Positron Emission Tomography Information Research Articles

Parametric response mapping of co-registered intravoxel incoherent motion magnetic resonance imaging and positron emission tomography in locally advanced cervical cancer undergoing concurrent chemoradiation therapy

Background and PurposeIntravoxel-incoherent-motion (IVIM) magnetic-resonance-imaging (MRI) and positron-emission-tomography (PET) have been investigated independently but not voxel-wise to evaluate tumor microenvironment in cervical carcinoma patients. Whether regionally combined information of IVIM and PET offers additional predictive benefit over each modality independently has not been explored. Here, we investigated parametric-response-mapping (PRM) of co-registered PET and IVIM in cervical cancer patients to identify sub-volumes that may predict tumor shrinkage to concurrent-chemoradiation-therapy (CCRT). Materials and MethodsTwenty cervical cancer patients (age: 63[41–85]) were retrospectively evaluated. Diffusion-weighted-images (DWIs) were acquired on 3.0 T MRIs using a free-breathing single-shot-spin echo-planar-imaging (EPI) sequence. Pre- and on-treatment (∼after four-weeks of CCRT) MRI and pre-treatment FDG-PET/CT were acquired. IVIM model-fitting on the DWIs was performed using a Bayesian-fitting simplified two-compartment model. Three-dimensional rigidly-registered maps of PET/CT standardized-uptake-value (SUV) and IVIM diffusion-coefficient (D) and perfusion-fraction (f) were generated. Population-means of PET-SUV, IVIM-D and IVIM-f from pre-treatment-scans were calculated and used to generate PRM via a voxel-wise joint-histogram-analysis to classify voxels as high/low metabolic-activity and with high/low (hi/lo) cellular-density. Similar PRM maps were generated for SUV and f. ResultsTumor-volume (p < 0.001) significantly decreased, while IVIM-f (p = 0.002) and IVIM-D (p = 0.03) significantly increased on-treatment. Pre-treatment tumor-volume (r = -0.45,p = 0.04) and PRM-SUVhiDlo (r = -0.65,p = 0.002) negatively correlated with ΔGTV, while pre-treatment IVIM-D (r = 0.64,p = 0.002), PRM-SUVlofhi (r = 0.52,p = 0.02), and PRM-SUVloDhi (r = 0.74,p < 0.001) positively correlated with ΔGTV. ConclusionIVIM and PET was performed on cervical cancer patients undergoing CCRT and we observed that both IVIM-f and IVIM-D increased during treatment. Additionally, PRM was applied, and sub-volumes were identified that were related to ΔGTV.

A novel method for PET connectomics guided by fibre-tracking MRI: Application to Alzheimer's disease.

This study introduces a novel brain connectome matrix, track-weighted PET connectivity(twPC) matrix, which combines positron emission tomography (PET) and diffusion magnetic resonance imaging data to compute a PET-weighted connectome at the individual subject level. The new method is applied to characterise connectivity changes in the Alzheimer's disease (AD) continuum. The proposed twPC samples PET tracer uptake guided by the underlying white matter fibre-tracking streamline point-to-point connectivity calculated from diffusion MRI (dMRI). Using tau-PET, dMRI and T1-weighted MRI from the Alzheimer's Disease Neuroimaging Initiative database, structural connectivity (SC) and twPC matrices were computed and analysed using the network-based statistic (NBS) technique to examine topological alterations in early mild cognitive impairment (MCI), late MCI and AD participants. Correlation analysis was also performed to explore the coupling between SC and twPC. The NBS analysis revealed progressive topological alterations in both SC and twPC as cognitive decline progressed along the continuum. Compared to healthy controls, networks with decreased SC were identified in late MCI and AD, and networks with increased twPC were identified in early MCI, late MCI and AD. The altered network topologies were mostly different between twPC and SC, although with several common edges largely involving the bilateral hippocampus, fusiform gyrus and entorhinal cortex. Negative correlations were observed between twPC and SC across all subject groups, although displaying an overall reduction in the strength of anti-correlation with disease progression. twPC provides a new means for analysing subject-specific PET and MRI-derived information within a hybrid connectome using established network analysis methods, providing valuable insights into the relationship between structural connections and molecular distributions. PRACTITIONER POINTS: New method is proposed to compute patient-specific PET connectome guided by MRI fibre-tracking. Track-weighted PET connectivity (twPC) matrix allows to leverage PET and structural connectivity information. twPC was applied to dementia, to characterise the PET nework abnormalities in Alzheimer's disease and mild cognitive impairment.

