Improving depth-of-interaction resolution in pixellated PET detectors using neural networks

A multi-resolution TOF-DOI detector for human brain dedicated PET scanner

Objective. We propose a single-ended readout, multi-resolution detector design that can achieve high spatial, depth-of-interaction (DOI), and time-of-flight (TOF) resolutions, as well as high sensitivity for human brain-dedicated positron emission tomography (PET) scanners.Approach. The detector comprised two layers of LYSO crystal arrays and a lightguide in between. The top (gamma ray entrance) layer consisted of a 16 × 16 array of 1.53 × 1.53 × 6 mm3LYSO crystals for providing high spatial resolution. The bottom layer consisted of an 8 × 8 array of 3.0 × 3.0 × 15 mm3LYSO crystals that were one-to-one coupled to an 8 × 8 multipixel photon counter (MPPC) array for providing high TOF resolution. The 2 mm thick lightguide introduces inter-crystal light sharing that causes variations of the light distribution patterns for high DOI resolution. The detector was read out by a PETsys TOFPET2 application-specific integrated circuit.Main result. The top and bottom layers were distinguished by a convolutional neural network with 97% accuracy. All crystals in the top and bottom layers were resolved. The inter-crystal scatter (ICS) events in the bottom layer were identified, and the measured average DOI resolution of the bottom layer was 4.1 mm. The coincidence time resolution (CTR) for the top-top, top-bottom, and bottom-bottom coincidences was 476 ps, 405 ps, and 298 ps, respectively. When ICS events were excluded from the bottom layer, the CTR of the bottom-bottom coincidence was 277 ps.Significance. The top layer of the proposed two-layer detector achieved a high spatial resolution and the bottom layer achieved a high TOF resolution. Together with its high DOI resolution and detection efficiency, the proposed detector is well suited for next-generation high-performance brain-dedicated PET scanners.

The quest for multifunctional and dedicated PET instrumentation with irregular geometries

We focus on reviewing state-of-the-art developments of dedicated PET scanners with irregular geometries and the potential of different aspects of multifunctional PET imaging. First, we discuss advances in non-conventional PET detector geometries. Then, we present innovative designs of organ-specific dedicated PET scanners for breast, brain, prostate, and cardiac imaging. We will also review challenges and possible artifacts by image reconstruction algorithms for PET scanners with irregular geometries, such as non-cylindrical and partial angular coverage geometries and how they can be addressed. Then, we attempt to address some open issues about cost/benefits analysis of dedicated PET scanners, how far are the theoretical conceptual designs from the market/clinic, and strategies to reduce fabrication cost without compromising performance.

Detectors in positron emission tomography

Positron emission tomography is a highly sensitive molecular imaging modality, based on the coincident detection of annihilation photons after positron decay. The most used detector is based on dense, fast, and luminous scintillators read out by light sensors. This review covers the various detector concepts for clinical and preclinical systems.

A role for artificial intelligence in molecular imaging of infection and inflammation

The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers’ expertise. Although molecular imaging, like [¹⁸F]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment.

A neural network-based algorithm for simultaneous event positioning and timestamping in monolithic scintillators

Objective. Monolithic scintillator crystals coupled to silicon photomultiplier (SiPM) arrays are promising detectors for PET applications, offering spatial resolution around 1 mm and depth-of-interaction information. However, their timing resolution has always been inferior to that of pixellated crystals, while the best results on spatial resolution have been obtained with algorithms that cannot operate in real-time in a PET detector. In this study, we explore the capabilities of monolithic crystals with respect to spatial and timing resolution, presenting new algorithms that overcome the mentioned problems. Approach. Our algorithms were tested first using a simulation framework, then on experimentally acquired data. We tested an event timestamping algorithm based on neural networks which was then integrated into a second neural network for simultaneous estimation of the event position and timestamp. Both algorithms are implemented in a low-cost field-programmable gate array that can be integrated in the detector and can process more than 1 million events per second in real-time. Results. Testing the neural network for the simultaneous estimation of the event position and timestamp on experimental data we obtain 0.78 2D FWHM on the (x, y) plane, 1.2 depth-of-interaction FWHM and 156 coincidence time resolution on a 25 mm × 25 mm × 8 mm × LYSO monolith read-out by 64 3 mm × 3 mm Hamamatsu SiPMs. Significance. Our results show that monolithic crystals combined with artificial intelligence can rival pixellated crystals performance for time-of-flight PET applications, while having better spatial resolution and DOI resolution. Thanks to the use of very light neural networks, event characterization can be done on-line directly in the detector, solving the issues of scalability and computational complexity that up to now were preventing the use of monolithic crystals in clinical PET scanners.

Artificial intelligence with deep learning in nuclear medicine and radiology

The use of deep learning in medical imaging has increased rapidly over the past few years, finding applications throughout the entire radiology pipeline, from improved scanner performance to automatic disease detection and diagnosis. These advancements have resulted in a wide variety of deep learning approaches being developed, solving unique challenges for various imaging modalities. This paper provides a review on these developments from a technical point of view, categorizing the different methodologies and summarizing their implementation. We provide an introduction to the design of neural networks and their training procedure, after which we take an extended look at their uses in medical imaging. We cover the different sections of the radiology pipeline, highlighting some influential works and discussing the merits and limitations of deep learning approaches compared to other traditional methods. As such, this review is intended to provide a broad yet concise overview for the interested reader, facilitating adoption and interdisciplinary research of deep learning in the field of medical imaging.

