PET/MRI for neurologic applications

Neuroimaging in Neuro-Ophthalmology: Past, Present, and Future

BACKGROUND

Before 1895, all anatomic, pathologic, and functional understanding of the visual system was provided by postmortem studies. The advent of neuroimaging in 1895 with the development of X-ray technology enabled the living brain to be visualized, including the intraorbital and intracranial visual pathways. This has been augmented with the development of computed tomography, magnetic resonance imaging, and positron emission tomography.

EVIDENCE ACQUISITION

A literature review of the history of neuroimaging of the visual axis was completed from the time of antiquity to the present day.

RESULTS

The ability to visualize intracranial and orbital anatomy has been completely transformed. Imaging the visual axis has become faster, easier, and more precise allowing earlier diagnosis and management of a multitude of neuro-ophthalmic conditions. As we look to the future of neuroimaging, there is momentum to improve techniques enabling the assessment of microstructural architecture, metabolic and functional changes, and genetic biomarkers of disease.

CONCLUSIONS

The development of high-resolution, multiplanar neuroimaging revolutionized the ability to visualize neuro-ophthalmic anatomy and pathology. Continued research will expand our ability to integrate the metabolic, anatomic, and connectivity profiles of the visual system.

Will PET/MR Imaging Replace PET/CT for Pediatric Applications?

The combination of positron emission tomography (PET) and magnetic resonance imaging (MRI) is a modern, highly advanced diagnostic tool that offers numerous advantages in the treatment and management of some pediatric pathologies. The use of PET/MR in children provides high-resolution images with outstanding tissue characterization, as well as important metabolic and physiological information; it is not only essential for early diagnosis, but also for the assessment and management of oncological, neurological, and cardiovascular diseases. The hybrid PET/MR is a multimodal approach that reduces the need for separate examinations, minimizes radiation exposure, and improves the overall experience for pediatric patients. In addition, PET/MR, by combining functional data, allows for more precise therapeutic planning and monitoring of treatment responses, optimizing clinical interventions especially with regard to staging and follow-up. This review will explore the benefits, weaknesses, and emerging applications of PET/MR in pediatric patients.

A stochastic structural similarity guided approach for multi-modal medical image fusion

Multi-modal medical image fusion (MMIF) aims to integrate complementary information from different modalities to obtain a fused image that contains more comprehensive details, providing clinicians with a more thorough reference for diagnosis. However, most existing deep learning-based fusion methods predominantly focus on the local statistical features within images, which limits the ability of the model to capture long-range dependencies and correlations within source images, thus compromising fusion performance. To address this issue, we propose an unsupervised image fusion method guided by stochastic structural similarity (S3IMFusion). This method incorporates a multi-scale fusion network based on CNN and Transformer modules to extract complementary information from the images effectively. During the training, a loss function with the ability to interact global contextual information was designed. Specifically, a random sorting index is generated based on the source images, and pixel features are mixed and rearranged between the fused and source images according to this index. The structural similarity loss is then computed by averaging the losses between pixel blocks of the rearranged images. This ensures that the fusion result preserves the globally correlated complementary features from the source images. Experimental results on the Harvard dataset demonstrate that S3IMFusion outperforms existing methods, achieving more accurate fusion of medical images. Additionally, we extend the method to infrared and visible image fusion tasks, with results indicating that S3IMFusion exhibits excellent generalization performance.

Initial results of the Hyperion IIDPET insert for simultaneous PET-MRI applied to atherosclerotic plaque imaging in New-Zealand white rabbits

Objective.In preclinical research,in vivoimaging of mice and rats is more common than any other animal species, since their physiopathology is very well-known and many genetically altered disease models exist. Animal studies based on small rodents are usually performed using dedicated preclinical imaging systems with high spatial resolution. For studies that require animal models such as mini-pigs or New-Zealand White (NZW) rabbits, imaging systems with larger bore sizes are required. In case of hybrid imaging using positron emission tomography (PET) and magnetic resonance imaging (MRI), clinical systems have to be used, as these animal models do not typically fit in preclinical simultaneous PET-MRI scanners.Approach.In this paper, we present initial imaging results obtained with the Hyperion IIDPET insert which can accommodate NZW rabbits when combined with a large volume MRI RF coil. First, we developed a rabbit-sized image quality phantom of comparable size to a NZW rabbit in order to evaluate the PET imaging performance of the insert under high count rates. For this phantom, radioactive spheres with inner diameters between 3.95 and7.86mm were visible in a warm background with a tracer activity ratio of 4.1 to 1 and with a total18F activity in the phantom of58MBq at measurement start. Second, we performed simultaneous PET-MR imaging of atherosclerotic plaques in a rabbitin vivousing a single injection containing18F-FDG for detection of inflammatory activity, and Gd-ESMA for visualization of the aortic vessel wall and plaques with MRI.Main results.The fused PET-MR images reveal18F-FDG uptake within an active plaques with plaque thicknesses in the sub-millimeter range. Histology showed colocalization of18F-FDG uptake with macrophages in the aortic vessel wall lesions.Significance.Our initial results demonstrate that this PET insert is a promising system for simultaneous high-resolution PET-MR atherosclerotic plaque imaging studies in NZW rabbits.

PET-MRI Applications and Future Prospects in Psychiatry

This article reviews the synergistic application of positron emission tomography-magnetic resonance imaging (PET-MRI) in neuroscience with relevance for psychiatry, particularly examining neurotransmission, epigenetics, and dynamic imaging methodologies. We begin by discussing the complementary insights that PET and MRI modalities provide into neuroreceptor systems, with a focus on dopamine, opioids, and serotonin receptors, and their implications for understanding and treating psychiatric disorders. We further highlight recent PET-MRI studies using a radioligand that enables the quantification of epigenetic enzymes, specifically histone deacetylases, in the brain in vivo. Imaging epigenetics is used to exemplify the impact the quantification of novel molecular targets may have, including new treatment approaches for psychiatric disorders. Finally, we discuss innovative designs involving functional PET using [18F]FDG (fPET-FDG), which provides detailed information regarding dynamic changes in glucose metabolism. Concurrent acquisitions of fPET-FDG and functional MRI provide a time-resolved approach to studying brain function, yielding simultaneous metabolic and hemodynamic information and thereby opening new avenues for psychiatric research. Collectively, the review underscores the potential of a multimodal PET-MRI approach to advance our understanding of brain structure and function in health and disease, which could improve clinical care based on objective neurobiological features and treatment response monitoring. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

PET-Computed Tomography-MR Imaging in Central Nervous System Disorders with Cognitive and Motor Impairment

Neuroimaging, particularly positron emission tomography (PET), plays a crucial role in diagnosing and managing brain disorders by providing insights into diverse neuropathologies such as vascular issues, infections, inflammation, degenerative diseases, and tumors. In dementia, [18F]FDG-PET helps predict Alzheimer's disease (AD) development from mild cognitive impairment, revealing metabolic reductions in specific brain regions. PET's evolution with novel radiotracers and advanced imaging techniques addresses diagnostic challenges and enhances disease monitoring. Despite limitations like off-target binding, PET remains indispensable in clinical neurology, offering noninvasive insights into brain functions, disease progression, and treatment responses.

Joint diffusion: mutual consistency-driven diffusion model for PET-MRI co-reconstruction

Objective. Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. But PET suffers from a low signal-to-noise ratio, while MRI are time-consuming. To address time-consuming, an effective strategy involves reducing k-space data collection, albeit at the cost of lowering image quality. This study aims to leverage the inherent complementarity within PET-MRI data to enhance the image quality of PET-MRI.Approach. A novel PET-MRI joint reconstruction model, termed MC-Diffusion, is proposed in the Bayesian framework. The joint reconstruction problem is transformed into a joint regularization problem, where data fidelity terms of PET and MRI are expressed independently. The regular term, the derivative of the logarithm of the joint probability distribution of PET and MRI, employs a joint score-based diffusion model for learning. The diffusion model involves the forward diffusion process and the reverse diffusion process. The forward diffusion process adds noise to transform a complex joint data distribution into a known joint prior distribution for PET and MRI simultaneously, resembling a denoiser. The reverse diffusion process removes noise using a denoiser to revert the joint prior distribution to the original joint data distribution, effectively utilizing joint probability distribution to describe the correlations of PET and MRI for improved quality of joint reconstruction.Main results. Qualitative and quantitative improvements are observed with the MC-Diffusion model. Comparative analysis against LPLS and Joint ISAT-net on the ADNI dataset demonstrates superior performance by exploiting complementary information between PET and MRI. The MC-Diffusion model effectively enhances the quality of PET and MRI images.Significance. This study employs the MC-Diffusion model to enhance the quality of PET-MRI images by integrating the fundamental principles of PET and MRI modalities and leveraging their inherent complementarity. Furthermore, utilizing the diffusion model to learn the joint probability distribution of PET and MRI, thereby elucidating their latent correlation, facilitates a more profound comprehension of the priors obtained through deep learning, contrasting with black-box prior or artificially constructed structural similarities.

