83), high correlations were observed in µ-mapMR-5c versus µ-mapCT-5c (R2 > 0.94) and PET-MRAC5c versus PET-CTAC5c (R2 > 0.96). CONCLUSIONS: According to the SNRSTE, pelvic bone, the cortical bone can be separate from air cavity in STE imaging based on TWIST sequence. The proposed method generated an MRI-based µ-map containing bone and air cavity that led to more accurate tracer uptake estimation than MRAC4c. Uptake estimation in hybrid PET/MRI can be improved by employing the proposed method.

Implementation of absolute quantification in small-animal SPECT imaging: Phantom and animal studies.

PURPOSE: Presence of photon attenuation severely challenges quantitative accuracy in single-photon emission computed tomography (SPECT) imaging. Subsequently, various attenuation correction methods have been developed to compensate for this degradation. The present study aims to implement an attenuation correction method and then to evaluate quantification accuracy of attenuation correction in small-animal SPECT imaging. METHODS: Images were reconstructed using an iterative reconstruction method based on the maximum-likelihood expectation maximization (MLEM) algorithm including resolution recovery. This was implemented in our designed dedicated small-animal SPECT (HiReSPECT) system. For accurate quantification, the voxel values were converted to activity concentration via a calculated calibration factor. An attenuation correction algorithm was developed based on the first-order Chang's method. Both phantom study and experimental measurements with four rats were used in order to validate the proposed method. RESULTS: The phantom experiments showed that the error of -15.5% in the estimation of activity concentration in a uniform region was reduced to +5.1% when attenuation correction was applied. For in vivo studies, the average quantitative error of -22.8 ± 6.3% (ranging from -31.2% to -14.8%) in the uncorrected images was reduced to +3.5 ± 6.7% (ranging from -6.7 to +9.8%) after applying attenuation correction. CONCLUSION: The results indicate that the proposed attenuation correction algorithm based on the first-order Chang's method, as implemented in our dedicated small-animal SPECT system, significantly improves accuracy of the quantitative analysis as well as the absolute quantification.

Is grid therapy useful for all tumors and every grid block design?

Grid therapy is a treatment technique that has been introduced for patients with advanced bulky tumors. The purpose of this study is to investigate the effect of the radiation sensitivity of the tumors and the design of the grid blocks on the clinical response of grid therapy. The Monte Carlo simulation technique is used to determine the dose distribution through a grid block that was used for a Varian 2100C linear accelerator. From the simulated dose profiles, the therapeutic ratio (TR) and the equivalent uniform dose (EUD) for different types of tumors with respect to their radiation sensitivities were calculated. These calculations were performed using the linear quadratic (LQ) and the Hug-Kellerer (H-K) models. The results of these calculations have been validated by comparison with the clinical responses of 232 patients from different publications, who were treated with grid therapy. These published results for different tumor types were used to examine the correlation between tumor radiosensitivity and the clinical response of grid therapy. Moreover, the influence of grid design on their clinical responses was investigated by using Monte Carlo simulations of grid blocks with different hole diameters and different center-to-center spacing. The results of the theoretical models and clinical data indicated higher clinical responses for the grid therapy on the patients with more radioresistant tumors. The differences between TR values for radioresistant cells and radiosensitive cells at 20 Gy and 10 Gy doses were up to 50% and 30%, respectively. Interestingly, the differences between the TR values with LQ model and H-K model were less than 4%. Moreover, the results from the Monte Carlo studies showed that grid blocks with a hole diameters of 1.0 cm and 1.25 cm may lead to about 19% higher TR relative to the grids with hole diameters smaller than 1.0 cm or larger than 1.25 cm (with 95% confidence interval). In sum-mary, the results of this study indicate that grid therapy is more effective for tumors with radioresistant characteristics than radiosensitive tumors.

Performance evaluation of a newly developed high-resolution, dual-head animal SPECT system based on the NEMA NU1-2007 standard.

