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Rationale and Objectives: Misregistration artifacts between the PET and attenuation correction CT (CTAC) exams can degrade image quality and cause diagnostic errors. Deep learning (DL)-warped elastic registration methods have been proposed to improve misregistration errors., Materials and Methods: 30 patients undergoing routine oncologic examination (20 18 F-FDG PET/CT and 10 64 Cu-DOTATATE PET/CT) were retrospectively identified and compared using unmodified CTAC, and a DL-augmented spatial transformation CT attenuation map. Primary endpoints included differences in subjective image quality and standardized uptake values (SUV). Exams were randomized to reduce reader bias, and three radiologists rated image quality across six anatomic sites using a modified Likert scale. Measures of local bias and lesion SUV were also quantitatively evaluated., Results: The DL attenuation correction methods were associated with higher image quality and reduced misregistration artifacts (Mean 18 F-FDG quality rating=3.5-3.8 for DL vs 3.2-3.5 for standard reconstruction (STD); Mean 64 Cu-DOTATATE quality rating= 3.2-3.4 for DL vs 2.1-3.3; P < 0.05 for STD, for all except 64 Cu-DOTATATE inferior spleen). Percent change in superior liver SUV mean for 18 F-FDG and 64 Cu-DOTATATE were 5.3 ± 4.9 and 8.2 ± 4.1%, respectively. Measures of signal-to-noise ratio were significantly improved for the DL over STD (Hepatopulmonary index (HPI) [ 18 F-FDG] = 4.5 ± 1.2 vs 4.0 ± 1.1, P < 0.001; HPI [ 64 Cu-DOTATATE] = 16.4 ± 16.9 vs 12.5 ± 5.5, P = 0.039)., Conclusion: Deep learning elastic registration for CT attenuation correction maps on routine oncology PET/CT decreases misregistration artifacts, with a greater impact on PET scans with longer acquisition times., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Joshua Schaefferkoetter reports a relationship with Siemens Medical Solutions USA Inc that includes: employment. James Hamill reports a relationship with Siemens Medical Solutions USA Inc that includes: employment. Jim O Doherty reports a relationship with Siemens Medical Solutions USA Inc that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Purpose: Clinical whole-body (WB) PET images can be compensated for respiratory motion using data-driven gating (DDG). However, PET DDG images may still exhibit motion artefacts at the diaphragm if the CT is acquired in a different respiratory phase than the PET image. This study evaluates the combined use of PET DDG and a deep-learning model (AIR-PETCT) for elastic registration of CT (WarpCT) to the non attenuation- and non scatter-corrected PET image (PET NAC), enabling improved PET reconstruction., Methods: The validation cohort included 20 patients referred for clinical FDG PET/CT, undergoing two CT scans: a free respiration CT free and an end-expiration breath-hold CT ex . AIR-PETCT registered each CT to the PET NAC and PET DDG NAC images. The image quality of PET and PET DDG images reconstructed using CTs and WarpCTs was evaluated by three blinded readers. Additionally, a clinical impact cohort of 20 patients with significant "banana" artefacts from FDG, PSMA, and DOTATOC scans was assessed for image quality and tumor-to-background ratios., Results: AIR-PETCT was robust and generated consistent WarpCTs when registering different CTs to the same PET NAC. The use of WarpCT instead of CT consistently led to equivalent or improved PET image quality. The algorithm significantly reduced "banana" artefacts and improved lesion-to-background ratios around the diaphragm. The blinded clinicians clearly preferred PET DDG images reconstructed using WarpCT., Conclusion: AIR-PETCT effectively reduces respiratory motion artefacts from PET images, while improving lesion contrast. The combination of PET DDG and WarpCT holds promise for clinical application, improving PET image evaluation and diagnostic confidence., Competing Interests: Declarations. Ethical approval: Approval for the study was obtained from the ethics committee at Region Midtjylland (1-10-72-194-19). Consent to participate: Informed consent was obtained from all participants before each PET scan. Consent to publish: Publication consent was obtained from all study participants., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: For PET/CT, the CT transmission data are used to correct the PET emission data for attenuation. However, subject motion between the consecutive scans can cause problems for the PET reconstruction. A method to match the CT to the PET would reduce resulting artifacts in the reconstructed images., Purpose: This work presents a deep learning technique for inter-modality, elastic registration of PET/CT images for improving PET attenuation correction (AC). The feasibility of the technique is demonstrated for two applications: general whole-body (WB) imaging and cardiac myocardial perfusion imaging (MPI), with a specific focus on respiratory and gross voluntary motion., Materials