PET Imaging of Metabolism, Perfusion, and Hypoxia: FDG and Beyond

ABSTRACT

Imaging glucose metabolism with [18F]fluorodeoxyglucose positron emission tomography has transformed the diagnostic and treatment algorithms of numerous malignancies in clinical practice. The cancer phenotype, though, extends beyond dysregulation of this single pathway. Reprogramming of other pathways of metabolism, as well as altered perfusion and hypoxia, also typifies malignancy. These features provide other opportunities for imaging that have been developed and advanced into humans. In this review, we discuss imaging metabolism, perfusion, and hypoxia in cancer, focusing on the underlying biology to provide context. We conclude by highlighting the ability to image multiple facets of biology to better characterize cancer and guide targeted treatment.

Study on the radiofrequency transparency of partial-ring oval-shaped prototype PET inserts in a 3 T clinical MRI system

The purpose of this study is to evaluate the RF field responses of partial-ring RF-shielded oval-shaped positron emission tomography (PET) inserts that are used in combination with an MRI body RF coil. Partial-ring PET insert is particularly suitable for interventional investigation (e.g., trimodal PET/MRI/ultrasound imaging) and intraoperative (e.g., robotic surgery) PET/MRI studies. In this study, we used electrically floating Faraday RF shield cages to construct different partial-ring configurations of oval and cylindrical PET inserts and performed experiments on the RF field, spin echo and gradient echo images for a homogeneous phantom in a 3 T clinical MRI system. For each geometry, partial-ring configurations were studied by removing an opposing pair or a single shield cage from different positions of the PET ring. Compared to the MRI-only case, reduction in mean RF homogeneity, flip angle, and SNR for the detector opening in the first and third quadrants was approximately 13%, 15%, and 43%, respectively, whereas the values were 8%, 23%, and 48%, respectively, for the detector openings in the second and fourth quadrants. The RF field distribution also varied for different partial-ring configurations. It can be concluded that the field penetration was high for the detector openings in the first and third quadrants of both the inserts.

A systematic review of the challenges, emerging solutions and applications, and future directions of PET/MRI in Parkinson's disease

PET/MRI is a hybrid imaging modality that boasts the simultaneous acquisition of high-resolution anatomical data and metabolic information. Having these exceptional capabilities, it is often implicated in clinical research for diagnosing and grading, as well as tracking disease progression and response to interventions. Despite this, its low level of clinical widespread use is questioned. This is especially the case with Parkinson's disease (PD), the fastest progressively disabling and neurodegenerative cause of death. To optimise the clinical applicability of PET/MRI for diagnosing, differentiating, and tracking PD progression, the emerging novel uses, and current challenges must be identified. This systematic review aimed to present the specific challenges of PET/MRI use in PD. Further, this review aimed to highlight the possible resolution of these challenges, the emerging applications and future direction of PET/MRI use in PD. EBSCOHost (indexing CINAHL Plus, PsycINFO) Ovid (Medline, EMBASE) PubMed, Web of Science, and Scopus from 2006 (the year of first integrated PET/MRI hybrid system) to 30 September 2022 were used to search for relevant primary articles. A total of 933 studies were retrieved and following the screening procedure, 18 peer-reviewed articles were included in this review. This present study is of great clinical relevance and significance, as it informs the reasoning behind hindered widespread clinical use of PET/MRI for PD. Despite this, the emerging applications of image reconstruction developed by PET/MRI research data to the use of fully automated systems show promising and desirable utility. Furthermore, many of the current challenges and limitations can be resolved by using much larger-sampled and longitudinal studies. Meanwhile, the development of new fast-binding tracers that have specific affinity to PD pathological processes is warranted.

Evaluation of the MRI compatibility of PET detectors modules for organ-specific inserts in a 3T and 7T MRI scanner

BACKGROUND

Simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI) scanners and inserts are valuable tools for accurate diagnosis, treatment planning, and monitoring due to their complementary information. However, the integration of a PET system into an MRI scanner presents technical challenges for a distortion-free operation.

PURPOSE

We aim to develop a PET insert dedicated to breast imaging in combination with the 3T PET/MRI scanner Biograph mMR (Siemens Healthineers) as well as a brain PET insert for the 7T MRI scanner MAGNETOM Terra (Siemens Healthineers). For this development, we selected as a basis the C13500 series PET modules (Hamamatsu Photonics K.K.) as they offer an all-in-one solution with a scalable, modular design for compact integration with state-of-the-art performance. The original PET modules were not designed to be operated with an MRI scanner, therefore we implemented several modifications such as signal transmission via plastic optical fiber, radio frequency (RF) shielding of the front-end electronics, and filter for the power supply lines. In this work, we evaluated the mutual MRI compatibility between the modified PET modules and the 3T and 7T MRI scanner.

METHODS

We used a proof-of-concept setup with two detectors to comprehensively evaluate a potential distortion of the performance of the modified PET modules whilst exposing them to a variety of MR sequences up to the peak operation conditions of the Biograph mMR. A method using the periodicity of the sequences to identify distortions of the PET events in the phase of RF pulse transmission was introduced. Vice versa, the potential distortion of the Biograph mMR was evaluated by vendor proprietary MRI compatibility test sequences. Afterwards, these studies were extended to the MAGNETOM Terra.

RESULTS

No distortions were introduced by gradient field switching (field strength up to 20 mT/m at a slew rate of 66.0 T/ms-1 ). However, RF pulse transmission induced a reduction of the single event rate from 33.0 kcounts/s to 32.0 kcounts/s and a degradation of the coincidence resolution time from 251 to 299 ps. Further, the proposed method revealed artifacts in the energy and timing histograms. Finally, by using the front-end filters it was possible to prevent any RF pulse induced distortion of event rate, energy, or time stamps even for a 700° flip angle (45.5 μT) sequence. The evaluations to assess potential distortions of the MRI scanner showed that carefully designed RF shielding boxes for the PET modules were required to prevent distortion of the RF spectra. The increase in B0 field inhomogeneity of 0.254 ppm and local changes of the B1 field of 12.5% introduced by the PET modules did not qualitatively affect the MR imaging with a spin echo and MPRAGE sequence for the Biograph mMR and the MAGNETOM Terra, respectively.

CONCLUSION

Our study demonstrates the feasibility of using a modified version of the PET modules in combination with 3T and 7T MRI scanners. Building upon the encouraging MRI compatibility results from our proof-of-concept detectors, we will proceed to develop PET inserts for breast and brain imaging using these modules.

Molecular and cellular imaging of the eye

The application of molecular and cellular imaging in ophthalmology has numerous benefits. It can enable the early detection and diagnosis of ocular diseases, facilitating timely intervention and improved patient outcomes. Molecular imaging techniques can help identify disease biomarkers, monitor disease progression, and evaluate treatment responses. Furthermore, these techniques allow researchers to gain insights into the pathogenesis of ocular diseases and develop novel therapeutic strategies. Molecular and cellular imaging can also allow basic research to elucidate the normal physiological processes occurring within the eye, such as cell signaling, tissue remodeling, and immune responses. By providing detailed visualization at the molecular and cellular level, these imaging techniques contribute to a comprehensive understanding of ocular biology. Current clinically available imaging often relies on confocal microscopy, multi-photon microscopy, PET (positron emission tomography) or SPECT (single-photon emission computed tomography) techniques, optical coherence tomography (OCT), and fluorescence imaging. Preclinical research focuses on the identification of novel molecular targets for various diseases. The aim is to discover specific biomarkers or molecular pathways associated with diseases, allowing for targeted imaging and precise disease characterization. In parallel, efforts are being made to develop sophisticated and multifunctional contrast agents that can selectively bind to these identified molecular targets. These contrast agents can enhance the imaging signal and improve the sensitivity and specificity of molecular imaging by carrying various imaging labels, including radionuclides for PET or SPECT, fluorescent dyes for optical imaging, or nanoparticles for multimodal imaging. Furthermore, advancements in technology and instrumentation are being pursued to enable multimodality molecular imaging. Integrating different imaging modalities, such as PET/MRI (magnetic resonance imaging) or PET/CT (computed tomography), allows for the complementary strengths of each modality to be combined, providing comprehensive molecular and anatomical information in a single examination. Recently, photoacoustic microscopy (PAM) has been explored as a novel imaging technology for visualization of different retinal diseases. PAM is a non-invasive, non-ionizing radiation, and hybrid imaging modality that combines the optical excitation of contrast agents with ultrasound detection. It offers a unique approach to imaging by providing both anatomical and functional information. Its ability to utilize molecularly targeted contrast agents holds great promise for molecular imaging applications in ophthalmology. In this review, we will summarize the application of multimodality molecular imaging for tracking chorioretinal angiogenesis along with the migration of stem cells after subretinal transplantation in vivo.

