Effects of TOF Resolution Models on Edge Artifacts in PET Reconstruction from Limited-Angle Data

Flexible and modular PET: Evaluating the potential of TOF-DOI panel detectors

BACKGROUND

Panel detectors have the potential to provide a flexible, modular approach to Positron Emission Tomography (PET), enabling customization to meet patient-specific needs and scan objectives. The panel design allows detectors to be positioned close to the patient, aiming to enhance sensitivity and spatial resolution through improved geometric coverage and reduced noncollinearity blurring. Parallax error can be mitigated using depth of interaction (DOI) information.

PURPOSE

One of the key questions the article addresses is: Do panel detectors offer viable clinical imaging capabilities, or does limited angular sampling restrict their utility by causing image distortions and artifacts? Additionally, this article explores the scalability of panel detectors for constructing scanners with a long axial field of view (LAFOV).

METHODS

Monte Carlo simulations using GATE software were used to assess the performance of panel detectors with various DOI resolutions and Time-of-Flight (TOF) resolutions as fine as 70 ps. The 30 × $\times$ 30 cm panels comprised pixelated 3 × $\times$ 3 × $\times$ 20 mm LSO crystals. Simulations were run on large high-performance computing clusters (122,000 CPU cores). Open-source CASToR software was used for (TOF MLEM) image reconstruction. The image quality of the scanners was assessed using a range of phantoms (NEMA, Derenzo, XCAT, and a high-resolution brain phantom). The Siemens Biograph Vision PET/CT scanner served as the reference model. The performance of larger 120 × $\times$ 60 cm panels was also evaluated.

RESULTS

Sensitivity increases over threefold when panel-panel distance is reduced from 80 to 40 cm. The noise equivalent count rate, unmodified by TOF gain, of the panel detectors matches that of the reference clinical scanner at a distance of approximately 50 cm between the panels. Spatial resolution perpendicular to the panels improves from 8.7 to 1.6 mm when the panel-panel distance is reduced, and 70 ps + DOI detectors are used instead of 200 ps, no-DOI detectors. With enhanced TOF and DOI capabilities, panel detectors achieve image quality that matches or surpasses the reference scanner while using about four times less detector material. These detectors can be extended for LAFOV imaging without distortions or artifacts. Additionally, improving TOF and DOI performance enhances contrast-to-noise ratios, thereby improving lesion detection.

CONCLUSIONS

A compact 2-panel PET scanner can match the performance of conventional scanners, producing high-quality, distortion-free images. Its mobility and flexibility enable novel applications, including bedside imaging and intensive care unitdiagnostics, as well as imaging in positions such as sitting or standing. Furthermore, the modularity of panel detectors offers the potential to construct cost-effective, high-performance total-body imaging systems.

Initial demonstration of the Scratch-PET concept: an intraoperative PET with a hand-held detector

Positron emission tomography (PET) is a valuable tool for diagnosing malignant tumors. Intraoperative PET imaging is expected to allow the more accurate localization of tumors that need resections. However, conventional devices feature a large detector ring that obstructs surgical procedures, preventing their intraoperative application. This paper proposes a new PET device, Scratch-PET, for image-guided tumor resection. The key feature of Scratch-PET is its use of a hand-held detector to scan the surgical field, ensuring open space for surgery while measuring annihilation radiation with a fixed detector array placed below the patient. We developed a prototype device using two detectors: the hand-held detector and a fixed detector, to demonstrate the feasibility of the proposed concept. Both detectors consisted of 16 × 16 arrays of lutetium yttrium orthosilicates (3 × 3 × 15 mm3) coupled one-to-one with 16 × 16 silicon photomultiplier arrays. The position and orientation of the hand-held detector are tracked using an optical tracking sensor that detects attached markers. We measured a 22Na multi-rod phantom and two 22Na point sources separately for 180 s while moving the hand-held detector. The rod diameters were 6.0, 5.0, 4.0, 3.0, 2.2, and 1.6 mm. Each point source was placed at the field-of-view center and 35 mm off-center which was outside the sensitive area when the hand-held detector was positioned facing the fixed detector. The 2.2 mm rods were partially resolved, and both point sources were successfully visualized. The potential of the proposed device to visualize small tumors was validated.