and Methods: A convolutional neural network (CNN) was developed and trained for the registration task, comprising two distinct modules: a feature extractor and a displacement vector field (DVF) regressor. It took as input a non-attenuation-corrected PET/CT image pair and returned the relative DVF between them-it was trained in a supervised fashion using simulated inter-image motion. The 3D motion fields produced by the network were used to resample the CT image volumes, elastically warping them to spatially match the corresponding PET distributions. Performance of the algorithm was evaluated in different independent sets of WB clinical subject data: for recovering deliberate misregistrations imposed in motion-free PET/CT pairs and for improving reconstruction artifacts in cases with actual subject motion. The efficacy of this technique is also demonstrated for improving PET AC in cardiac MPI applications., Results: A single registration network was found to be capable of handling a variety of PET tracers. It demonstrated state-of-the-art performance in the PET/CT registration task and was able to significantly reduce the effects of simulated motion imposed in motion-free, clinical data. Registering the CT to the PET distribution was also found to reduce various types of AC artifacts in the reconstructed PET images of subjects with actual motion. In particular, liver uniformity was improved in the subjects with significant observable respiratory motion. For MPI, the proposed approach yielded advantages for correcting artifacts in myocardial activity quantification and potentially for reducing the rate of the associated diagnostic errors., Conclusion: This study demonstrated the feasibility of using deep learning for registering the anatomical image to improve AC in clinical PET/CT reconstruction. Most notably, this improved common respiratory artifacts occurring near the lung/liver border, misalignment artifacts due to gross voluntary motion, and quantification errors in cardiac PET imaging., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Introduction: The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomography (PET), as inputs into machine learning (ML) to predict the Gleason scores (GS) of detected lesions for improved PCa lesion classification., Methods: 20 biopsy-confirmed PCa subjects underwent imaging before radical prostatectomy. A pathologist assigned GS from tumour tissue. Two radiologists and one nuclear medicine physician delineated the lesions on the mpMR and PET images, yielding 45 lesion inputs. Seven quantitative parameters were extracted from the lesions, namely T2-weighted (T2w) image intensity, apparent diffusion coefficient (ADC), transfer constant (K TRANS ), efflux rate constant (K ep ), and extracellular volume ratio (V e ) from mpMR images, and SUV mean and SUV max from PET images. Eight radiomic features were selected out of 109 radiomic features from T2w, ADC and PET images. Quantitative parameters or radiomic features, with risk factors of age, prostate-specific antigen (PSA), PSA density and volume, of 45 different lesion inputs were input in different combinations into four ML models - Decision Tree (DT), Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Ensembles model (EM)., Results: SUV max yielded the highest accuracy in discriminating detected lesions. Among the 4 ML models, kNN yielded the highest accuracies of 0.929 using either quantitative parameters or radiomic features with risk factors as input., Conclusions: ML models' performance is dependent on the input combinations and risk factors further improve ML classification accuracy., Competing Interests: Declaration of Competing Interest Josh Schaefferkoetter, Maurizio Conti and Lars Eriksson have full-time employment with Siemens Medical Solutions USA, Inc. The other authors declare that they have no competing interests., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Cardiac and respiratory motions in clinical positron emission tomography (PET) are a major contributor to inaccurate PET quantification and lesion characterisation. In this study, an elastic motion-correction (eMOCO) technique based on mass preservation optical flow is adapted and investigated for positron emission tomography-magnetic resonance imaging (PET-MRI) applications., Methods: The eMOCO technique was investigated in a motion management QA phantom and in twenty-four patients who underwent PET-MRI for dedicated liver imaging and nine patients for cardiac PET-MRI evaluation. Acquired data were reconstructed with eMOCO and gated motion correction techniques at cardiac, respiratory and dual gating modes, and compared to static images. Standardized uptake value (SUV), signal-to-noise ratio (SNR) of lesion activities from each gating mode and correction technique were measured and their means/standard deviation (SD) were compared using 2-ways ANOVA analysis and post-hoc Tukey's test., Results: Lesions' SNR are highly recovered from phantom and patient