Development and performance of SIAT bPET: a high-resolution and high-sensitivity MR-compatible brain PET scanner using dual-ended readout detectors

PURPOSE

Positron emission tomography/magnetic resonance imaging (PET/MRI) is a powerful tool for brain imaging, but the spatial resolution of the PET scanners currently used for brain imaging can be further improved to enhance the quantitative accuracy of brain PET imaging. The purpose of this study is to develop an MR-compatible brain PET scanner that can simultaneously achieve a uniform high spatial resolution and high sensitivity by using dual-ended readout depth encoding detectors.

METHODS

The MR-compatible brain PET scanner, named SIAT bPET, consists of 224 dual-ended readout detectors. Each detector contains a 26 × 26 lutetium yttrium oxyorthosilicate (LYSO) crystal array of 1.4 × 1.4 × 20 mm3 crystal size read out by two 10 × 10 silicon photomultiplier (SiPM) arrays from both ends. The scanner has a detector ring diameter of 376.8 mm and an axial field of view (FOV) of 329 mm. The performance of the scanner including spatial resolution, sensitivity, count rate, scatter fraction, and image quality was measured. Imaging studies of phantoms and the brain of a volunteer were performed. The mutual interferences of the PET insert and the uMR790 3 T MRI scanner were measured, and simultaneous PET/MRI imaging of the brain of a volunteer was performed.

RESULTS

A spatial resolution of better than 1.5 mm with an average of 1.2 mm within the whole FOV was obtained. A sensitivity of 11.0% was achieved at the center FOV for an energy window of 350-750 keV. Except for the dedicated RF coil, which caused a ~ 30% reduction of the sensitivity of the PET scanner, the MRI sequences running had a negligible effect on the performance of the PET scanner. The reduction of the SNR and homogeneity of the MRI images was less than 2% as the PET scanner was inserted to the MRI scanner and powered-on. High quality PET and MRI images of a human brain were obtained from simultaneous PET/MRI scans.

CONCLUSION

The SIAT bPET scanner achieved a spatial resolution and sensitivity better than all MR-compatible brain PET scanners developed up to date. It can be used either as a standalone brain PET scanner or a PET insert placed inside a commercial whole-body MRI scanner to perform simultaneous PET/MRI imaging.

An automatic pipeline for PET/MRI attenuation correction validation in the brain

PURPOSE

Challenges in PET/MRI quantitative accuracy for neurological uses arise from PET attenuation correction accuracy. We proposed and evaluated an automatic pipeline to assess the quantitative accuracy of four MRI-derived PET AC methods using analytically simulated PET brain lesions and ROIs as ground truth for PET activity.

METHODS

Our proposed pipeline, integrating a synthetic lesion insertion tool and the FreeSurfer neuroimaging framework, inserts simulated spherical and brain ROIs into PET projection space, reconstructing them via four PET MRAC techniques. Utilizing an 11-patient brain PET dataset, we compared the quantitative accuracy of four MRACs (DIXON, DIXONbone, UTE AC, and DL-DIXON) against the gold standard PET CTAC, evaluating MRAC to CTAC activity bias in spherical lesions and brain ROIs with and without background activity against original (lesion free) PET reconstructed images.

RESULTS

The proposed pipeline yielded accurate results for spherical lesions and brain ROIs, adhering to the MRAC to CTAC pattern of original brain PET images. Among the MRAC methods, DIXON AC exhibited the highest bias, followed by UTE, DIXONBone, and DL-DIXON showing the least. DIXON, DIXONbone, UTE, and DL-DIXON showed MRAC to CTAC biases of - 5.41%, - 1.85%, - 2.74%, and 0.08% respectively for ROIs inserted in background activity; - 7.02%, - 2.46%, - 3.56%, and - 0.05% for lesion ROIs without background; and - 6.82%, - 2.08%, - 2.29%, and 0.22% for the original brain PET images' 16 FreeSurfer brain ROIs.

CONCLUSION

The proposed pipeline delivers accurate results for synthetic spherical lesions and brain ROIs, with and without background activity consideration, enabling the evaluation of new attenuation correction approaches without utilizing measured PET emission data. Additionally, it offers a consistent method to generate realistic lesion ROIs, potentially applicable in assessing further PET correction techniques.

Generative Adversarial Network-Enhanced Ultra-Low-Dose [18F]-PI-2620 τ PET/MRI in Aging and Neurodegenerative Populations

BACKGROUND AND PURPOSE

With the utility of hybrid τ PET/MR imaging in the screening, diagnosis, and follow-up of individuals with neurodegenerative diseases, we investigated whether deep learning techniques can be used in enhancing ultra-low-dose [18F]-PI-2620 τ PET/MR images to produce diagnostic-quality images.

MATERIALS AND METHODS

Forty-four healthy aging participants and patients with neurodegenerative diseases were recruited for this study, and [18F]-PI-2620 τ PET/MR data were simultaneously acquired. A generative adversarial network was trained to enhance ultra-low-dose τ images, which were reconstructed from a random sampling of 1/20 (approximately 5% of original count level) of the original full-dose data. MR images were also used as additional input channels. Region-based analyses as well as a reader study were conducted to assess the image quality of the enhanced images compared with their full-dose counterparts.

RESULTS

The enhanced ultra-low-dose τ images showed apparent noise reduction compared with the ultra-low-dose images. The regional standard uptake value ratios showed that while, in general, there is an underestimation for both image types, especially in regions with higher uptake, when focusing on the healthy-but-amyloid-positive population (with relatively lower τ uptake), this bias was reduced in the enhanced ultra-low-dose images. The radiotracer uptake patterns in the enhanced images were read accurately compared with their full-dose counterparts.

CONCLUSIONS

The clinical readings of deep learning-enhanced ultra-low-dose τ PET images were consistent with those performed with full-dose imaging, suggesting the possibility of reducing the dose and enabling more frequent examinations for dementia monitoring.

Neuromorphic applications in medicine

In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry. To meet these demands, healthcare professionals are seeking new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which uses neural models in hardware and software to replicate brain-like behaviors, can help usher in a new era of medicine by delivering low power, low latency, small footprint, and high bandwidth solutions. This paper provides an overview of recent neuromorphic advancements in medicine, including medical imaging and cancer diagnosis, processing of biosignals for diagnosis, and biomedical interfaces, such as motor, cognitive, and perception prostheses. For each section, we provide examples of how brain-inspired models can successfully compete with conventional artificial intelligence algorithms, demonstrating the potential of neuromorphic engineering to meet demands and improve patient outcomes. Lastly, we discuss current struggles in fitting neuromorphic hardware with non-neuromorphic technologies and propose potential solutions for future bottlenecks in hardware compatibility.

Multimodal brain image fusion based on error texture elimination and salient feature detection

As an important clinically oriented information fusion technology, multimodal medical image fusion integrates useful information from different modal images into a comprehensive fused image. Nevertheless, existing methods routinely consider only energy information when fusing low-frequency or base layers, ignoring the fact that useful texture information may exist in pixels with lower energy values. Thus, erroneous textures may be introduced into the fusion results. To resolve this problem, we propose a novel multimodal brain image fusion algorithm based on error texture removal. A two-layer decomposition scheme is first implemented to generate the high- and low-frequency subbands. We propose a salient feature detection operator based on gradient difference and entropy. The proposed operator integrates the gradient difference and amount of information in the high-frequency subbands to effectively identify clearly detailed information. Subsequently, we detect the energy information of the low-frequency subband by utilizing the local phase feature of each pixel as the intensity measurement and using a random walk algorithm to detect the energy information. Finally, we propose a rolling guidance filtering iterative least-squares model to reconstruct the texture information in the low-frequency components. Through extensive experiments, we successfully demonstrate that the proposed algorithm outperforms some state-of-the-art methods. Our source code is publicly available at https://github.com/ixilai/ETEM.

An automatic pipeline for PET/MRI attenuation correction validation in the brain

Purpose

PET/MRI quantitative accuracy for neurological applications is challenging due to accuracy of the PET attenuation correction. In this work, we proposed and evaluated an automatic pipeline for assessing the quantitative accuracy of four different MRI = based attenuation correction (PET MRAC) approaches.

Methods

The proposed pipeline consists of a synthetic lesion insertion tool and the FreeSurfer neuroimaging analysis framework. The synthetic lesion insertion tool is used to insert simulated spherical, and brain regions of interest (ROI) into the PET projection space and reconstructed with four different PET MRAC techniques, while FreeSurfer is used to generate brain ROIs from T1 weighted MRI image. Using a cohort of 11 patients' brain PET dataset, the quantitative accuracy of four MRAC(s), which are: DIXON AC, DIXONbone AC, UTE AC, and Deep learning trained with DIXON AC, named DL-DIXON AC, were compared to the PET-based CT attenuation correction (PET CTAC). MRAC to CTAC activity bias in spherical lesions and brain ROIs were reconstructed with and without background activity and compared to the original PET images.