Enhancing predictability of IDH mutation status in glioma patients at initial diagnosis: a comparative analysis of radiomics from MRI, [18F]FET PET, and TSPO PET

PurposeAccording to the World Health Organization classification for tumors of the central nervous system, mutation status of the isocitrate dehydrogenase (IDH) genes has become a major diagnostic discriminator for gliomas. Therefore, imaging-based prediction of IDH mutation status is of high interest for individual patient management. We compared and evaluated the diagnostic value of radiomics derived from dual positron emission tomography (PET) and magnetic resonance imaging (MRI) data to predict the IDH mutation status non-invasively.MethodsEighty-seven glioma patients at initial diagnosis who underwent PET targeting the translocator protein (TSPO) using [18F]GE-180, dynamic amino acid PET using [18F]FET, and T1-/T2-weighted MRI scans were examined. In addition to calculating tumor-to-background ratio (TBR) images for all modalities, parametric images quantifying dynamic [18F]FET PET information were generated. Radiomic features were extracted from TBR and parametric images. The area under the receiver operating characteristic curve (AUC) was employed to assess the performance of logistic regression (LR) classifiers. To report robust estimates, nested cross-validation with five folds and 50 repeats was applied.ResultsTBRGE-180 features extracted from TSPO-positive volumes had the highest predictive power among TBR images (AUC 0.88, with age as co-factor 0.94). Dynamic [18F]FET PET reached a similarly high performance (0.94, with age 0.96). The highest LR coefficients in multimodal analyses included TBRGE-180 features, parameters from kinetic and early static [18F]FET PET images, age, and the features from TBRT2 images such as the kurtosis (0.97).ConclusionThe findings suggest that incorporating TBRGE-180 features along with kinetic information from dynamic [18F]FET PET, kurtosis from TBRT2, and age can yield very high predictability of IDH mutation status, thus potentially improving early patient management.

A Multivariate Method for Estimating and comparing whole brain functional connectomes from fMRI and PET data.

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are two commonly used imaging techniques to visualize brain function. The use of inter-network covariation (a functional connectome) is a widely used approach to infer links among different brain networks. While whole brain resting fMRI connectomes are widely used, PET data has mostly been analyzed using a few regions of interest. There has been much less work estimating PET spatial networks and almost no work on their connectivity (covariation) in the context of a whole brain data-driven connectome, nor have there been direct comparisons between whole brain PET and fMRI connectomes. Here we present an approach to leverage spatially constrained ICA to compute an estimate of the PET connectome. Results reveal highly modularized connectome patterns that are complementary to that identified from resting fMRI. Similarly, we were able to identify comparable resting networks from a PiB PET scan that can be directly compared to networks in rest fMRI data and results reveal similar, but not identical, network spatial patterns, with the PET networks being slightly smoother and, in some cases, showing variations in subnodes. The resulting networks, decomposed into spatial maps and subject expressions (loading parameters) linked to resting fMRI provide a new way to evaluate the complementary information in PET and fMRI and open up new possibilities for biomarker development.Clinical Relevance-This study analyzes the whole-brain PET and fMRI connectomes, capturing the complementary information from both imaging modalities, thereby introducing a new scope for biomarker development.