Design and proof of concept of a double-panel TOF-PET system

OBJECTIVE

Positron Emission Tomography (PET) is a well-known imaging technology for the diagnosis, treatment, and monitoring of several diseases. Most PET scanners use a Ring-Shaped Detector Configuration (RSDC), which helps obtain homogeneous image quality but are restricted to an invariable Field-of-View (FOV), scarce spatial resolution, and low sensitivity. Alternatively, few PET systems use Open Detector Configurations (ODC) to permit an accessible FOV adaptable to different target sizes, thus optimizing sensitivity. Yet, to compensate the lack of angular coverage in ODC-PET, developing a detector with high-timing performance is mandatory to enable Time-of-Flight (TOF) techniques during reconstruction. The main goal of this work is to provide a proof of concept PET scanner appropriate for constructing the new generation of ODC-PET suitable for biopsy guidance and clinical intervention during acquisition. The designed detector has to be compact and robust, and its requirements in terms of performance are spatial and time resolutions < 2 mm and < 200 ps, respectively.

METHODS

The present work includes a simulation study of an ODC-PET based on 2-panels with variable distance. The image quality (IQ) and Derenzo phantoms have been simulated and evaluated. The phantom simulations have also been performed using a ring-shaped PET for comparison purposes of the ODC approach with conventional systems. Then, an experimental evaluation of a prototype detector that has been designed following the simulation results is presented. This study focused on tuning the ASIC parameters and evaluating the scintillator surface treatment (ESR and TiO2), and configuration that yields the best Coincidence Time Resolution (CTR). Moreover, the scalability of the prototype to a module of 64 × 64mm2 and its preliminary evaluation regarding pixel identification are provided.

RESULTS

The simulation results reported sensitivity (%) values at the center of the FOV of 1.96, 1.63, and 1.18 for panel distances of 200, 250, and 300 mm, respectively. The IQ reconstructed image reported good uniformity (87%) and optimal CRC values, and the Derenzo phantom reconstruction suggests a system resolution of 1.6-2 mm. The experimental results demonstrate that using TiO2 coating yielded better detector performance than ESR. Acquired data was filtered by applying an energy window of ± 30% at the photopeak level. After filtering, best CTR of 230 ± 2 ps was achieved for an 8 × 8 LYSO pixel block with 2 × 2 × 12mm3 each. The detector performance remained constant after scaling-up the prototype to a module of 64 × 64mm2, and the flood map demonstrates the module's capabilities to distinguish the small pixels; thus, a spatial resolution < 2 mm (pixel size) is achieved.

CONCLUSIONS

The simulated results of this biplanar scanner show high performance in terms of image quality and sensitivity. These results are comparable to state-of-the-art PET technology and, demonstrate that including TOF information minimizes the image artifacts due to the lack of angular projections. The experimental results concluded that using TiO2 coating provide the best performance. The results suggest that this scanner may be suitable for organ study, breast, prostate, or cardiac applications, with good uniformity and CRC.

Recovery of the spatially-variant deformations in dual-panel PET reconstructions using deep-learning

Dual panel PET systems, such as Breast-PET (B-PET) scanner, exhibit strong asymmetric and anisotropic spatially-variant deformations in the reconstructed images due to the limited-angle data and strong depth of interaction effects for the oblique LORs inherent in such systems. In our previous work, we studied time-of-flight (TOF) effects and image-based spatially-variant PSF resolution models within dual-panel PET reconstruction to reduce these deformations. The application of PSF based models led to better and more uniform quantification of small lesions across the field of view (FOV). However, the ability of such a model to correct for PSF deformation is limited to small objects. On the other hand, large object deformations caused by the limited-angle reconstruction cannot be corrected with the PSF modeling alone. In this work, we investigate the ability of deep-learning (DL) networks to recover such strong spatially-variant image deformations using first simulated PSF deformations in image space of a generic dual panel PET system and then using simulated and acquired phantom reconstructions from dual panel B-PET system developed in our lab at University of Pennsylvania. For the studies using real B-PET data, the network was trained on the simulated synthetic data sets providing ground truth for objects resembling experimentally acquired phantoms on which the network deformation corrections were then tested. The synthetic and acquired limited-angle B-PET data were reconstructed using DIRECT-RAMLA reconstructions, which were then used as the network inputs. Our results demonstrate that DL approaches can significantly eliminate deformations of limited angle systems and improve their quantitative performance.