Results

The proposed pipeline provides accurate and consistent results for inserted spherical lesions and brain ROIs inserted with and without considering the background activity and following the same MRAC to CTAC pattern as the original brain PET images. As expected, the DIXON AC showed the highest bias; the second was for the UTE, then the DIXONBone, and the DL-DIXON with the lowest bias. For simulated ROIs inserted in the background activity, DIXON showed a -4.65% MRAC to CTAC bias, 0.06% for the DIXONbone, -1.70% for the UTE, and - 0.23% for the DL-DIXON. For lesion ROIs inserted without background activity, DIXON showed a -5.21%, -1% for the DIXONbone, -2.55% for the UTE, and - 0.52 for the DL-DIXON. For MRAC to CTAC bias calculated using the same 16 FreeSurfer brain ROIs in the original brain PET reconstructed images, a 6.87% was observed for the DIXON, -1.83% for DIXON bone, -3.01% for the UTE, and - 0.17% for the DL-DIXON.

Conclusion

The proposed pipeline provides accurate and consistent results for synthetic spherical lesions and brain ROIs inserted with and without considering the background activity; hence a new attenuation correction approach can be evaluated without using measured PET emission data.

PET/MR enterography in inflammatory bowel disease: A review of applications and technical considerations

Positron emission tomography (PET) magnetic resonance (MR) enterography is a novel hybrid imaging technique that is gaining popularity in the study of complex inflammatory disorders of the gastrointestinal system, such as inflammatory bowel disease (IBD). This imaging technique combines the metabolic information of PET imaging with the spatial resolution and soft tissue contrast of MR imaging. Several studies have suggested potential roles for PET/MR imaging in determining the activity status of IBD, evaluating treatment response, stratifying risk, and predicting long-term clinical outcomes. However, there are challenges in generalizing findings due to limited studies, technical aspects of hybrid MR/PET imaging, and clinical indications of this imaging modality. This review aims to further elucidate the possible role of PET/MR in IBD, highlight important technical aspects of imaging, and address potential pitfalls and prospects of this modality in IBDs.

(Dis-)Connected Dots in Dementia with Lewy Bodies-A Systematic Review of Connectivity Studies

Studies on dementia with Lewy bodies (DLB) have mainly focused on the degeneration of distinct cortical and subcortical regions related to the deposition of Lewy bodies. In view of the proposed trans-synaptic spread of the α-synuclein pathology, investigating the disease only in this segregated fashion would be detrimental to our understanding of its progression. In this systematic review, we summarize findings on structural and functional brain connectivity in DLB, as connectivity measures may offer better insights on how the brain is affected by the spread of the pathology. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched Web of Science, PubMed, and SCOPUS for relevant articles published up to November 1, 2021. Of 1215 identified records, we selected and systematically reviewed 53 articles that compared connectivity features between patients with DLB and healthy controls. Structural and functional magnetic resonance imaging, positron emission tomography, single-positron emission computer tomography, and electroencephalography assessments of patients revealed widespread abnormalities within and across brain networks in DLB. Frontoparietal, default mode, and visual networks and their connections to other brain regions featured the most consistent disruptions, which were also associated with core clinical features and cognitive impairments. Furthermore, graph theoretical measures revealed disease-related decreases in local and global network efficiency. This systematic review shows that structural and functional connectivity characteristics in DLB may be particularly valuable at early stages, before overt brain atrophy can be observed. This knowledge may help improve the diagnosis and prognosis in DLB as well as pinpoint targets for future disease-modifying treatments. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Brain imaging of sequential acquisition using a flexible PET scanner and 3-T MRI: quantitative and qualitative assessment

OBJECTIVE

A mobile PET scanner termed flexible PET (fxPET) has been designed to fit existing MRI systems. The purpose of this study was to assess brain imaging with fxPET combined with 3-T MRI in comparison with conventional PET (cPET)/CT.

METHODS

In this prospective study, 29 subjects with no visible lesions except for mild leukoaraiosis on whole brain imaging underwent 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) cPET/CT followed by fxPET and MRI. The registration differences between fxPET and MRI and between cPET and CT were compared by measuring spatial coordinates. Three-dimensional magnetization-prepared rapid acquisition gradient-echo T1-weighted imaging (T1WI) was acquired. We applied two methods of attenuation correction to the fxPET images: MR-based attenuation correction, which yielded fxPET_MRAC; and CT-based attenuation correction, which yielded fxPET_CTAC. The three PET datasets were co-registered to the T1WI. Following subcortical segmentation and cortical parcellation, volumes of interest were placed in each PET image to assess physiological accumulation in the brain. SUV_mean was obtained and compared between the three datasets. We also visually evaluated image distortion and clarity of fxPET_MRAC.

RESULTS

Mean misregistration of fxPET/MRI was < 3 mm for each margin. Mean registration differences were significantly larger for fxPET/MRI than for cPET/CT except for the superior margin. There were high correlations between the three PET datasets regarding SUV_mean. On visual evaluation of image quality, the grade of distortion was comparable between fxPET_MRAC and cPET, and the grade of clarity was acceptable but inferior for fxPET_MRAC compared with cPET.

CONCLUSIONS

fxPET could successfully determine physiological [¹⁸F]FDG uptake; however, improved image clarity is desirable. In this study, fxPET/MRI at 3-T was feasible for brain imaging.

Principles of Lung Cancer Metastasis to Brain

Lung cancer is a disease associated with significant morbidity and mortality on a global setting. This form of cancer commonly gives raise to metastatic lesions the brain, which can further worsen outcomes. In this focused review, we discuss an overview of lung cancers that metastasize to the brain: known risk factors; means of detection and diagnosis; and options for treatment including a comparison between surgical resection, stereotactic radiosurgery, and whole-brain radiation therapy. These interventions are still being assessed by clinical trials and continue to be modified through evidence-based practice.

MATR: Multimodal Medical Image Fusion via Multiscale Adaptive Transformer

Owing to the limitations of imaging sensors, it is challenging to obtain a medical image that simultaneously contains functional metabolic information and structural tissue details. Multimodal medical image fusion, an effective way to merge the complementary information in different modalities, has become a significant technique to facilitate clinical diagnosis and surgical navigation. With powerful feature representation ability, deep learning (DL)-based methods have improved such fusion results but still have not achieved satisfactory performance. Specifically, existing DL-based methods generally depend on convolutional operations, which can well extract local patterns but have limited capability in preserving global context information. To compensate for this defect and achieve accurate fusion, we propose a novel unsupervised method to fuse multimodal medical images via a multiscale adaptive Transformer termed MATR. In the proposed method, instead of directly employing vanilla convolution, we introduce an adaptive convolution for adaptively modulating the convolutional kernel based on the global complementary context. To further model long-range dependencies, an adaptive Transformer is employed to enhance the global semantic extraction capability. Our network architecture is designed in a multiscale fashion so that useful multimodal information can be adequately acquired from the perspective of different scales. Moreover, an objective function composed of a structural loss and a region mutual information loss is devised to construct constraints for information preservation at both the structural-level and the feature-level. Extensive experiments on a mainstream database demonstrate that the proposed method outperforms other representative and state-of-the-art methods in terms of both visual quality and quantitative evaluation. We also extend the proposed method to address other biomedical image fusion issues, and the pleasing fusion results illustrate that MATR has good generalization capability. The code of the proposed method is available at https://github.com/tthinking/MATR.

Update on the Use of PET/MRI Contrast Agents and Tracers in Brain Oncology: A Systematic Review

The recent advancements in hybrid positron emission tomography–magnetic resonance imaging systems (PET/MRI) have brought massive value in the investigation of disease processes, in the development of novel treatments, in the monitoring of both therapy response and disease progression, and, not least, in the introduction of new multidisciplinary molecular imaging approaches. While offering potential advantages over PET/CT, the hybrid PET/MRI proved to improve both the image quality and lesion detectability. In particular, it showed to be an effective tool for the study of metabolic information about lesions and pathological conditions affecting the brain, from a better tumor characterization to the analysis of metabolic brain networks. Based on the PRISMA guidelines, this work presents a systematic review on PET/MRI in basic research and clinical differential diagnosis on brain oncology and neurodegenerative disorders. The analysis includes literature works and clinical case studies, with a specific focus on the use of PET tracers and MRI contrast agents, which are usually employed to perform hybrid PET/MRI studies of brain tumors. A systematic literature search for original diagnostic studies is performed using PubMed/MEDLINE, Scopus and Web of Science. Patients, study, and imaging characteristics were extracted from the selected articles. The analysis included acquired data pooling, heterogeneity testing, sensitivity analyses, used tracers, and reported patient outcomes. Our analysis shows that, while PET/MRI for the brain is a promising diagnostic method for early diagnosis, staging and recurrence in patients with brain diseases, a better definition of the role of tracers and imaging agents in both clinical and preclinical hybrid PET/MRI applications is needed and further efforts should be devoted to the standardization of the contrast imaging protocols, also considering the emerging agents and multimodal probes.