Triple modality image reconstruction of PET data using SPECT, PET, CT information increases lesion uptake in images of patients treated with radioembolization with ^{90}Y micro-spheres

PurposeNuclear medicine imaging modalities like computed tomography (CT), single photon emission CT (SPECT) and positron emission tomography (PET) are employed in the field of theranostics to estimate and plan the dose delivered to tumors and the surrounding tissues and to monitor the effect of the therapy. However, therapeutic radionuclides often provide poor images, which translate to inaccurate treatment planning and inadequate monitoring images. Multimodality information can be exploited in the reconstruction to enhance image quality. Triple modality PET/SPECT/CT scanners are particularly useful in this context due to the easier registration process between images. In this study, we propose to include PET, SPECT and CT information in the reconstruction of PET data. The method is applied to Yttrium-90 (^{90}Y) data.MethodsData from a NEMA phantom filled with ^{90}Y were used for validation. PET, SPECT and CT data from 10 patients treated with Selective Internal Radiation Therapy (SIRT) were used. Different combinations of prior images using the Hybrid kernelized expectation maximization were investigated in terms of VOI activity and noise suppression.ResultsOur results show that triple modality PET reconstruction provides significantly higher uptake when compared to the method used as standard in the hospital and OSEM. In particular, using CT-guided SPECT images, as guiding information in the PET reconstruction significantly increases uptake quantification on tumoral lesions.ConclusionThis work proposes the first triple modality reconstruction method and demonstrates up to 69% lesion uptake increase over standard methods with SIRT ^{90}Y patient data. Promising results are expected for other radionuclide combination used in theranostic applications using PET and SPECT.

Current Update on PET/MRI in Gynecological Malignancies-A Review of the Literature.

Early detection of gynecological malignancies is vital for patient management and prolonging the patient's survival. Molecular imaging, such as positron emission tomography (PET)/computed tomography, has been increasingly utilized in gynecological malignancies. PET/magnetic resonance imaging (MRI) enables the assessment of gynecological malignancies by combining the metabolic information of PET with the anatomical and functional information from MRI. This article will review the updated applications of PET/MRI in gynecological malignancies.

Review: Application Trends of Contrast Media in PET/MRI

Research on medical imaging contrast agents to improve diagnostic performance and optimize treatment strategies has been ongoing for a long time. Recently, hybrid imaging technology in which two different devices such as PET/CT, SPECT/CT, and PET/MRI are fused is emerging as a new trend in medical imaging. The high spatial resolution of Magnetic Resonance Imaging (MRI) and the high-sensitivity molecular-level information of Positron Emission Tomography (PET) by these convergence devices are leading to the development of new hybrid imaging technologies along with hybrid contrast agents. To create a hybrid contrast agent for simultaneous PET-MRI devices, the radiotracer for PET should be combined with the MRI contrast agent. The most common approach to achieve this is to coat radioisotopes to the surface of small superparamagnetic iron oxide (SPIO) particles. These contrast agents are used for pH change monitoring, non-invasive angiography, and early imaging diagnosis of atherosclerosis. In addition, tumor detection and cardiovascular imaging are becoming important research areas for the development of simultaneous PET/MRI imaging systems and hybrid contrast agents. Therefore, tumor detection and cardiovascular imaging are major areas for development of simultaneous PET/MRI imaging systems and hybrid contrast agents. The goal of developing hybrid contrast agents for this purpose is to combine high spatial resolution, high sensitivity, and morphological and functional information. The future prospect is considered to be the development of a multimodal hybrid contrast agent that detects diseases early, provides information before and after surgery for treatment, and provides treatment prognosis.

In vivo imaging of invasive aspergillosis with 18F-fluorodeoxysorbitol positron emission tomography

Invasive aspergillosis is a critical complication in immunocompromised patients with hematologic malignancies or with viral pneumonia caused by influenza virus or SARS‑CoV‑2. Although early and accurate diagnosis of invasive aspergillosis can maximize clinical outcomes, current diagnostic methods are time-consuming and poorly sensitive. Here, we assess the ability of 2-deoxy-2-18F-fluorosorbitol (18F-FDS) positron emission tomography (PET) to specifically and noninvasively detect Aspergillus infections. We show that 18F-FDS PET can be used to visualize Aspergillus fumigatus infection of the lungs, brain, and muscles in mouse models. In particular, 18F-FDS can distinguish pulmonary aspergillosis from Staphylococcus aureus infection, both of which induce pulmonary infiltrates in immunocompromised patients. Thus, our results indicate that the combination of 18F-FDS PET and appropriate clinical information may be useful in the differential diagnosis and localization of invasive aspergillosis.