studies. The SD of the SUV resulted from the eMOCO technique was statistically significantly less (P<0.01) than the SD resulted from conventional gated and static SUVs at the liver, lung and heart., Conclusions: The eMOCO technique was successfully implemented in PET-MRI in a clinical setting and produced the lowest SD compared to gated and static images, and hence provided the least noisy PET images. Therefore, the eMOCO technique can potentially be used on PET-MRI for improved respiratory and cardiac motion correction., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-1017/coif). JS, IH and JJ are full-time employees of Siemens Healthcare. UM reports that he received consulting fee from POINT Biopharma Inc. (2020–2022). KH reports that he received honoraria from Sanofi-genzyme. PVH reports that he received grants, support for attending meetings and WIP software from Siemens Healthineers; and he also received speaker fees from Spring Nature, JCA Seminars, and Ontario association of radiologists. The other authors have no conflicts of interest to declare., (2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Background: Artificial intelligence (AI) algorithms based on deep convolutional networks have demonstrated remarkable success for image transformation tasks. State-of-the-art results have been achieved by generative adversarial networks (GANs) and training approaches which do not require paired data. Recently, these techniques have been applied in the medical field for cross-domain image translation., Purpose: This study investigated deep learning transformation in medical imaging. It was motivated to identify generalizable methods which would satisfy the simultaneous requirements of quality and anatomical accuracy across the entire human body. Specifically, whole-body MR patient data acquired on a PET/MR system were used to generate synthetic CT image volumes. The capacity of these synthetic CT data for use in PET attenuation correction (AC) was evaluated and compared to current MR-based attenuation correction (MR-AC) methods, which typically use multiphase Dixon sequences to segment various tissue types., Materials and Methods: This work aimed to investigate the technical performance of a GAN system for general MR-to-CT volumetric transformation and to evaluate the performance of the generated images for PET AC. A dataset comprising matched, same-day PET/MR and PET/CT patient scans was used for validation., Results: A combination of training techniques was used to produce synthetic images which were of high-quality and anatomically accurate. Higher correlation was found between the values of mu maps calculated directly from CT data and those derived from the synthetic CT images than those from the default segmented Dixon approach. Over the entire body, the total amounts of reconstructed PET activities were similar between the two MR-AC methods, but the synthetic CT method yielded higher accuracy for quantifying the tracer uptake in specific regions., Conclusion: The findings reported here demonstrate the feasibility of this technique and its potential to improve certain aspects of attenuation correction for PET/MR systems. Moreover, this work may have larger implications for establishing generalized methods for inter-modality, whole-body transformation in medical imaging. Unsupervised deep learning techniques can produce high-quality synthetic images, but additional constraints may be needed to maintain medical integrity in the generated data., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The aim of this study was to determine whether quantitative PET parameters on baseline 68 Ga-DOTATATE PET/CT and interim PET (iPET) performed before the second cycle of therapy are predictive of the therapy response and progression-free survival (PFS). Methods: Ninety-one patients with well-differentiated neuroendocrine tumors (mean Ki-67 index, 8.3%) underwent 68 Ga-DOTATATE PET/CT to determine suitability for peptide receptor radionuclide therapy as part of a prospective multicenter study. The mean follow-up was 12.2 mo. Of the 91 patients, 36 had iPET. The tumor metrics evaluated were marker lesion-based measures (mean SUV max and ratio of the mean lesion SUV max to the SUV max in the liver or the SUV max in the spleen), segmented 68 Ga-DOTATATE tumor volumes (DTTVs), SUV max and SUV mean obtained with the liver and spleen as thresholds, and heterogeneity parameters (coefficient of variation, kurtosis, and skewness). The Wilcoxon rank sum test was used for the association between continuous variables and the therapy response, as determined by the clinical response. Univariable and multivariable Cox proportional hazards models were used for the association with PFS. Results: There were 71 responders and 20 nonresponders. When marker lesions were used, higher mean SUV max and ratio of the mean lesion SUV max to the SUV max in the liver were predictors of the therapy response ( P  = 0.018 and 0.024, respectively). For DTTV