Neuroimaging Modalities in Alzheimer's Disease: Diagnosis and Clinical Features

Alzheimer's disease (AD) is a neurodegenerative disease causing progressive cognitive decline until eventual death. AD affects millions of individuals worldwide in the absence of effective treatment options, and its clinical causes are still uncertain. The onset of dementia symptoms indicates severe neurodegeneration has already taken place. Therefore, AD diagnosis at an early stage is essential as it results in more effective therapy to slow its progression. The current clinical diagnosis of AD relies on mental examinations and brain imaging to determine whether patients meet diagnostic criteria, and biomedical research focuses on finding associated biomarkers by using neuroimaging techniques. Multiple clinical brain imaging modalities emerged as potential techniques to study AD, showing a range of capacity in their preciseness to identify the disease. This review presents the advantages and limitations of brain imaging modalities for AD diagnosis and discusses their clinical value.

Study on the RF transparency of electrically floating and ground PET inserts in a 3T clinical MRI system

PURPOSE

The positron emission tomography (PET) insert for a magnetic resonance imaging (MRI) system that implements the radiofrequency (RF) built-in body coil of the MRI system as a transmitter is designed to be RF-transparent, as the coil resides outside the RF-shielded PET ring. This approach reduces the design complexities (e.g., large PET ring diameter) related to implementing a transmit coil inside the PET ring. However, achieving the required field transmission into the imaging region of interest (ROI) becomes challenging because of the RF shield of the PET insert. In this study, a modularly RF-shielded PET insert is used to investigate the RF transparency considering two electrical configurations of the RF shield, namely the electrical floating and ground configurations. The purpose is to find the differences, advantages and disadvantages of these two configurations.

METHODS

Eight copper-shielded PET detector modules (intermodular gap: 3 mm) were oriented cylindrically with an inner-diameter of 234 mm. Each PET module included four-layer LYSO scintillation crystal blocks and front-end readout electronics. RF-shielded twisted-pair cables were used to connect the front-end electronics with the power sources and PET data acquisition systems located outside the MRI room. In the ground configuration, both the detector and cable shields were connected to the RF ground of the MRI system. In the floating configuration, only the RF shields of the PET modules were isolated from the RF ground. Experiments were conducted using two cylindrical homogeneous phantoms in a 3T clinical MRI system, in which the built-in body RF coil (a cylindrical volume coil of diameter 700 mm and length 540 mm) was implemented as a transceiver.

RESULTS

For both PET configurations, the RF and MR imaging performances were lower than those for the MRI-only case, and the MRI-system provided SAR values that were almost double. The RF homogeneity and field strength, and the SNR of the MR images were mostly higher for the floating PET configuration than they were for the ground PET configuration. However, for a shorter axial FOV of 125 mm, both configurations offered almost the same performance with high RF homogeneities (e.g., 76 ± 10%). Moreover, for both PET configurations, 56 ± 6% larger RF pulse amplitudes were required for MR imaging purposes. The increased power is mostly absorbed in the conductive shields in the form of shielding RF eddy currents; as a result, the SAR values only in the phantoms were estimated to be close to the MRI-only values.

CONCLUSIONS

The floating PET configuration showed higher RF transparency under all experimental setups. For a relatively short axial FOV of 125 mm, the ground configuration also performed well which indicated that an RF-penetrable PET insert with the conventional design (e.g., the ground configuration) might also become possible. However, some design modifications (e.g., a wider intermodular gap and using the RF receiver coil inside the PET insert) should improve the RF performance to the level of the MRI-only case. This article is protected by copyright. All rights reserved.

Evaluating different methods of MR-based motion correction in simultaneous PET/MR using a head phantom moved by a robotic system

Background

Due to comparatively long measurement times in simultaneous positron emission tomography and magnetic resonance (PET/MR) imaging, patient movement during the measurement can be challenging. This leads to artifacts which have a negative impact on the visual assessment and quantitative validity of the image data and, in the worst case, can lead to misinterpretations. Simultaneous PET/MR systems allow the MR-based registration of movements and enable correction of the PET data. To assess the effectiveness of motion correction methods, it is necessary to carry out measurements on phantoms that are moved in a reproducible way. This study explores the possibility of using such a phantom-based setup to evaluate motion correction strategies in PET/MR of the human head.

Method

An MR-compatible robotic system was used to generate rigid movements of a head-like phantom. Different tools, either from the manufacturer or open-source software, were used to estimate and correct for motion based on the PET data itself (SIRF with SPM and NiftyReg) and MR data acquired simultaneously (e.g. MCLFIRT, BrainCompass). Different motion estimates were compared using data acquired during robot-induced motion. The effectiveness of motion correction of PET data was evaluated by determining the segmented volume of an activity-filled flask inside the phantom. In addition, the segmented volume was used to determine the centre-of-mass and the change in maximum activity concentration.

Results

The results showed a volume increase between 2.7 and 36.3% could be induced by the experimental setup depending on the motion pattern. Both, BrainCompass and MCFLIRT, produced corrected PET images, by reducing the volume increase to 0.7–4.7% (BrainCompass) and to -2.8–0.4% (MCFLIRT). The same was observed for example for the centre-of-mass, where the results show that MCFLIRT (0.2–0.6 mm after motion correction) had a smaller deviation from the reference position than BrainCompass (0.5–1.8 mm) for all displacements.

Conclusions

The experimental setup is suitable for the reproducible generation of movement patterns. Using open-source software for motion correction is a viable alternative to the vendor-provided motion-correction software.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-022-00442-6.

Investigating Simultaneity for Deep Learning-Enhanced Actual Ultra-Low-Dose Amyloid PET/MR Imaging

BACKGROUND AND PURPOSE

Diagnostic-quality amyloid PET images can be created with deep learning using actual ultra-low-dose PET images and simultaneous structural MR imaging. Here, we investigated whether simultaneity is required; if not, MR imaging-assisted ultra-low-dose PET imaging could be performed with separate PET/CT and MR imaging acquisitions.

MATERIALS AND METHODS

We recruited 48 participants: Thirty-two (20 women; mean, 67.7 [SD, 7.9] years) were used for pretraining; 328 (SD, 32) MBq of [¹⁸F] florbetaben was injected. Sixteen participants (6 women; mean, 71.4 [SD. 8.7] years of age) were scanned in 2 sessions, with 6.5 (SD, 3.8) and 300 (SD, 14) MBq of [¹⁸F] florbetaben injected, respectively. Structural MR imaging was acquired simultaneously with PET (90-110 minutes postinjection) on integrated PET/MR imaging in 2 sessions. Multiple U-Net-based deep networks were trained to create diagnostic PET images. For each method, training was done with the ultra-low-dose PET as input combined with MR imaging from either the ultra-low-dose session (simultaneous) or from the standard-dose PET session (nonsimultaneous). Image quality of the enhanced and ultra-low-dose PET images was evaluated using quantitative signal-processing methods, standardized uptake value ratio correlation, and clinical reads.

RESULTS

Qualitatively, the enhanced images resembled the standard-dose image for both simultaneous and nonsimultaneous conditions. Three quantitative metrics showed significant improvement for all networks and no differences due to simultaneity. Standardized uptake value ratio correlation was high across different image types and network training methods, and 31/32 enhanced image pairs were read similarly.

CONCLUSIONS

This work suggests that accurate amyloid PET images can be generated using enhanced ultra-low-dose PET and either nonsimultaneous or simultaneous MR imaging, broadening the utility of ultra-low-dose amyloid PET imaging.

A survey of brain network analysis by electroencephalographic signals

Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

mGluR 5 and GABA A receptor‐specific parametric PET atlas construction— PET / MR data processing pipeline, validation, and application

The glutamate and γ‐aminobutyric acid neuroreceptor subtypes mGluR₅ and GABA_A are hypothesized to be involved in the development of a variety of psychiatric diseases. However, detailed information relating to their in vivo distribution is generally unavailable. Maps of such distributions could potentially aid clinical studies by providing a reference for the normal distribution of neuroreceptors and may also be useful as covariates in advanced functional magnetic resonance imaging (MR) studies. In this study, we propose a comprehensive processing pipeline for the construction of standard space, in vivo distributions of non‐displaceable binding potential (BP_ND), and total distribution volume (V_T) based on simultaneously acquired bolus‐infusion positron emission tomography (PET) and MR data. The pipeline was applied to [¹¹C]ABP688‐PET/MR (13 healthy male non‐smokers, 26.6 ± 7.0 years) and [¹¹C]Flumazenil‐PET/MR (10 healthy males, 25.8 ± 3.0 years) data. Activity concentration templates, as well as V_T and BP_ND atlases of mGluR₅ and GABA_A, were generated from these data. The maps were validated by assessing the percent error δ from warped space to native space in a selection of brain regions. We verified that the average δ_ABP = 3.0 ± 1.0% and δ_FMZ = 3.8 ± 1.4% were lower than the expected variabilities σ of the tracers (σ_ABP = 4.0%–16.0%, σ_FMZ = 3.9%–9.5%). An evaluation of PET‐to‐PET registrations based on the new maps showed higher registration accuracy compared to registrations based on the commonly used [¹⁵O]H₂O‐template distributed with SPM12. Thus, we conclude that the resulting maps can be used for further research and the proposed pipeline is a viable tool for the construction of standardized PET data distributions.