Small-animal single-photon emission computed tomography (SPECT) system plays an important role in the field of drug development and investigation of potential drugs in the preclinical phase. The small-animal High-Resolution SPECT (HiReSPECT) scanner has been recently designed and developed based on compact and high-resolution detectors. The detectors are based on a high-resolution parallel hole collimator, a cesium iodide (sodium-activated) pixelated crystal array and two H8500 position-sensitive photomultiplier tubes. In this system, a full set of data cor- rections such as energy, linearity, and uniformity, together with resolution recovery option in reconstruction algorithms, are available. In this study, we assessed the performance of the system based on NEMA-NU1-2007 standards for pixelated detector cameras. Characterization of the HiReSPECT was performed by measure- ment of the physical parameters including planar and tomographic performance. The planar performance of the system was characterized with flood-field phantom for energy resolution and uniformity. Spatial resolution and sensitivity were evaluated as functions of distance with capillary tube and cylindrical source, respectively. Tomographic spatial resolution was characterized as a function of radius of rotation (ROR). A dedicated hot rod phantom and image quality phantom was used for the evaluation of overall tomographic quality of the HiReSPECT. The results showed that the planar spatial resolution was ~ 1.6 mm and ~ 2.3 mm in terms of full-width at half-maximum (FWHM) along short- and long-axis dimensions, respectively, when the source was placed on the detector surface. The integral uniformity of the system after uniformity correction was 1.7% and 1.2% in useful field of view (UFOV) and central field of view (CFOV), respectively. System sensitivity on the collimator surface was 1.31 cps/µCi and didn't vary significantly with distance. Mean tomographic spatial resolution was measured ~ 1.7 mm FWHM at the radius of rotation of 25 mm with dual-head configuration.The measured performance demonstrated that the HiReSPECT scanner has acceptable image quality and, hence, is well suited for preclinical molecular imaging research.  

Attention-based deep neural network for partial volume correction in brain 18F-FDG PET imaging.

PURPOSE: This work set out to propose an attention-based deep neural network to predict partial volume corrected images from PET data not utilizing anatomical information. METHODS: An attention-based convolutional neural network (ATB-Net) is developed to predict PVE-corrected images in brain PET imaging by concentrating on anatomical areas of the brain. The performance of the deep neural network for performing PVC without using anatomical images was evaluated for two PVC methods, including iterative Yang (IY) and reblurred Van-Cittert (RVC) approaches. The RVC and IY PVC approaches were applied to PET images to generate the reference images. The training of the U-Net network for the partial volume correction was trained twice, once without using the attention module and once with the attention module concentrating on the anatomical brain regions. RESULTS: Regarding the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and root mean square error (RMSE) metrics, the proposed ATB-Net outperformed the standard U-Net model (without attention compartment). For the RVC technique, the ATB-Net performed just marginally better than the U-Net; however, for the IY method, which is a region-wise method, the attention-based approach resulted in a substantial improvement. The mean absolute relative SUV difference and mean absolute relative bias improved by 38.02 % and 91.60 % for the RVC method and 77.47 % and 79.68 % for the IY method when using the ATB-Net model, respectively. CONCLUSIONS: Our results propose that without using anatomical data, the attention-based DL model could perform PVC on PET images, which could be employed for PVC in PET imaging.

The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods.