Application of Artificial Intelligence in PET Instrumentation

Artificial intelligence (AI) has been widely used throughout medical imaging, including PET, for data correction, image reconstruction, and image processing tasks. However, there are number of opportunities for the application of AI in photon detector performance or the data collection process, such as to improve detector spatial resolution, time-of-flight information, or other PET system performance characteristics. This review outlines current topics, research highlights, and future directions of AI in PET instrumentation.

Development of dual-ended depth-of-interaction detectors using laser-induced crystals for small animal PET systems

Sensitivity and spatial resolution of positron emission tomography (PET) scanners can be improved by using thicker scintillation crystals with depth-of-interaction (DOI) encoding. Subsurface laser engraving (SSLE) can be used to segment crystals of a scintillation detector in order to fabricate a DOI detector. We previously applied SSLE to crystal bars of 3 × 3 × 20 mm³and 1.5 × 1.5 × 20 mm³and developed two dual-ended detectors with DOI segments of 3 mm and 1.5 mm, respectively. To further improve the DOI resolution, our SSLE detector design can be used with smaller pitch crystal bars, making them excellent detector candidates for small animal PET scanners with submillimetre resolution. In the present study, three small crystal bars of 1 × 1 × 20 mm³, 2 × 1 × 20 mm³, and 2 × 1 × 40 mm³were laser engraved to 12, 20 and 40 segments, respectively, by applying SSLE in their height directions. The segmented crystal bars were characterised in three prototype detector arrangements. First, the 1 × 1 × 20 mm³crystal bars were characterised in an 8 × 8 crystal array designed for DOI encoding along crystal height in a conventional small animal PET design. Second, a 4 × 8 crystal array of 2 × 1 × 20 mm³crystal bars was characterised for using the DOI information for crystal interaction positioning along the axial axis of a small animal PET scanner. Finally, the third part of the study was performed on a single 2 × 1 × 40 mm³crystal bar with 40 segments to investigate the feasibility of DOI estimation in longer crystals for application in a system with extended axial length. We evaluated the capability of segment identification and energy resolution of theses detectors. The 3D position maps of the detectors were obtained using the Anger-type calculation and the crystal identification performance was evaluated for each detector. Clear segment separation was obtained for the crystal arrays with 12 (segment pitch of 1.67 mm) and 20 (segment pitch of 1 mm) segments. Mean energy resolutions of 8.8% ± 0.4% and 9.6% ± 0.8% at 511 keV were obtained for the segments in the central regions of the 8 × 8 array with 12 segments and the 4 × 8 array with 20 segments, respectively. Clear segment identification was found to be difficult for the detector with 40 segments, especially for the segments at the middle of the crystal. Energy and interaction positioning characterisation results suggest that both prototype detectors with 12 and 20 segments are well suited for small animal PET scanners with high spatial resolution.

Advanced Monte Carlo simulations of emission tomography imaging systems with GATE

Built on top of the Geant4 toolkit, GATE is collaboratively developed for more than 15 years to design Monte Carlo simulations of nuclear-based imaging systems. It is, in particular, used by researchers and industrials to design, optimize, understand and create innovative emission tomography systems. In this paper, we reviewed the recent developments that have been proposed to simulate modern detectors and provide a comprehensive report on imaging systems that have been simulated and evaluated in GATE. Additionally, some methodological developments that are not specific for imaging but that can improve detector modeling and provide computation time gains, such as Variance Reduction Techniques and Artificial Intelligence integration, are described and discussed.

H2RSPET: a 0.5 mm resolution high-sensitivity small-animal PET scanner, a simulation study

With the goal of developing a total-body small-animal PET system with a high spatial resolution of ∼0.5 mm and a high sensitivity >10% for mouse/rat studies, we simulated four scanners using the graphical processing unit-based Monte Carlo simulation package (gPET) and compared their performance in terms of spatial resolution and sensitivity. We also investigated the effect of depth-of-interaction (DOI) resolution on the spatial resolution. All the scanners are built upon 128 DOI encoding dual-ended readout detectors with lutetium yttrium oxyorthosilicate (LYSO) arrays arranged in 8 detector rings. The solid angle coverages of the four scanners are all ∼0.85 steradians. Each LYSO element has a cross-section of 0.44 × 0.44 mm² and the pitch size of the LYSO arrays are all 0.5 mm. The four scanners can be divided into two groups: (1) H²RS110-C10 and H²RS110-C20 with 40 × 40 LYSO arrays, a ring diameter of 110 mm and axial length of 167 mm, and (2) H²RS160-C10 and H²RS160-C20 with 60 × 60 LYSO arrays, a diameter of 160 mm and axial length of 254 mm. C10 and C20 denote the crystal thickness of 10 and 20 mm, respectively. The simulation results show that all scanners have a spatial resolution better than 0.5 mm at the center of the field-of-view (FOV). The radial resolution strongly depends on the DOI resolution and radial offset, but not the axial resolution and tangential resolution. Comparing the C10 and C20 designs, the former provides better resolution, especially at positions away from the center of the FOV, whereas the latter has 2× higher sensitivity (∼10% versus ∼20%). This simulation study provides evidence that the 110 mm systems are a good choice for total-body mouse studies at a lower cost, whereas the 160 mm systems are suited for both total-body mouse and rat studies.

Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy

This brief review summarizes the major applications of artificial intelligence (AI), in particular deep learning approaches, in molecular imaging and radiation therapy research. To this end, the applications of artificial intelligence in five generic fields of molecular imaging and radiation therapy, including PET instrumentation design, PET image reconstruction quantification and segmentation, image denoising (low-dose imaging), radiation dosimetry and computer-aided diagnosis, and outcome prediction are discussed. This review sets out to cover briefly the fundamental concepts of AI and deep learning followed by a presentation of seminal achievements and the challenges facing their adoption in clinical setting.