3D directional gradient L0 norm minimization guided limited-view reconstruction in a dual-panel positron emission mammography

BACKGROUND

Dual-panel PET is often used for local organ imaging, especially breast imaging, due to its simple structure, high sensitivity, good in-plane resolution, and straightforward fusion with other imaging modalities. Nevertheless, because of data loss caused by the dual-panel structure, using conventional image reconstruction methods results in limited-view artifacts and low image quality in dual-panel positron emission mammography (PEM), which may seriously affect the diagnosis. To mitigate the limited-view artifacts in the dual-panel PEM, we propose a 3D directional gradient L₀ norm minimization (3D-DL₀) guided reconstruction method.

METHODS

The detailed derivation and reasonable simplification of the 3D-DL₀ algorithm are given first. Using this algorithm, we then obtain a prior image with edge recovery but contrast loss. To limit the solution space, the 3D-DL₀ prior is introduced into the Maximum a Posteriori reconstruction. Meanwhile, a space-invariant point spread function is also implemented to restore image contrast and boundaries. Finally, the reconstructed images with limited-view artifact suppression are obtained. The proposed method was evaluated using the data acquired from physical phantoms and patients with breast tumors on a commercial dual-panel PET system.

RESULTS

The qualitative and quantitative studies for phantom data and the blind reader study for clinical data show that the proposed method is more effective in reaching a balance between artifact elimination and image contrast improvement compared with various limited-view reconstruction methods. In addition, the iteration process of the method is proved convergent numerically.

CONCLUSIONS

The image quality improvement confirms the potential value of the proposed reconstruction algorithm to address the limited-view problem, and thus improve diagnostic accuracy in dual-panel PEM imaging.

Time of Flight in Perspective: Instrumental and Computational Aspects of Time Resolution in Positron Emission Tomography

The first time-of-flight positron emission tomography (TOF-PET) scanners were developed as early as in the 1980s. However, the poor light output and low detection efficiency of TOF-capable detectors available at the time limited any gain in image quality achieved with these TOF-PET scanners over the traditional non-TOF PET scanners. The discovery of LSO and other Lu-based scintillators revived interest in TOF-PET and led to the development of a second generation of scanners with high sensitivity and spatial resolution in the mid-2000s. The introduction of the silicon photomultiplier (SiPM) has recently yielded a third generation of TOF-PET systems with unprecedented imaging performance. Parallel to these instrumentation developments, much progress has been made in the development of image reconstruction algorithms that better utilize the additional information provided by TOF. Overall, the benefits range from a reduction in image variance (SNR increase), through allowing joint estimation of activity and attenuation, to better reconstructing data from limited angle systems. In this work, we review these developments, focusing on three broad areas: 1) timing theory and factors affecting the time resolution of a TOF-PET system; 2) utilization of TOF information for improved image reconstruction; and 3) quantification of the benefits of TOF compared to non-TOF PET. Finally, we offer a brief outlook on the TOF-PET developments anticipated in the short and longer term. Throughout this work, we aim to maintain a clinically driven perspective, treating TOF as one of multiple (and sometimes competitive) factors that can aid in the optimization of PET imaging performance.

2D feasibility study of joint reconstruction of attenuation and activity in limited angle TOF-PET

Several research groups are studying organ-dedicated limited angle positron emission tomography (PET) systems to optimize performance-cost ratio, sensitivity, access to the patient and/or flexibility. Often open systems are considered, typically consisting of two detector panels of various sizes. Such systems provide incomplete sampling due to limited angular coverage and/or truncation, which leads to artefacts in the reconstructed activity images. In addition, these organ-dedicated PET systems are usually stand-alone systems, and as a result, no attenuation information can be obtained from anatomical images acquired in the same imaging session. It has been shown that the use of time-of-flight information reduces incomplete data artefacts and enables the joint estimation of the activity and the attenuation factors. In this work, we explore with simple 2D simulations the performance and stability of a joint reconstruction algorithm, for imaging with a limited angle PET system. The reconstruction is based on the so-called MLACF (Maximum Likelihood Attenuation Correction Factors) algorithm and uses linear attenuation coefficients in a known-tissue-class region to obtain absolute quantification. Different panel sizes and different time-of-flight (TOF) resolutions are considered. The noise propagation is compared to that of MLEM reconstruction with exact attenuation correction (AC) for the same PET system. The results show that with good TOF resolution, images of good visual quality can be obtained. If also a good scatter correction can be implemented, quantitative PET imaging will be possible. Further research, in particular on scatter correction, is required.