PURPOSE: This study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC). METHODS: We included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET scanning as a routine part of their care. All patients had a biopsy-proven NSCLC. Nine segmentation methods were applied to each image, including manual delineation, K-means (KM), watershed, fuzzy-C-mean, region-growing, local active contour (LAC), and iterative thresholding (IT) with 40, 45, and 50% thresholds. Diverse image discretizations, both without a filter and with different wavelet decompositions, were applied to PET images. Overall, 6741 radiomic features were extracted from each image (749 radiomic features from each segmented area). Non-parametric empirical Bayes (NPEB) ComBat harmonization was used to harmonize the features. Linear Support Vector Classifier (LinearSVC) with L1 regularization For feature selection and Support Vector Machine classifier (SVM) with fivefold nested cross-validation was performed using StratifiedKFold with 'n_splits' set to 5 to predict recurrence in NSCLC patients and assess the impact of ComBat harmonization on the outcome. RESULTS: From 749 extracted radiomic features, 206 (27%) and 389 (51%) features showed excellent reliability (ICC ≥ 0.90) against segmentation method variation before and after NPEB ComBat harmonization, respectively. Among all, 39 features demonstrated poor reliability, which declined to 10 after ComBat harmonization. The 64 fixed bin widths (without any filter) and wavelets (LLL)-based radiomic features set achieved the best performance in terms of robustness against diverse segmentation techniques before and after ComBat harmonization. The first-order and GLRLM and also first-order and NGTDM feature families showed the largest number of robust features before and after ComBat harmonization, respectively. In terms of predicting recurrence in NSCLC, our findings indicate that using ComBat harmonization can significantly enhance machine learning outcomes, particularly improving the accuracy of watershed segmentation, which initially had fewer reliable features than manual contouring. Following the application of ComBat harmonization, the majority of cases saw substantial increase in sensitivity and specificity. CONCLUSION: Radiomic features are vulnerable to different segmentation methods. ComBat harmonization might be considered a solution to overcome the poor reliability of radiomic features.

Development of novel low-cost readout electronics for large field-of-view gamma camera detectors.

PURPOSE: Large scintillation crystals-based gamma cameras play a crucial role in nuclear medicine imaging. In this study, a large field-of-view (FOV) gamma detector consisting of 48 square PMTs developed using a new readout electronics, reducing 48 (6 × 8) analog signals to 14 (6 + 8) analog sums of each row and column, with reduced complexity and cost while preserving image quality. METHODS: All 14 analog signals were converted to digital signals using AD9257 high-speed analog to digital (ADC) converters driven by the SPARTAN-6 family of field-programmable gate arrays (FPGA) in order to calculate the signal integrals. The positioning algorithm was based on the digital correlated signal enhancement (CSE) algorithm implemented in the acquisition software. The performance characteristics of the developed gamma camera were measured using the NEMA NU 1-2018 standards. RESULTS: The measured energy resolution of the developed detector was 8.7 % at 140 keV, with an intrinsic spatial resolution of 3.9 mm. The uniformity was within 0.6 %, while the linearity was within 0.1 %. CONCLUSION: The performance evaluation demonstrated that the developed detector has suitable specifications for high-end nuclear medicine imaging.

Deep learning-based partial volume correction in standard and low-dose positron emission tomography-computed tomography imaging.

Background: Positron emission tomography (PET) imaging encounters the obstacle of partial volume effects, arising from its limited intrinsic resolution, giving rise to (I) considerable bias, particularly for structures comparable in size to the point spread function (PSF) of the system; and (II) blurred image edges and blending of textures along the borders. We set out to build a deep learning-based framework for predicting partial volume corrected full-dose (FD + PVC) images from either standard or low-dose (LD) PET images without requiring any anatomical data in order to provide a joint solution for partial volume correction and de-noise LD PET images. Methods: We trained a modified encoder-decoder U-Net network with standard of care or LD PET images as the input and FD + PVC images by six different PVC methods as the target. These six PVC approaches include geometric transfer matrix (GTM), multi-target correction (MTC), region-based voxel-wise correction (RBV), iterative Yang (IY), reblurred Van-Cittert (RVC), and Richardson-Lucy (RL). The proposed models were evaluated using standard criteria, such as peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity index (SSIM), relative bias, and absolute relative bias. Results: Different levels of error were observed for these partial volume correction methods, which were relatively smaller for GTM with a SSIM of 0.63 for LD and 0.29 for FD, IY with an SSIM of 0.63 for LD and 0.67 for FD, RBV with an SSIM of 0.57 for LD and 0.65 for FD, and RVC with an SSIM of 0.89 for LD and 0.94 for FD PVC approaches. However, large quantitative errors were observed for multi-target MTC with an RMSE of 2.71 for LD and 2.45 for FD and RL with an RMSE of 5 for LD and 3.27 for FD PVC approaches. Conclusions: We found that the proposed framework could effectively perform joint de-noising and partial volume correction for PET images with LD and FD input PET data (LD vs. FD). When no magnetic resonance imaging (MRI) images are available, the developed deep learning models could be used for partial volume correction on LD or standard PET-computed tomography (PET-CT) scans as an image quality enhancement technique.