Association of Sleep and β-Amyloid Pathology Among Older Cognitively Unimpaired Adults

Disrupted sleep commonly occurs with progressing neurodegenerative disease. Large, well-characterized neuroimaging studies of cognitively unimpaired adults are warranted to clarify the magnitude and onset of the association between sleep and emerging β-amyloid (Aβ) pathology. To evaluate the associations between daytime and nighttime sleep duration with regional Aβ pathology in older cognitively unimpaired adults. In this cross-sectional study, screening data were collected between April 1, 2014, and December 31, 2017, from healthy, cognitively unimpaired adults 65 to 85 years of age who underwent florbetapir F 18 positron emission tomography (PET), had APOE genotype information, scored between 25 and 30 on the Mini-Mental State Examination, and had a Clinical Dementia Rating of 0 for the Anti-Amyloid Treatment in Asymptomatic Alzheimer Disease (A4) Study. Data analysis was performed from December 1, 2019, to May 10, 2021. Self-reported daytime and nighttime sleep duration. Regional Aβ pathology, measured by florbetapir PET standardized uptake value ratio. Amyloid PET and sleep duration information was acquired on 4425 cognitively unimpaired participants (mean [SD] age, 71.3 [4.7] years; 2628 [59.4%] female; 1509 [34.1%] tested Aβ positive). Each additional hour of nighttime sleep was associated with a 0.005 reduction of global Aβ standardized uptake value ratio (F1, 4419 = 5.0; P = .03), a 0.009 reduction of medial orbitofrontal Aβ (F1, 4419 = 17.4; P < .001), and a 0.011 reduction of anterior cingulate Aβ (F1, 4419 = 15.9; P < .001). When restricting analyses to participants who tested Aβ negative, nighttime sleep was associated with a 0.006 reduction of medial orbitofrontal Aβ (F1,2910 = 16.9; P < .001) and a 0.005 reduction of anterior cingulate Aβ (F1,2910 = 7.6; P = .03). Daytime sleep was associated with a 0.013 increase of precuneus Aβ (F1,2910 = 7.3; P = .03) and a 0.024 increase of posterior cingulate Aβ (F1,2910 = 14.2; P = .001) in participants who tested Aβ negative. In this cross-sectional study, the increased risk of Aβ deposition with reduced nighttime sleep duration occurred early, before cognitive impairment or significant Aβ deposition. Daytime sleep may be associated with an increase in risk for early Aβ accumulation and did not appear to be corrective for loss of nighttime sleep, demonstrating a circadian rhythm dependence of sleep in preventing Aβ accumulation. Treatments that improve sleep may reduce early Aβ accumulation and aid in delaying the onset of cognitive dysfunction associated with early Alzheimer disease.

Abstract PO-029: Survival prediction of non-small cell lung cancer by deep learning model integrating clinical and positron emission tomography data

Abstract The Cox proportional hazards model (CPH), the standard method for survival analysis in cancer patients, is difficult to use maximal information of positron emission tomography (PET) images containing information that reflects underlying pathophysiology. In this retrospective study, we aimed to investigate a deep learning model integrating clinical and PET image data to improve survival prediction in non-small cell lung cancer (NSCLC) patients. We developed a bimodal learning model thorough DeepSurv-based model using 3D-CNN-based 3D-Resnet model for image data and deep neural network (DNN) model for clinical data. DeepSurv-based model combining clinical and PET image data showed the best performance among the four models, the c-index of the training and testing sets reaching 0.898 and 0.768, respectively, followed by DeepSurv-based model for single modalities (clinical (0.763/0.740) or PET (0.773/0.743)) and CPH (0.726/0.736). Deep learning-based survival prediction with combination of two modalities may improve prediction accuracy in NSCLC patients. Citation Format: Sae-Ryung Kang, Seungwon Oh, In-Jae Oh, Jung-Joon Min, Hee-Seung Bom, Hyung-Jeong Yang, Guee-Sang Lee, Soo-Hyung Kim, Min Soo Kim. Survival prediction of non-small cell lung cancer by deep learning model integrating clinical and positron emission tomography data [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-029.