parameters, higher SUV max and SUV mean obtained with the liver as a threshold and lower kurtosis were predictors of a favorable response ( P  = 0.025, 0.0055, and 0.031, respectively). The latter also correlated with a longer PFS. The iPET DTTV SUV mean obtained with the liver as a threshold and the ratio of mean SUV max obtained from target lesions at iPET to baseline PET correlated with the therapy response ( P  = 0.024 and 0.048, respectively) but not PFS. From the multivariable analysis with adjustment for age, primary site, and Ki-67 index, the mean SUV max ( P  = 0.019), ratio of the mean lesion SUV max to the SUV max in the liver ( P  = 0.018), ratio of the mean lesion SUV max to the SUV max in the spleen ( P  = 0.041), DTTV SUV mean obtained with the liver ( P  = 0.0052), and skewness ( P  = 0.048) remained significant predictors of PFS. Conclusion: The degree of somatostatin receptor expression and tumor heterogeneity, as represented by several metrics in our analysis, were predictive of the therapy response or PFS. Changes in these parameters after the first cycle of peptide receptor radionuclide therapy did not correlate with clinical outcomes., (© 2021 by the Society of Nuclear Medicine and Molecular Imaging.)

Purpose: To conduct a simplified lesion-detection task of a low-dose (LD) PET-CT protocol for frequent lung screening using 30% of the effective PETCT dose and to investigate the feasibility of increasing clinical value of low-statistics scans using machine learning., Methods: We acquired 33 SD PET images, of which 13 had actual LD (ALD) PET, and simulated LD (SLD) PET images at seven different count levels from the SD PET scans. We employed image quality transfer (IQT), a machine learning algorithm that performs patch-regression to map parameters from low-quality to high-quality images. At each count level, patches extracted from 23 pairs of SD/SLD PET images were used to train three IQT models - global linear, single tree, and random forest regressions with cubic patch sizes of 3 and 5 voxels. The models were then used to estimate SD images from LD images at each count level for 10 unseen subjects. Lesion-detection task was carried out on matched lesion-present and lesion-absent images., Results: LD PET-CT protocol yielded lesion detectability with sensitivity of 0.98 and specificity of 1. Random forest algorithm with cubic patch size of 5 allowed further 11.7% reduction in the effective PETCT dose without compromising lesion detectability, but underestimated SUV by 30%., Conclusion: LD PET-CT protocol was validated for lesion detection using ALD PET scans. Substantial image quality improvement or additional dose reduction while preserving clinical values can be achieved using machine learning methods though SUV quantification may be biased and adjustment of our research protocol is required for clinical use., (Copyright © 2020 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Our purpose was to determine the effect of a smoothing filter and partial-volume correction (PVC) on measured prostate-specific membrane antigen (PSMA) activity in small metastatic lesions and to determine the impact of these changes on molecular imaging PSMA (miPSMA) scoring. Methods: Men who had biochemical recurrence of prostate cancer with negative findings on CT and bone scintigraphy were referred for 18 F-DCFPyL (2-(3-(1-carboxy-5-[(6- 18 F-fluoro-pyridine-3-carbonyl)-amino]-pentyl) PET/CT. Examinations were performed on 1 of 2 different brands of PET/CT scanner. All suspected tumor sites were manually contoured on coregistered CT and PET images, and each was assigned an miPSMA score as per the PROMISE criteria. The PVC factors were calculated for every lesion using the anatomic CT and then applied to the unsmoothed PET images. The miPSMA scores, with and without the corrections, were compared, and a simplified rule-of-thumb (RoT) correction factor (CF) was derived for lesions at various sizes (<4 mm, 4-7 mm, 7-9 mm, and 9-12 mm). This CF was then applied to the original dataset and the miPSMA scores that were obtained using the RoT CF were compared with those obtained using the actual corrections. Results: There were 75 men (median age, 69 y; median serum PSA, 3.69 μg/L) with 232 metastatic nodes less than 12 mm in diameter (mean lesion volume, 313.5 ± 309.6 mm 3 ). The mean SUV max before and after correction was 11.0 ± 9.3 and 28.5 ± 22.8, respectively ( P < 0.00001). The mean CF for lesions smaller than 4 mm ( n = 22), 4-7 mm ( n = 140), 7-9 mm ( n = 50), and 9-12 mm ( n = 20) was 4 (range, 2.5-6.4), 2.8 (range, 1.6-4.9), 2.3 (range, 1.6-3.3), and 1.8 (range, 1.4-2.4), respectively. Overall, the miPSMA scores were concordant between the corrected dataset and the RoT dataset for 205 of 232 lesions (88.4%). Conclusion: A smoothing filter and PVC had a significant effect on measured PSMA activity in small nodal metastases, impacting the miPSMA score., (© 2020 by the Society of Nuclear Medicine and Molecular Imaging.)