Accurate modeling and performance evaluation of a total-body pet scanner using Monte Carlo simulations.

BACKGROUND: The limited axial field-of-view (FOV) of conventional clinical positron emission tomography (PET) scanners (∼15 to 26 cm) allows detecting only 1% of all coincidence photons, hence limiting significantly their sensitivity. To overcome this limitation, the EXPLORER consortium developed the world's first total-body PET/CT scanner that significantly increased the sensitivity, thus enabling to decrease the scan duration or injected dose. PURPOSE: The purpose of this study is to perform and validate Monte Carlo simulations of the uEXPLORER PET scanner, which can be used to devise novel conceptual designs and geometrical configurations through obtaining features that are difficult to obtain experimentally. METHODS: The total-body uEXPLORER PET scanner was modeled using GATE Monte Carlo (MC) platform. The model was validated through comparison with experimental measurements of various performance parameters, including spatial resolution, sensitivity, count rate performance, and image quality, according to NEMA-NU2 2018 standards. Furthermore, the effects of the time coincidence window and maximum ring difference on the count rate and noise equivalent count rate (NECR) were evaluated. RESULTS: Overall, the validation study showed that there was a good agreement between the simulation and experimental results. The differences between the simulated and experimental total sensitivity for the NEMA and extended phantoms at the center of the FOV were 2.3% and 0.0%, respectively. The difference in peak NECR was 9.9% for the NEMA phantom and 1.0% for the extended phantom. The average bias between the simulated and experimental results of the full-width-at-half maximum (FWHM) for six different positions and three directions was 0.12 mm. The simulations showed that using a variable coincidence time window based on the maximum ring difference can reduce the effect of random coincidences and improve the NECR compared to a constant time coincidence window. The NECR corresponding to 252-ring difference was 2.11 Mcps, which is larger than the NECR corresponding to 336-ring difference (2.04 Mcps). CONCLUSION: The developed MC model of the uEXPLORER PET scanner was validated against experimental measurements and can be used for further assessment and design optimization of the scanner.

Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study.

Purpose.This study aims to predict radiotherapy-induced rectal and bladder toxicity using computed tomography (CT) and magnetic resonance imaging (MRI) radiomics features in combination with clinical and dosimetric features in rectal cancer patients.Methods.A total of sixty-three patients with locally advanced rectal cancer who underwent three-dimensional conformal radiation therapy (3D-CRT) were included in this study. Radiomics features were extracted from the rectum and bladder walls in pretreatment CT and MR-T2W-weighted images. Feature selection was performed using various methods, including Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-square (Chi2), Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and SelectPercentile. Predictive modeling was carried out using machine learning algorithms, such as K-nearest neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Gradient Boosting (XGB), and Linear Discriminant Analysis (LDA). The impact of the Laplacian of Gaussian (LoG) filter was investigated with sigma values ranging from 0.5 to 2. Model performance was evaluated in terms of the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, and specificity.Results.A total of 479 radiomics features were extracted, and 59 features were selected. The pre-MRI T2W model exhibited the highest predictive performance with an AUC: 91.0/96.57%, accuracy: 90.38/96.92%, precision: 90.0/97.14%, sensitivity: 93.33/96.50%, and specificity: 88.09/97.14%. These results were achieved with both original image and LoG filter (sigma = 0.5-1.5) based on LDA/DT-RF classifiers for proctitis and cystitis, respectively. Furthermore, for the CT data, AUC: 90.71/96.0%, accuracy: 90.0/96.92%, precision: 88.14/97.14%, sensitivity: 93.0/96.0%, and specificity: 88.09/97.14% were acquired. The highest values were achieved using XGB/DT-XGB classifiers for proctitis and cystitis with LoG filter (sigma = 2)/LoG filter (sigma = 0.5-2), respectively. MRMR/RFE-Chi2 feature selection methods demonstrated the best performance for proctitis and cystitis in the pre-MRI T2W model. MRMR/MRMR-Lasso yielded the highest model performance for CT.Conclusion.Radiomics features extracted from pretreatment CT and MR images can effectively predict radiation-induced proctitis and cystitis. The study found that LDA, DT, RF, and XGB classifiers, combined with MRMR, RFE, Chi2, and Lasso feature selection algorithms, along with the LoG filter, offer strong predictive performance. With the inclusion of a larger training dataset, these models can be valuable tools for personalized radiotherapy decision-making.