The Applications of Radiomics for Predicting Survival Rate of Patients with Head and Neck Squamous Cell Carcinoma (HNSCC)

The radiomics based on positron emission tomography (PET) data and random forest can predict overall survival rate of head and neck squamous cell carcinoma (HNSCC). We used the texture features extracted from PET and clinical information from patients with HNSCC (n = 138). The Spearman

Practical setting and potential applications of interventions guided by PET/MRI.

Multimodality imaging has emerged from a vision thirty years ago to routine clinical use today. Positron emission tomography (PET)/magnetic resonance imaging (MRI) is still relatively new in this arena and particularly suitable for clinical research and technical development. PET/MRI-guidance for interventions opens up opportunities for novel treatments but at the same time demands certain technical and organizational requirements to be fulfilled. In this work, we aimed to demonstrate a practical setting and potential application of PET/MRI guidance of interventional procedures. The superior quantitative physiologic information of PET, the various unique imaging characteristics of MRI, and the reduced radiation exposure are the most relevant advantages of this technique. As a noninvasive interventional tool, focused ultrasound (FUS) ablation of tumor cells would benefit from PET/MRI for diagnostics, treatment planning and intervention. Yet, technical limitations might impeed preclinical research, given that PET/MRI sites are per se not designed as interventional suites. Nonetheless, several approaches have been offered in the past years to upgrade MRI suites for interventional purposes. Taking advantage of state of the art and easy-to-use technology it is possible to create a supporting infrastructure that is suitable for broad preclinical adaption. Several aspects are to be addressed, including remote control of the imaging system, display of the imaging results, communication technology, and implementation of additional devices such as a FUS platform and an MR-compatible robotic system for positioning of the FUS equipment. Feasibility could be demostrated with an examplary experimental setup for interventional PET/MRI. Most PET/MRI sites could allow for interventions with just a few add-ons and modifications, such as comunication, in room image display and sytems control. By unlocking this feature, and driving preclinical research in interventional PET/MRI, translation of the protocol and methodology into clinical settings seems feasible.

Quantitative Comparison of Prone and Supine PERCIST Measurements in Breast Cancer.

Positron emission tomography (PET) is typically performed in the supine position. However, breast magnetic resonance imaging (MRI) is performed in prone, as this improves visibility of deep breast tissues. With the emergence of hybrid scanners that integrate molecular information from PET and functional information from MRI, it is of great interest to determine if the prognostic utility of prone PET is equivalent to supine. We compared PERCIST (PET Response Criteria in Solid Tumors) measurements between prone and supine FDG-PET in patients with breast cancer and the effect of orientation on predicting pathologic complete response (pCR). In total, 47 patients were enrolled and received up to 6 cycles of neoadjuvant therapy. Prone and supine FDG-PET were performed at baseline (t0; n = 46), after cycle 1 (t1; n = 1) or 2 (t2; n = 10), or after all neoadjuvant therapy (t3; n = 19). FDG uptake was quantified by maximum and peak standardized uptake value (SUV) with and without normalization to lean body mass; that is, SUVmax, SUVpeak, SULmax, and SULpeak. PERCIST measurements were performed for each paired baseline and post-treatment scan. Receiver operating characteristic analysis for the prediction of pCR was performed using logistic regression that included age and tumor size as covariates. SUV and SUL metrics were significantly different between orientation (P < .001), but were highly correlated (P > .98). Importantly, no differences were observed with the PERCIST measurements (P > .6). Overlapping 95% confidence intervals for the receiver operating characteristic analysis suggested no difference at predicting pCR. Therefore, prone and supine PERCIST in this data set were not statistically different.