Fabrication and evaluation of bilateral Helmholtz radiofrequency coil for thermo-stable breast image with reduced artifacts

PURPOSE

The positron emission tomography (PET)-magnetic resonance (MR) system is a newly emerging technique that yields hybrid images with high-resolution anatomical and metabolic information. With PET-MR imaging, a definitive diagnosis of breast abnormalities will be possible with high spatial accuracy and images will be acquired for the optimal fusion of anatomic locations. Therefore, we propose a PET-compatible two-channel breast MR coil with minimal disturbance to image acquisition which can be used for simultaneous PET-MR imaging in patients with breast cancer.

MATERIALS AND METHODS

For coil design and construction, the conductor loops of the Helmholtz coil were tuned, matched, and subdivided with nonmagnetic components. Element values were optimized with an electromagnetic field simulation. Images were acquired on a GE 600 PET-computed tomography (CT) and GE 3.0 T MR system. For this study, we used the T1-weighted image (volunteer; repetition time (TR), 694 ms; echo time (TE), 9.6 ms) and T2-weighted image (phantom; TR, 8742 ms; TE, 104 ms) with the fast spin-echo sequence.

RESULTS

The results of measuring image factors with the proposed radiofrequency (RF) coil and standard conventional RF coil were as follows: signal-to-noise ratio (breast; 207.7 vs. 175.2), percent image uniformity (phantom; 89.22%-91.27% vs. 94.63%-94.77%), and Hounsfield units (phantom; -4.51 vs. 2.38).

CONCLUSIONS

Our study focused on the feasibility of proposed two-channel Helmholtz loops (by minimizing metallic components and soldering) for PET-MR imaging and found the comparable image quality to the standard conventional coil. We believe our work will help significantly to improve image quality with the development of a less metallic breast MR coil.

Integrated Positron Emission Tomography/Magnetic Resonance Imaging in clinical diagnosis of Alzheimer's disease

Alzheimer's disease (AD), a progressive neurodegenerative disease which seriously endangers the health of the aged, is the most common etiology of senile dementia. With the increasing progress of neuroimaging technology, more and more imaging methods have been applied to study Alzheimer's disease. The emergence of integrated PET/MRI (Positron Emission Tomography/Magnetic Resonance Imaging) is a major advance in multimodal molecular imaging with many advantages on the structure of resolution and contrast of image over computed tomography (CT), PET and MRI. PET/MRI is now used stepwise in neurodegenerative diseases, and also has broad prospect of application in the early diagnosis of AD. In this review, we emphatically introduce the imaging advances of AD including functional imaging and molecular imaging, the advantages of PET/MRI over other imaging methods and prospects of PET/MRI in AD clinical diagnosis, especially in early diagnosis, clinical assessment and prediction on AD.

The use of positron emission tomography/magnetic resonance imaging in dementia: A literature review

OBJECTIVES

Positron emission tomography-magnetic resonance imaging (PET/MRI) is an emerging hybrid imaging system in clinical nuclear medicine. Research demonstrates a comparative utility to current unimodal and hybrid methods, including PET-computed tomography (PET/CT), in several medical subspecialities such as neuroimaging. The aim of this review is to critically evaluate the literature from 2016 to 2021 using PET/MRI for the investigation of patients with mild cognitive impairment or dementia, and discuss the evidence base for widening its application into clinical practice.

METHODS

A comprehensive literature search using the PubMed database was conducted to retrieve studies using PET/MRI in relation to the topics of mild cognitive impairment, dementia, or Alzheimer's disease between January 2016 and January 2021. This search strategy enabled studies on all dementia types to be included in the analysis. Studies were required to have a minimum of 10 human subjects and incorporate simultaneous PET/MRI.

RESULTS

A total of 116 papers were retrieved, with 39 papers included in the final selection. These were broadly categorised into reviews (12), technical/methodological papers (11) and new data studies (16). For the current review, discussion focused on findings from the new data studies.

CONCLUSIONS

PET/MRI offers additional insight into the underlying anatomical, metabolic and functional changes associated with dementia when compared with unimodal methods and PET/CT, particularly relating to brain regions including the hippocampus and default mode network. Furthermore, the improved diagnostic utility of PET/MRI, as reported by radiologists, offers improved classification of dementia patients, with important implications for clinical management.

Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging

Subject motion in positron emission tomography (PET) is a key factor that degrades image resolution and quality, limiting its potential capabilities. Correcting for it is complicated due to the lack of sufficient measured PET data from each position. This poses a significant barrier in calculating the amount of motion occurring during a scan. Motion correction can be implemented at different stages of data processing either during or after image reconstruction, and once applied accurately can substantially improve image quality and information accuracy. With the development of integrated PET-MRI (magnetic resonance imaging) scanners, internal organ motion can be measured concurrently with both PET and MRI. In this review paper, we explore the synergistic use of PET and MRI data to correct for any motion that affects the PET images. Different types of motion that can occur during PET-MRI acquisitions are presented and the associated motion detection, estimation and correction methods are reviewed. Finally, some highlights from recent literature in selected human and animal imaging applications are presented and the importance of motion correction for accurate kinetic modelling in dynamic PET-MRI is emphasized. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

Validation of the estimation of the macrovascular contribution in multi-timepoint arterial spin labeling MRI using a 2-component kinetic model

PURPOSE

In this paper, the ability to quantify cerebral blood flow by arterial spin labeling (ASL) was studied by investigating the separation of the macrovascular and tissue component using a 2-component model. Underlying assumptions of this model, especially the inclusion of dispersion in the analysis, were studied, as well as the temporal resolution of the ASL datasets.

METHODS

Four different datasets were acquired: (1) 4D ASL angiography to characterize the macrovascular component and to study dispersion modeling within this component, (2) high temporal resolution ASL data to investigate the separation of the 2 components and the effect of dispersion modelling on this separation, (3) low temporal resolution ASL dataset to study the effect of the temporal resolution on the separation of the 2 components, and (4) low temporal resolution ASL data with vascular crushing.

RESULTS

The model that included a gamma dispersion kernel had the best fit to the 4D ASL angiography. For the high temporal resolution ASL dataset, inclusion of the gamma dispersion kernel led to more signal included in the arterial blood volume map, which resulted in decreased cerebral blood flow values. The arterial blood volume and cerebral blood flow maps showed overall higher arterial blood volume values and lower cerebral blood flow values for the high temporal resolution dataset compared to the low temporal resolution dataset.

CONCLUSION

Inclusion of a gamma dispersion kernel resulted in better fitting of the model to the data. The separation of the macrovascular and tissue component is affected by the inclusion of a gamma dispersion kernel and the temporal resolution of the ASL dataset.

SimPET: a Preclinical PET Insert for Simultaneous PET/MR Imaging

PURPOSE

SimPET/M7 system is a small-animal dedicated simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI) scanner. The SimPET insert has been upgraded from its prototype with a focus on count rate performance and sensitivity. The M7 scanner is a 1-T permanent magnet-based compact MRI system without any cryogens. Here, we present performance evaluation results of SimPET along with the results of mutual interference evaluation and simultaneously acquired PET/MR imaging.

PROCEDURES

Following NEMA NU 4-2008 standard, we evaluated the performance of the SimPET system. The M7 MRI compatibility of SimPET was also assessed by analyzing MRI images of a uniform phantom under different PET conditions and PET count rates with different MRI pulse sequences. Mouse imaging was performed including a whole-body ¹⁸F-NaF PET scan and a simultaneous PET/MRI scan with ⁶⁴Cu-NOTA-ironoxide.