Prostate segmentation in multiparametric magnetic resonance imaging (mpMRI) can help to support prostate cancer diagnosis and therapy treatment. However, manual segmentation of the prostate is subjective and time-consuming. Many deep learning monomodal networks have been developed for automatic whole prostate segmentation from T2-weighted MR images. We aimed to investigate the added value of multimodal networks in segmenting the prostate into the peripheral zone (PZ) and central gland (CG). We optimized and evaluated monomodal DenseVNet, multimodal ScaleNet, and monomodal and multimodal HighRes3DNet, which yielded dice score coefficients (DSC) of 0.875, 0.848, 0.858, and 0.890 in WG, respectively. Multimodal HighRes3DNet and ScaleNet yielded higher DSC with statistical differences in PZ and CG only compared to monomodal DenseVNet, indicating that multimodal networks added value by generating better segmentation between PZ and CG regions but did not improve the WG segmentation. No significant difference was observed in the apex and base of WG segmentation between monomodal and multimodal networks, indicating that the segmentations at the apex and base were more affected by the general network architecture. The number of training data was also varied for DenseVNet and HighRes3DNet, from 20 to 120 in steps of 20. DenseVNet was able to yield DSC of higher than 0.65 even for special cases, such as TURP or abnormal prostate, whereas HighRes3DNet's performance fluctuated with no trend despite being the best network overall. Multimodal networks did not add value in segmenting special cases but generally reduced variations in segmentation compared to the same matched monomodal network., Competing Interests: The authors have no conflict of interest., (Copyright © 2020 Ying-Hwey Nai et al.)

Goal: PET is a relatively noisy process compared to other imaging modalities, and sparsity of acquisition data leads to noise in the images. Recent work has focused on machine learning techniques to improve PET images, and this study investigates a deep learning approach to improve the quality of reconstructed image volumes through denoising by a 3D convolution neural network. Potential improvements were evaluated within a clinical context by physician performance in a reading task., Methods: A wide range of controlled noise levels was emulated from a set of chest PET data in patients with lung cancer, and a convolutional neural network was trained to denoise the reconstructed images using the full-count reconstructions as the ground truth. The benefits, over conventional Gaussian smoothing, were quantified across all noise levels by observer performance in an image ranking and lesion detection task., Results: The CNN-denoised images were generally ranked by the physicians equal to or better than the Gaussian-smoothed images for all count levels, with the largest effects observed in the lowest-count image sets. For the CNN-denoised images, overall lesion contrast recovery was 60% and 90% at the 1 and 20 million count levels, respectively. Notwithstanding the reduced lesion contrast recovery in noisy data, the CNN-denoised images also yielded better lesion detectability in low count levels. For example, at 1 million true counts, the average true positive detection rate was around 40% for the CNN-denoised images and 30% for the smoothed images., Conclusion: Significant improvements were found for CNN-denoising for very noisy images, and to some degree for all noise levels. The technique presented here offered however limited benefit for detection performance for images at the count levels routinely encountered in the clinic.