Fusion of Brain PET and MRI Images Using Tissue-Aware Conditional Generative Adversarial Network With Joint Loss

Positron emission tomography (PET) has rich pseudo color information that reflects the functional characteristics of tissue, but lacks structural information and its spatial resolution is low. Magnetic resonance imaging (MRI) has high spatial resolution as well as strong structural information of soft tissue, but lacks color information that shows the functional characteristics of tissue. For the purpose of integrating the color information of PET with the anatomical structures of MRI to help doctors diagnose diseases better, a method for fusing brain PET and MRI images using tissue-aware conditional generative adversarial network (TA-cGAN) is proposed. Specifically, the process of fusing brain PET and MRI images is treated as an adversarial machine between retaining the color information of PET and preserving the anatomical information of MRI. More specifically, the fusion of PET and MRI images can be regarded as a min-max optimization problem with respect to the generator and the discriminator, where the generator attempts to minimize the objective function via generating a fused image mainly contains the color information of PET, whereas the discriminator tries to maximize the objective function through urging the fused image to include more structural information of MRI. Both the generator and the discriminator in TA-cGAN are conditioned on the tissue label map generated from MRI image, and are trained alternatively with joint loss. Extensive experiments demonstrate that the proposed method enhances the anatomical details of the fused image while effectively preserving the color information from the PET. In addition, compared with other state-of-the-art methods, the proposed method achieves better fusion effects both in subjectively visual perception and in objectively quantitative assessment.

The Role of Positron Emission Tomography/Magnetic Resonance Imaging in Gynecological Malignancies

Accurate oncological staging for early detection is of utmost importance in patient care and increasing the overall patient survival outcome. Hybrid imaging in the form of positron emission tomography (PET)/computed tomography has been successfully implemented in oncological imaging and, where available, has been used consistently in patients with gynecologic malignancies. The implementation of PET/magnetic resonance imaging (MRI) enables high-quality assessment of gynecological malignancies by combining the diagnostic advantages of metabolic information of PET along with the high-resolution anatomical and functional information from the MRI to provide precise information about staging, recurrence, and metastases. This article will review the various applications of PET/MRI in gynecological cancer.

Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks.

To investigate the use and efficiency of 3-D deep learning, fully convolutional networks (DFCN) for simultaneous tumor cosegmentation on dual-modality nonsmall cell lung cancer (NSCLC) and positron emission tomography (PET)-computed tomography (CT) images. We used DFCN cosegmentation for NSCLC tumors in PET-CT images, considering both the CT and PET information. The proposed DFCN-based cosegmentation method consists of two coupled three-dimensional (3D)-UNets with an encoder-decoder architecture, which can communicate with the other in order to share complementary information between PET and CT. The weighted average sensitivity and positive predictive values denoted as Scores, dice similarity coefficients (DSCs), and the average symmetric surface distances were used to assess the performance of the proposed approach on 60 pairs of PET/CTs. A Simultaneous Truth and Performance Level Estimation Algorithm (STAPLE) of 3 expert physicians' delineations were used as a reference. The proposed DFCN framework was compared to 3 graph-based cosegmentation methods. Strong agreement was observed when using the STAPLE references for the proposed DFCN cosegmentation on the PET-CT images. The average DSCs on CT and PET are 0.861±0.037 and 0.828±0.087, respectively, using DFCN, compared to 0.638±0.165 and 0.643±0.141, respectively, when using the graph-based cosegmentation method. The proposed DFCN cosegmentation using both PET and CT also outperforms the deep learning method using either PET or CT alone. The proposed DFCN cosegmentation is able to outperform existing graph-based segmentation methods. The proposed DFCN cosegmentation shows promise for further integration with quantitative multimodality imaging tools in clinical trials.

Effect of PET-MR Inconsistency in the Kernel Image Reconstruction Method.

Anatomically-driven image reconstruction algorithms have become very popular in positron emission tomography (PET) where they have demonstrated improved image resolution and quantification. This work, consider the effect of spatial inconsistency between MR and PET images in hot and cold regions of the PET image. We investigate these effects on the kernel method from machine learning, in particular, the hybrid kernelized expectation maximization (HKEM). These were applied to Jaszczak phantom and patient data acquired with the Biograph Siemens mMR. The results show that even a small shift can cause a significant change in activity concentration. In general, the PET-MR inconsistencies can induce the partial volume effect, more specifically the 'spill-in' of the affected cold regions and the 'spill-out' from the hot regions. The maximum change was about 100% for the cold region and 10% for the hot lesion using KEM, against the 37% and 8% obtained with HKEM. The findings of this work suggest that including PET information in the kernel enhances the flexibility of the reconstruction in case of spatial inconsistency. Nevertheless, accurate registration and choice of the appropriate MR image for the creation of the kernel is essential to avoid artifacts, blurring, and bias.