Quantitative assessment of crystal material and size on the performance of rotating dual head small animal PET scanners using Monte Carlo modeling.

In this work, among different proposed designs we have studied dual-head coincidence detectors (DHC) with pixelated crystals in order to optimize the design of detector systems of small animal PET scanners. Monte Carlo simulations and different detector components and materials, under different imaging conditions and geant 4 application for tomographic emission (GATE) were used for all simulations. Crystal length and inter material space on system performance were studied modeling several pixel sizes, ranging from 0.5 x 0.5mm² to 3.0 x 3.0mm² by increment of 0.5mm and using epoxy intermaterial with pitch of 0.1, 0.2 and 0.3mm. Three types of scintillator crystals:bismuth germinate orthosilicate, cerium-doped lutetium orthosilicate and gadolinium orthosilicate were simulated with thicknesses of 10mm and 15 mm. For all measurements a point source with the activity of 1MBq was placed at the center of field of view. The above simulation revealed that by increasing pixel size and crystal length in scintillator material of a pixelated array, sensitivity can be raised from 1% to 7%. However, spatial resolution becomes worse when pixel size increases from 0.6mm to 2.6mm. In addition, photons mispositioned events decrease from 76%to 45%. Crystal length decrease, significantly reduces the percentage of mispositioned events from 89% to 59%. Moreover increase in crystal length from 10mm to 15 mm changes sensitivity from 2% to 6% and spatial resolution from 0.6mm to 3.5mm. In conclusion, it was shown that pixel size 2mm with 10mm crystal thickness can provide the best dimensions in order to optimize system performance. These results confirmed the value of GATE Monte Carlo code, as being a useful tool for optimizing nuclear medicine imaging systems performance, for small animal PET studies.

The influence of resolution recovery by using collimator detector response during 3D OSEM image reconstruction on (99m)Tc-ECD brain SPET images.

Partial volume effect, due to the poor spatial resolution of single photon emission tomography (SPET), significantly restricts the absolute quantification of the regional brain uptake and limits the accuracy of the absolute measurement of blood flow. In this study the importance of compensation for the collimator-detector response (CDR) in the technetium-99m ethyl cysteinate dimer ((99m)Tc-ECD) brain SPET was assessed, by incorporating system response in the ordered-subsets expectation maximization (OSEM) reconstruction algorithm. By placing a point source of (99m)Tc at different distances from the face of the collimator, CDR were found and modeled using Gaussian functions. A fillable slice of the brain phantom was designed and filled by (99m)Tc. Projections acquired from the phantom and also 4 patients who underwent the (99m)Tc-ECD brain SPET were used in this study. To reconstruct the images, 3D OSEM algorithm was used. System blurring functions were modeled, during the reconstruction in both projection and backprojection steps. Our results were compared with the conventional resolution recovery using Metz filter in filtered backprojection (FBP). Visual inspection of the images was performed by six nuclear medicine specialists. Quantitative analysis was also studied by calculating the contrast and the count density of the reconstructed images. For the phantom images, background counts and noise were decreased by 3D OSEM compared to the FBP-Metz method. Quantitatively, the ratio of the counts of the occupied hot region to that of the cold region of the reconstructed by FBP-Metz images was 1.14. This value was decreased from 1.12 to 0.86 for 3D OSEM of 2 and 30 iterations respectively. The reference value was 0.85 for the planar image. For clinical images, hot to cold regions (grey to white matter), the count ratio was increased from 1.44 in FBP-Metz to 3.2 and 4 in 3D OSEM with 10 and 20 iterations respectively. Based on the interpretability of images, the best scores (3.79±0.51) by the physicians were given to the images reconstructed by 3D OSEM and 10 iterations. This value was 0.63±0.77 for FBP-Metz images. In conclusion, by incorporating the distance dependent CDR during 3D OSEM, it was possible to reconstruct the brain images with much higher resolution and contrast as compared to the conventional resolution recovery method, which used FBP-Metz. It was however important to make a trade-off between noise and resolution by determining an optimum iterations number.