RESULTS

The spatial resolution at center based on 3D OSEM without and with warm background was 0.7 mm and 1.45 mm, respectively. Peak sensitivity was 4.21 % (energy window = 250-750 keV). The peak noise equivalent count rate with the same energy window was 151 kcps at 38.4 MBq. The uniformity was 4.42 %, and the spillover ratios in water- and air-filled chambers were 14.6 % and 12.7 %, respectively. In the hot rod phantom image, 0.75-mm-diameter rods were distinguishable. There were no remarkable differences in the SNR and uniformity of MRI images and PET count rates with different PET conditions and MRI pulse sequences. In the whole-body ¹⁸F-NaF PET images, fine skeletal structures were well resolved. In the simultaneous PET/MRI study with ⁶⁴Cu-NOTA-ironoxide, both PET and MRI signals changed before and after injection of the dual-modal imaging probe, which was evident with the exact spatiotemporal correlation.

CONCLUSIONS

We demonstrated that the SimPET scanner has a high count rate performance and excellent spatial resolution. The combined SimPET/M7 enabled simultaneous PET/MR imaging studies with no remarkable mutual interference between the two imaging modalities.

True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation

PURPOSE

While sampled or short-frame realizations have shown the potential power of deep learning to reduce radiation dose for PET images, evidence in true injected ultra-low-dose cases is lacking. Therefore, we evaluated deep learning enhancement using a significantly reduced injected radiotracer protocol for amyloid PET/MRI.

METHODS

Eighteen participants underwent two separate ¹⁸F-florbetaben PET/MRI studies in which an ultra-low-dose (6.64 ± 3.57 MBq, 2.2 ± 1.3% of standard) or a standard-dose (300 ± 14 MBq) was injected. The PET counts from the standard-dose list-mode data were also undersampled to approximate an ultra-low-dose session. A pre-trained convolutional neural network was fine-tuned using MR images and either the injected or sampled ultra-low-dose PET as inputs. Image quality of the enhanced images was evaluated using three metrics (peak signal-to-noise ratio, structural similarity, and root mean square error), as well as the coefficient of variation (CV) for regional standard uptake value ratios (SUVRs). Mean cerebral uptake was correlated across image types to assess the validity of the sampled realizations. To judge clinical performance, four trained readers scored image quality on a five-point scale (using 15% non-inferiority limits for proportion of studies rated 3 or better) and classified cases into amyloid-positive and negative studies.

RESULTS

The deep learning-enhanced PET images showed marked improvement on all quality metrics compared with the low-dose images as well as having generally similar regional CVs as the standard-dose. All enhanced images were non-inferior to their standard-dose counterparts. Accuracy for amyloid status was high (97.2% and 91.7% for images enhanced from injected and sampled ultra-low-dose data, respectively) which was similar to intra-reader reproducibility of standard-dose images (98.6%).

CONCLUSION

Deep learning methods can synthesize diagnostic-quality PET images from ultra-low injected dose simultaneous PET/MRI data, demonstrating the general validity of sampled realizations and the potential to reduce dose significantly for amyloid imaging.

Early Detection of Alzheimer's Disease Based on Clinical Trials, Three-Dimensional Imaging Data, and Personal Information Using Autoencoders

Background:

A timely diagnosis of Alzheimer's disease (AD) is crucial to obtain more practical treatments. In this article, a novel approach using Auto-Encoder Neural Networks (AENN) for early detection of AD was proposed.

Method:

The proposed method mainly deals with the classification of multimodal data and the imputation of missing data. The data under study involve the MiniMental State Examination, magnetic resonance imaging, positron emission tomography, cerebrospinal fluid data, and personal information. Natural logarithm was used for normalizing the data. The Auto-Encoder Neural Networks was used for imputing missing data. Principal component analysis algorithm was used for reducing dimensionality of data. Support Vector Machine (SVM) was used as classifier. The proposed method was evaluated using Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Then, 10fold crossvalidation was used to audit the detection accuracy of the method.

Results:

The effectiveness of the proposed approach was studied under several scenarios considering 705 cases of ADNI database. In three binary classification problems, that is AD vs. normal controls (NCs), mild cognitive impairment (MCI) vs. NC, and MCI vs. AD, we obtained the accuracies of 95.57%, 83.01%, and 78.67%, respectively.

Conclusion:

Experimental results revealed that the proposed method significantly outperformed most of the stateoftheart methods.

A pain-induced tonic hypodopaminergic state augments phasic dopamine release in the nucleus accumbens

Diseases and disorders such as Parkinson disease, schizophrenia, and chronic pain are characterized by altered mesolimbic dopaminergic neurotransmission. Dopamine release in the nucleus accumbens influences behavior through both tonic and phasic signaling. Tonic dopamine levels are hypothesized to inversely regulate phasic signals through dopamine D2 receptor feedback inhibition. We tested this hypothesis directly in the context of ongoing pain. Tonic and phasic dopamine signals were measured using fast-scan controlled-adsorption voltammetry and fast-scan cyclic voltammetry, respectively, in the nucleus accumbens shell of male rats with standardized levels of anesthesia. Application of capsaicin to the cornea produced a transient decrease in tonic dopamine levels. During the pain-induced hypodopaminergic state, electrically evoked phasic dopamine release was significantly increased when compared to baseline, evoked phasic release. A second application of capsaicin to the same eye had a lessened effect on tonic dopamine suggesting desensitization of TRPV1 channels in that eye. Capsaicin treatment in the alternate cornea, however, again produced coincident decreased dopaminergic tone and increased phasic dopamine release. These findings occurred independently of stimulus lateralization relative to the hemisphere of dopamine measurement. Our data show that (1) the mesolimbic dopamine circuit reliably encodes acute noxious stimuli; (2) ongoing pain produces decreases in dopaminergic tone; and (3) pain-induced decreases in tonic dopamine correspond to augmented evoked phasic dopamine release. Enhanced phasic dopamine neurotransmission resulting from salient stimuli may contribute to increased impulsivity and cognitive deficits often observed in conditions associated with decreased dopaminergic tone, including Parkinson disease and chronic pain.

The inappropriateness of brain MRI prescriptions: a study from Iran

Background

Inappropriate prescriptions can lead to adverse consequences for patients. It also imposes excessive cost on the patients, payers and health systems. The current study aimed at estimating the rate of inappropriate brain Magnetic Resonance Imaging (MRI) prescriptions and their financial burden in Iran.

Methods

Using systematic stratified sampling method, this cross-sectional study recruited 385 participants from three public teaching hospitals in Shiraz, Iran. Demographic information, questions related to brain MRI prescription and its indications checklist were collected using study-specific data collection tools. The completed indications checklist was compared to the appropriateness status table of indications and scenarios to detect the percent of the appropriateness of prescriptions.

Results

About 21 percentage of total brain MRI prescriptions are inappropriate. Previous treatment, number of referrals to physician, having other diagnostic tests and the applicant of MRI (P < 0.01) had significant relationships with prescription appropriateness. The estimated financial burden of inappropriate brain MRIs in Shiraz teaching hospitals was 99,988 US dollar in 2017.

Conclusions

More than one-fifth of brains MRIs were inappropriate (i.e. prescriptions without medical indications). It caused 99,988 United States Dollar (USD) financial burden which is 17 times that of Iran's Gross Domestic Product (GDP) per capita. To better allocate resources for the provision of MRI services to health system, rationing policies for controlling moral hazard and reducing provider induced demand can be helpful.

Quantitative and qualitative evaluation of sequential PET/MRI using a newly developed mobile PET system for brain imaging

PURPOSE

To evaluate the clinical feasibility of a newly developed mobile PET system with MR-compatibility (flexible PET; fxPET), compared with conventional PET (cPET)/CT for brain imaging.

METHODS

Twenty-one patients underwent cPET/CT with subsequent fxPET/MRI using ¹⁸F-FDG. As qualitative evaluation, we visually rated image quality of MR and PET images using a four-point scoring system. We evaluated overall image quality for MR, while we evaluated overall image quality, sharpness and lesion contrast. As quantitative evaluation, we compared registration accuracy between two modalities [(fxPET and MRI) and (cPET and CT)] measuring spatial coordinates. We also examined the accuracy of regional ¹⁸F-FDG uptake.

RESULTS

All acquired images were of diagnostic quality and the number of detected lesions did not differ significantly between fxPET/MR and cPET/CT. Mean misregistration was significantly larger with fxPET/MRI than with cPET/CT. SUVmax and SUVmean for fxPET and cPET showed high correlations in the lesions (R = 0.84, 0.79; P < 0.001, P = 0.002, respectively). In normal structures, we also showed high correlations of SUVmax (R = 0.85, 0.87; P < 0.001, P < 0.001, respectively) and SUVmean (R = 0.83, 0.87; P < 0.001, P < 0.001, respectively) in bilateral caudate nuclei and a moderate correlation of SUVmax (R = 0.65) and SUVmean (R = 0.63) in vermis.

CONCLUSIONS

The fxPET/MRI system showed image quality within the diagnostic range, registration accuracy below 3 mm and regional ¹⁸F-FDG uptake highly correlated with that of cPET/CT.

A Role of PET/MR Imaging in Dementia?