Direct Patlak Reconstruction From Dynamic PET Data Using the Kernel Method With MRI Information Based on Structural Similarity.

Positron emission tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neuroscience. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information into image reconstruction. Previously, kernel learning has been successfully embedded into static and dynamic PET image reconstruction using either PET temporal or MRI information. Here, we combine both PET temporal and MRI information adaptively to improve the quality of direct Patlak reconstruction. We examined different approaches to combine the PET and MRI information in kernel learning to address the issue of potential mismatches between MRI and PET signals. Computer simulations and hybrid real-patient data acquired on a simultaneous PET/MR scanner were used to evaluate the proposed methods. Results show that the method that combines PET temporal information and MRI spatial information adaptively based on the structure similarity index has the best performance in terms of noise reduction and resolution improvement.

Maximum-Likelihood Estimation of Scintillation Pulse Timing

Including time-of-flight information in positron emission tomography (PET) reconstruction increases the signal-to-noise ratio if the timing information is sufficiently accurate. We estimate timing information by analyzing sampled waveforms, where the sampling frequency and number of samples acquired affect the accuracy of timing estimation. An efficient data-acquisition system acquires the minimum number of samples that contains the most timing information for a desired resolution. We describe a maximum-likelihood (ML) estimation algorithm to assign a time stamp to digital pulses. The method is based on a contracting-grid search algorithm that can be implemented in a field-programmable gate array and in graphics processing units. The Fisher-information (FI) matrix quantifies the amount of timing information that can be extracted from the waveforms. FI analyses on different segments of the waveform allow us to determine the smallest amount of data that we need to acquire in order to obtain a desired timing resolution. We describe the model and the procedure used to simulate waveforms for ML estimation and FI analysis, the ML-estimation algorithm and the timing resolution obtained from experimental data using a LaBr3:Ce crystal and two photomultiplier tubes. The results show that for lengthening segments of the pulse, timing resolution approaches a limit. We explored the method as a function of sampling frequency and compared the results to other digital time pickoff methods. This information will be used to build an efficient data-acquisition system with reduced complexity and cost that nonetheless preserves full timing performance.

Proof-of-concept prototype time-of-flight PET system based on high-quantum-efficiency multianode PMTs.

Time-of-flight (TOF) information in positron emission tomography (PET) scanners enhances the diagnostic power of PET scans owing to the increased signal-to-noise ratio of reconstructed images. There are numerous additional benefits of TOF reconstruction, including the simultaneous estimation of activity and attenuation distributions from emission data only. Exploring further TOF gains by using TOF PET scanners is important because it can broaden the applications of PET scans and expand our understanding of TOF techniques. Herein, we present a prototype TOF PET scanner with fine-time performance that can experimentally demonstrate the benefits of TOF information. A single-ring PET system with a coincidence resolving time of 360ps and a spatial resolution of 3.1/2.2mm (filtered backprojection/ordered-subset expectation maximization) was developed. The scanner was based on advanced high-quantum-efficiency (high-QE) multianode photomultiplier tubes (PMTs). The impact of its fine-time performance was demonstrated by evaluating body phantom images reconstructed with and without TOF information. Moreover, the feasibility of the scanner as an experimental validator of TOF gains was verified by investigating the improvement of images under various conditions, such as the use of joint estimation algorithms of activity and attenuation, erroneous data correction factors (e.g., without normalization correction), and incompletely sampled data. The prototype scanner showed excellent performance, producing improved phantom images, when TOF information was employed in the reconstruction process. In addition, investigation of the TOF benefits using the phantom data in different conditions verified the usefulness of the developed system for demonstrating the practical effects of TOF reconstruction. We developed a prototype TOF PET scanner with good performance and a fine-timing resolution based on advanced high-QE multianode PMTs and demonstrated its feasibility as an experimental validator of TOF gains, suggesting its usefulness for investigating new applications of PET scans and clarifying TOF techniques in detail.