One-View Time-of-Flight Positron Emission Tomography Reconstruction

The time-of-flight (TOF) information makes Orlov's condition not necessary for positron emission tomography (PET) imaging. One-view imaging has never been attempted before. This paper investigates whether one-view imaging is possible. We claim that it is possible to obtain an image with measurements from only one-view, provided the TOF time resolution is good enough and the photon counts are high enough. In fact, the image value at a point can be reconstructed by measurements along one line-of-response (LOR) that passes through the point of interest. The region-of-interest (ROI) can be reconstructed with ray-by-ray deblurring methods, which make TOF PET a local tomography system. One-view imaging is a severely ill-posed problem if the TOF resolution is not good enough. One indicator of an ill-posed problem is the ringing artifacts in the reconstruction. We show that ringing artifacts can be eliminated by using the nonnegativity constraint for the dot-like objects with a zero background.

Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning

The spectral computed tomography (CT) has huge advantages by providing accurate material information. Unfortunately, due to the instability or overdetermination of material decomposition model, the accuracy of material decomposition can be compromised in practice. Very recently, the dictionary learning based image-domain material decomposition (DLIMD) can obtain high accuracy for material decompositions from reconstructed spectral CT images. This method can explore the correlation of material components to some extent by training a unified dictionary from all material images. In addition, the dictionary learning based prior as a penalty is applied on material components independently, and many parameters would be carefully elaborated in practice. Because the concentration of contrast agent in clinical applications is low, it can result in data inconsistency for dictionary based representation during the iteration process. To avoid the aforementioned limitations and further improve the accuracy of materials, we first construct a generalized dictionary learning based image-domain material decomposition (GDLIMD) model. Then, the material tensor image is unfolded along the mode-1 to enhance the correlation of different materials. Finally, to avoid the data inconsistency of low iodine contrast, a normalization strategy is employed. Both physical phantom and tissue-synthetic phantom experiments demonstrate the proposed GDLIMD method outperforms the DLIMD and direct inversion (DI) methods.

Dictionary learning based image-domain material decomposition for spectral CT

The potential huge advantage of spectral computed tomography (CT) is that it can provide accurate material identification and quantitative tissue information by material decomposition. However, material decomposition is a typical inverse problem, where the noise can be magnified. To address this issue, we develop a dictionary learning based image-domain material decomposition (DLIMD) method for spectral CT to achieve accurate material components with better image quality. Specifically, a set of image patches are extracted from the mode-1 unfolding of normalized material images decomposed by direct inversion to train a unified dictionary using the K-SVD technique. Then, the DLIMD model is established to explore the redundant similarities of the material images, where the split-Bregman is employed to optimize the model. Finally, more constraints (i.e. volume conservation and the bounds of each pixel within material maps) are integrated into the DLIMD model. Numerical phantom, physical phantom and preclinical experiments are performed to evaluate the performance of the proposed DLIMD in material decomposition accuracy, material image edge preservation and feature recovery.

TOF-PET Image Reconstruction With Multiple Timing Kernels Applied on Cherenkov Radiation in BGO

Today Time-of-Flight (TOF), in PET scanners, assumes a single, well-defined timing resolution for all events. However, recent BGO-Cherenkov detectors, combining prompt Cherenkov emission and the typical BGO scintillation, can sort events into multiple timing kernels, best described by the Gaussian mixture models. The number of Cherenkov photons detected per event impacts directly the detector time resolution and signal rise time, which can later be used to improve the coincidence timing resolution. This work presents a simulation toolkit which applies multiple timing spreads on the coincident events and an image reconstruction that incorporates this information. A full cylindrical BGO-Cherenkov PET model was compared, in terms of contrast recovery and contrast-to-noise ratio, against an LYSO model with a time resolution of 213 ps. Two reconstruction approaches for the mixture kernels were tested: 1) mixture Gaussian and 2) decomposed simple Gaussian kernels. The decomposed model used the exact mixture component applied during the simulation. Images reconstructed using mixture kernels provided similar mean value and less noise than the decomposed. However, typically, more iterations were needed. Similarly, the LYSO model, with a single TOF kernel, converged faster than the BGO-Cherenkov with multiple kernels. The results indicate that the model complexity slows down convergence. However, due to the higher sensitivity, the contrast-to-noise ratio was 26.4% better for the BGO model.