Since many years, magnetic resonance imaging (MRI) and positron emission tomography (PET) have a prominent role in neurodegenerative disorders and dementia, not only in a research setting but also in a clinical setting. For several decades, information from both modalities is combined ranging from individual visual assessments to fully integrating all images. Several tools are available to coregister images from MRI and PET and to covisualize these images. When studying neurodegenerative disorders with PET it is important to perform a partial volume correction and this can be done using the structural information obtained by MRI. With the advent of PET/MR, the question arises in how far this hybrid imaging modality is an added value compared to combining PET and MRI data from two separate modalities. One issue in PET/MR is still not yet completely settled, that is, the attenuation correction. This is of less importance for visual assessments but it can become an issue when combining data from PET/CT and PET/MR scanners in multicenter studies or when using cut-off values to classify patients. Simultaneous imaging has clearly some advantages: for the patient it is beneficial to have only one scan session instead of two but also in cases in which PET data are related to functional of physiological data acquired with MRI (such as functional MRI or arterial spin labeling). However, the most important benefit is currently the more integrated use of PET and MRI. This is also possible with separate measurements but requires more streamlining of the whole process. In that case coregistration of images is mandatory. It needs to be determined in which cases simultaneous PET/MRI leads to new insights or improved diagnosis compared to multimodal imaging using dedicated scanners.

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation

Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.

Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning

PURPOSE

We aimed to evaluate the performance of deep learning-based generalization of ultra-low-count amyloid PET/MRI enhancement when applied to studies acquired with different scanning hardware and protocols.

METHODS

Eighty simultaneous [18F]florbetaben PET/MRI studies were acquired, split equally between two sites (site 1: Signa PET/MRI, GE Healthcare, 39 participants, 67 ± 8 years, 23 females; site 2: mMR, Siemens Healthineers, 64 ± 11 years, 23 females) with different MRI protocols. Twenty minutes of list-mode PET data (90-110 min post-injection) were reconstructed as ground-truth. Ultra-low-count data obtained from undersampling by a factor of 100 (site 1) or the first minute of PET acquisition (site 2) were reconstructed for ultra-low-dose/ultra-short-time (1% dose and 5% time, respectively) PET images. A deep convolution neural network was pre-trained with site 1 data and either (A) directly applied or (B) trained further on site 2 data using transfer learning. Networks were also trained from scratch based on (C) site 2 data or (D) all data. Certified physicians determined amyloid uptake (+/-) status for accuracy and scored the image quality. The peak signal-to-noise ratio, structural similarity, and root-mean-squared error were calculated between images and their ground-truth counterparts. Mean regional standardized uptake value ratios (SUVR, reference region: cerebellar cortex) from 37 successful site 2 FreeSurfer segmentations were analyzed.

RESULTS

All network-synthesized images had reduced noise than their ultra-low-count reconstructions. Quantitatively, image metrics improved the most using method B, where SUVRs had the least variability from the ground-truth and the highest effect size to differentiate between positive and negative images. Method A images had lower accuracy and image quality than other methods; images synthesized from methods B-D scored similarly or better than the ground-truth images.

CONCLUSIONS

Deep learning can successfully produce diagnostic amyloid PET images from short frame reconstructions. Data bias should be considered when applying pre-trained deep ultra-low-count amyloid PET/MRI networks for generalization.

Potentials and caveats of AI in hybrid imaging

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research.

Utility of hybrid PET/MRI multiparametric imaging in navigating SEEG placement in refractory epilepsy

PURPOSE

Stereo-electroencephalography (SEEG) implantation before epilepsy surgery is critical for precise localization and complete resection of the seizure onset zone (SOZ). Combined metabolic and morphological imaging using hybrid PET/MRI may provide supportive information for the optimization of the SEEG coverage of brain structures. In this study, we originally imported PET/MRI images into the SEEG positioning system to evaluate the application of PET/MRI in guiding SEEG implantation in refractory epilepsy patients.

MATERIALS

Forty-two patients undergoing simultaneous PET/MRI examinations were recruited. All the patients underwent SEEG implantation guided by hybrid PET/MRI and surgical resection or ablation of epileptic lesion. Surgery outcome was assessed using a modified Engel classification one year (13.60 ± 2.49 months) after surgery. Areas of SOZ were identified using hybrid PET/MRI and concordance with SEEG was evaluated. Logistic regression analysis was used to predict the presence of a favorable outcome with the coherence of concordance of PET/MRI and SEEG.

RESULTS

Hybrid PET/MRI (including visual PET, MRI, plus MI Neuro) identified SOZ lesions in 38 epilepsy patients (90.47 %). PET/MRI showed the same SOZ localization with SEEG in 29 patients (69.05 %), which was considered to be concordant. The concordance between the PET/MRI and SEEG findings was significantly predictive of a successful surgery outcome (odds ratio = 20.41; 95 % CI = 2.75-151.4, P = 0.003**).

CONCLUSION

Hybrid PET/MRI combined visual PET, multiple sequences MRI and SPM PET helps identify epilepsy lesions particularly in subtle hypometabolic areas. Patients with concordant epileptic lesion localization on PET/MRI and SEEG demonstrated a more favorable outcome than those with inconsistent localization between modalities.

Noise reduction with cross-tracer and cross-protocol deep transfer learning for low-dose PET

Previous studies have demonstrated the feasibility of reducing noise with deep learning-based methods for low-dose fluorodeoxyglucose (FDG) positron emission tomography (PET). This work aimed to investigate the feasibility of noise reduction for tracers without sufficient training datasets using a deep transfer learning approach, which can utilize existing networks trained by the widely available FDG datasets. In this study, the deep transfer learning strategy based on a fully 3D patch-based U-Net was investigated on a 18F-fluoromisonidazole (18F-FMISO) dataset using single-bed scanning and a 68Ga-DOTATATE dataset using whole-body scanning. The datasets of 18F-FDG by single-bed scanning and whole-body scanning were used to obtain pre-trained U-Nets separately for subsequent cross-tracer and cross-protocol transfer learning. The full-dose PET images were used as the labels while low-dose PET images from 10% counts were used as the inputs. Three types of U-Nets were obtained: a U-Net trained by FDG dataset, a pre-trained FDG U-Net fine-tuned by another less-available tracer (FMISO/DOATATE), and a U-Net completely trained by a large number of less-available tracer datasets (FMISO/DOATATE), used as the reference U-Net. The denoising performance of the three types of U-Nets was evaluated on single-bed 18F-FMISO and whole-body 68Ga-DOTATATE separately and compared using normalized root-mean-square error (NRMSE), signal-to-noise ratio (SNR), and relative bias of region of interest (ROI). For cross-tracer transfer learning, all the U-Nets provided denoised images with similar quality for both tracers. There was no significant difference in terms of NRMSE and SNR when comparing the former two U-Nets with the reference U-Net. The ROI biases for these U-Nets were similar. For cross-tracer and cross-protocol transfer learning, the pre-trained single-bed FDG U-Net fine-tuned by whole-body DOTATATE data provided the most consistent images with the reference U-Net. Fine-tuning significantly reduced the NRMSE and the ROI bias and improved the SNR when comparing the fine-tuned U-Net with the U-Net trained by single-bed FDG only (NRMSE: 96.3% ± 21.1% versus 120.6% ± 18.5%, ROI bias: -10.5% ± 13.0% versus -14.7% ± 6.4%, SNR: 4.2 ± 1.4 versus 3.9 ± 1.6, for fine-tuned U-Net and the U-Net trained by single-bed FDG, respectively, with p < 0.01 in all cases). This work demonstrated that it is feasible to utilize existing networks well-trained by FDG datasets to reduce the noise for other less-available tracers and other scanning protocols by using the fine-tuning strategy.

Contribution of PET-MRI in brain diseases in clinical practice

PURPOSE OF REVIEW

Hybrid PET- MRI is a technique that has the ability to improve diagnostic accuracy in many applications, whereas PET and MRI performed separately often fail to provide accurate responses to clinical questions. Here, we review recent studies and current developments in PET-MRI, focusing on clinical applications.

RECENT FINDINGS

The combination of PET and MRI imaging methods aims at increasing the potential of each individual modality. Combined methods of image reconstruction and correction of PET-MRI attenuation are being developed, and a number of applications are being introduced into clinical practice. To date, the value of PET-MRI has been demonstrated for the evaluation of brain tumours in epilepsy and neurodegenerative diseases. Continued advances in data analysis regularly improve the efficiency and the potential application of multimodal biomarkers.

SUMMARY

PET-MRI provides simultaneous of anatomical, functional, biochemical and metabolic information for the personalized characterization and monitoring of neurological diseases. In this review, we show the advantage of the complementarity of different biomarkers obtained using PET-MRI data. We also present the recent advances made in this hybrid imaging modality and its advantages in clinical practice compared with MRI and PET separately.