It is important to measure the respiratory cycle in positron emission tomography (PET) to enhance the contrast of the tumor as well as the accuracy of its localization in organs such as the lung and liver. Several types of data-driven respiratory gating methods, such as center of mass and principal component analysis, have been developed to directly measure the breathing cycle from PET images and listmode data. However, the breathing cycle is still hard to detect in low signal-to-noise ratio (SNR) data, particularly in low dose PET/CT scans. To address this issue, a time-of-flight (TOF) PET is currently utilized for the data-driven respiratory gating because of its higher SNR and better localization of the region of interest. To further improve the accuracy of respiratory gating with TOF information, we propose an accurate data-driven respiratory gating method, which retrospectively derives the respiratory signal using a localized sensing method based on a diaphragm mask in TOF PET data. To assess the accuracy of the proposed method, the performance is evaluated with three patient datasets, and a pressure-belt signal as the ground truth is compared. In our experiments, we validate that the respiratory signal using the proposed data-driven gating method is well matched to the pressure-belt respiratory signal with less than 5% peak time errors and over 80% trace correlations. Based on gated signals, the respiratory-gated image of the proposed method provides more clear edges of organs compared to images using conventional non-TOF methods. Therefore, we demonstrate that the proposed method can achieve improvements for the accuracy of gating signals and image quality.

Purpose: The fundamental nature of positron emission tomography (PET), as an event detection system, provides some flexibility for data handling, including retrospective data manipulation. The reorganization of acquisition data allows the emulation of new scans arising from identical radiotracer spatial distributions, but with different statistical compositions, and is especially useful for evaluating the stability and reproducibility of reconstruction algorithms or when investigating extremely low count conditions. This approach is ubiquitous in the research literature but has only been validated, from the point of view of the noise properties, with numerical simulations and phantom data. We present here the first experiment comparing PET images of the same human subjects generated with two separate injections of radiotracer, using actual low dose (LD) data to validate a randomly decimated emulation from a standard dose scan. A key point of the work is focused on the randoms fractions, which scale differently than the trues at varying activity levels., Methods: Eleven patients with non-small cell lung cancer were enrolled in the study. Each imaging session consisted of two independent FDG-PET/CT scans: a LD scan followed by a standard dose (SD) scan. Images were first reconstructed, using filtered back-projection (FBP) and OSEM incorporating time-of-flight information and point-spread function modeling (PSFTOF), from the LD and SD datasets comprising all counts from each scanned bed position. The number of true counts was recorded for all LD scans, and independent, count-matched emulations (ELD) were reconstructed from the SD data. Noise distribution within the liver and standardized uptake value reproducibility within a population of contoured, tracer-avid lesion volumes were evaluated across scans and statistics., Results: The randoms fraction estimates were 17.4 ± 1.6% (14.9-19.4) in the LD data and 42 ± 2.3% (37.1-45.5) in the SD data. Eleven lesions were identified and volumes of interest were generated with a 50% threshold isocontour for each lesion, in every image. The distributions of metabolic volumes, means and maxima defined by the contoured volumes-of-interest (VOIs) were similar between the LD and SD sets. A two-tailed, matched t-test was performed on the populations of region statistics for both LD and ELD reconstructions, and the t-statistics were 1.1 (P = 0.267) and -0.22 (P = 0.828) for the background liver VOIs and -0.54 (P = 0.603) and 0.23 (P = 0.821) for the lesion VOIs, for FBP and PSFTOF respectively. In every test, the null hypothesis that the two populations had the same mean could not be rejected at the 5% significance level., Conclusions: Our results demonstrate that clinical LD PET scans can indeed be accurately emulated by the statistical decimation of standard dose scans, and this was achieved through validation by images generated with unbiased random coincidence estimations., (© 2019 American Association of Physicists in Medicine.)