Harmonization based on quantitative analysis of standardized uptake value variations across PET/CT scanners: a multicenter phantom study.

OBJECTIVES: This study aimed to measure standardized uptake value (SUV) variations across different PET/computed tomography (CT) scanners to harmonize quantification across systems. METHODS: We acquired images using the National Electrical Manufacturers Association International Electrotechnical Commission phantom from three PET/CT scanners operated using routine imaging protocols at each site. The SUVs of lesions were assessed in the presence of reference values by a digital reference object (DRO) and recommendations by the European Association of Nuclear Medicine (EANM/EARL) to measure inter-site variations. For harmonization, Gaussian filters with tuned full width at half maximum (FWHM) values were applied to images to minimize differences in SUVs between reference and images. Inter-site variation of SUVs was evaluated in both pre- and postharmonization situations. Test-retest analysis was also carried out to evaluate repeatability. RESULTS: SUVs from different scanners became significantly more consistent, and inter-site differences decreased for SUV mean , SUV max and SUV peak from 17.3, 20.7, and 15.5% to 4.8, 4.7, and 2.7%, respectively, by harmonization ( P values <0.05 for all). The values for contrast-to-noise ratio in the smallest lesion of the phantom verified preservation of image quality following harmonization (>2.8%). CONCLUSIONS: Harmonization significantly lowered variations in SUV measurements across different PET/CT scanners, improving reproducibility while preserving image quality.

Synergistic impact of motion and acquisition/reconstruction parameters on 18 F-FDG PET radiomic features in non-small cell lung cancer: Phantom and clinical studies.

OBJECTIVES: This study is aimed at examining the synergistic impact of motion and acquisition/reconstruction parameters on 18 F-FDG PET image radiomic features in non-small cell lung cancer (NSCLC) patients, and investigating the robustness of features performance in differentiating NSCLC histopathology subtypes. METHODS: An in-house developed thoracic phantom incorporating lesions with different sizes was used with different reconstruction settings, including various reconstruction algorithms, number of subsets and iterations, full-width at half-maximum of post-reconstruction smoothing filter and acquisition parameters, including injected activity and test-retest with and without motion simulation. To simulate motion, a special motor was manufactured to simulate respiratory motion based on a normal patient in two directions. The lesions were delineated semi-automatically to extract 174 radiomic features. All radiomic features were categorized according to the coefficient of variation (COV) to select robust features. A cohort consisting of 40 NSCLC patients with adenocarcinoma (n = 20) and squamous cell carcinoma (n = 20) was retrospectively analyzed. Statistical analysis was performed to discriminate robust features in differentiating histopathology subtypes of NSCLC lesions. RESULTS: Overall, 29% of radiomic features showed a COV ≤5% against motion. Forty-five percent and 76% of the features showed a COV ≤ 5% against the test-retest with and without motion in large lesions, respectively. Thirty-three percent and 45% of the features showed a COV ≤ 5% against different reconstruction parameters with and without motion, respectively. For NSCLC histopathological subtype differentiation, statistical analysis showed that 31 features were significant (p-value < 0.05). Two out of the 31 significant features, namely, the joint entropy of GLCM (AUC = 0.71, COV = 0.019) and median absolute deviation of intensity histogram (AUC = 0.7, COV = 0.046), were robust against the motion (same reconstruction setting). CONCLUSIONS: Motion, acquisition, and reconstruction parameters significantly impact radiomic features, just as their synergies. Radiomic features with high predictive performance (statistically significant) in differentiating histopathological subtype of NSCLC may be eliminated due to non-reproducibility.