Purpose: A study was designed to investigate the impact of time-of-flight (TOF) and point spread function (PSF) modeling on the detectability of myocardial defects., Methods: Clinical FDG-PET data were used to generate populations of defect-present and defect-absent images. Defects were incorporated at three contrast levels, and images were reconstructed by ordered subset expectation maximization (OSEM) iterative methods including ordinary Poisson, alone and with PSF, TOF, and PSF+TOF. Channelized Hotelling observer signal-to-noise ratio (SNR) was the surrogate for human observer performance., Results: For three iterations, 12 subsets, and no postreconstruction smoothing, TOF improved overall defect detection SNR by 8.6% as compared to its non-TOF counterpart for all the defect contrasts. Due to the slow convergence of PSF reconstruction, PSF yielded 4.4% less SNR than non-PSF. For reconstruction parameters (iteration number and postreconstruction smoothing kernel size) optimizing observer SNR, PSF showed larger improvement for faint defects. The combination of TOF and PSF improved mean detection SNR as compared to non-TOF and non-PSF counterparts by 3.0% and 3.2%, respectively., Conclusions: For typical reconstruction protocol used in clinical practice, i.e., less than five iterations, TOF improved defect detectability. In contrast, PSF generally yielded less detectability. For large number of iterations, TOF+PSF yields the best observer performance.

It is expected that both noise and activity distribution can have impact on the detectability of a myocardial defect in a cardiac PET study. In this work, we performed phantom studies to investigate the detectability of a defect in the myocardium for different noise levels and activity distributions. We evaluated the performance of three reconstruction schemes: Filtered Back-Projection (FBP), Ordinary Poisson Ordered Subset Expectation Maximization (OP-OSEM), and Point Spread Function corrected OSEM (PSF-OSEM). We used the Channelized Hotelling Observer (CHO) for the task of myocardial defect detection. We found that the detectability of a myocardial defect is almost entirely dependent on the noise level and the contrast between the defect and its surroundings.

Time-of-flight (TOF) and point spread function (PSF) modeling have been shown to improve PET reconstructions, but the impact on physicians in the clinical setting has not been thoroughly investigated. A lesion detection and localization study was performed using simulated lesions in real patient images. Four reconstruction schemes were considered: ordinary Poisson OSEM (OP) alone and combined with TOF, PSF, and TOF + PSF. The images were presented to physicians experienced in reading PET images, and the performance of each was quantified using localization receiver operating characteristic. Numerical observers (non-prewhitening and Hotelling) were used to identify optimal reconstruction parameters, and observer SNR was compared to the performance of the physicians. The numerical models showed good agreement with human performance, and best performance was achieved by both when using TOF + PSF. These findings suggest a large potential benefit of TOF + PSF for oncology PET studies, especially in the detection of small, low-intensity, focal disease in larger patients.

Purpose: To determine whether the time course of 18-fluorine fluorodeoxyglucose (18F-FDG) activity in multiple consecutively obtained 18F-FDG positron emission tomography (PET)/computed tomography (CT) scans predictably identifies metastatic cervical adenopathy in patients with oral/head and neck cancer. It is hypothesized that the activity will increase significantly over time only in those lymph nodes harboring metastatic cancer., Patients and Methods: A prospective cohort study was performed whereby patients with oral/head and neck cancer underwent consecutive imaging at 9 time points with PET/CT from 60 to 115 minutes after injection with (18)F-FDG. The primary predictor variable was the status of the lymph nodes based on dynamic PET/CT imaging. Metastatic lymph nodes were defined as those that showed an increase greater than or equal to 10% over the baseline standard uptake values. The primary outcome variable was the pathologic status of the lymph node., Results: A total of 2,237 lymph nodes were evaluated histopathologically in the 83 neck dissections that were performed in 74 patients. A total of 119 lymph nodes were noted to have hypermetabolic activity on the 90-minute (static) portion of the study and were able to be assessed by time points. When we compared the PET/CT time point (dynamic) data with the histopathologic analysis of the lymph nodes, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 60.3%, 70.5%, 66.0%, 65.2%, and 65.5%, respectively., Conclusions: The use of dynamic PET/CT imaging does not permit the ablative surgeon to depend only on the results of the PET/CT study to determine which patients will benefit from neck dissection. As such, we maintain that surgeons should continue to rely on clinical judgment and maintain a low threshold for executing neck dissection in patients with oral/head and neck cancer, including those patients with N0 neck designations., (Copyright © 2013 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.)