SMART-PET: a Self-SiMilARiTy-aware generative adversarial framework for reconstructing low-count [18F]-FDG-PET brain imaging.

Introduction: In Positron Emission Tomography (PET) imaging, the use of tracers increases radioactive exposure for longitudinal evaluations and in radiosensitive populations such as pediatrics. However, reducing injected PET activity potentially leads to an unfavorable compromise between radiation exposure and image quality, causing lower signal-to-noise ratios and degraded images. Deep learning-based denoising approaches can be employed to recover low count PET image signals: nonetheless, most of these methods rely on structural or anatomic guidance from magnetic resonance imaging (MRI) and fails to effectively preserve global spatial features in denoised PET images, without impacting signal-to-noise ratios.

Dementia risk reduction in the African context: Multi-national implementation of multimodal strategies to promote healthy brain aging in Africa (the Africa-FINGERS project).

Dementia prevention in Africa is critically underexplored, despite the continent's high prevalence of modifiable risk factors. With a predominantly young and middle-aged population, Africa presents a prime opportunity to implement evidence-based strategies that could significantly reduce future dementia cases and mitigate its economic impact. The multinational Africa-FINGERS program offers an innovative solution, pioneering culturally sensitive, multidomain interventions tailored to the unique challenges of the region.

Automated, Transferable, and Ethanol-Free Radiosynthesis of [C]Butanol.

Cerebral blood flow and blood-brain barrier permeability assessment are crucial hemodynamic parameters to measure under neurological conditions. In conjunction with positron emission tomography (PET), oxygen-15-labeled water has emerged as a gold standard for measuring cerebral perfusion; however, at higher flow rates, [O]water extraction becomes nonlinear. In such a scenario, freely diffusible [C]butanol can provide a truer estimate.

Structural Brain Differences in the Alzheimer's Disease Continuum: Insights Into the Heterogeneity From a Large Multisite Neuroimaging Consortium.

Background: Neurodegenerative diseases require collaborative, multisite research to comprehensively grasp their complex and diverse pathological progression; however, there is caution in aggregating global data due to data heterogeneity. In the current study, we investigated brain structure across stages of Alzheimer's disease (AD) and how relationships vary across sources of heterogeneity.

Methods: Using 6 international datasets (N > 27,000), associations of structural neuroimaging markers were investigated in relation to the AD continuum via meta-analysis.

Scan With Me: A Train-the-Trainer Program to Upskill MRI Personnel in Low- and Middle-Income Countries.

Purpose: Access to MRI in low- and middle-income countries (LMICs) remains among the poorest in the world. The lack of skilled MRI personnel exacerbates access gaps, reinforcing long-standing health disparities. The Scan With Me (SWiM) program aims to sustainably create a network of highly skilled MRI technologists in LMICs who will facilitate the transfer of MRI knowledge and skills to their peers and contribute to the implementation of highly valuable imaging protocols for effective clinical and research use.

Self-supervised anatomical continuity enhancement network for 7T SWI synthesis from 3T SWI.

Synthesizing 7T Susceptibility Weighted Imaging (SWI) from 3T SWI could offer significant clinical benefits by combining the high sensitivity of 7T SWI for neurological disorders with the widespread availability of 3T SWI in diagnostic routines. Although methods exist for synthesizing 7T Magnetic Resonance Imaging (MRI), they primarily focus on traditional MRI modalities like T1-weighted imaging, rather than SWI. SWI poses unique challenges, including limited data availability and the invisibility of certain tissues in individual 3T SWI slices.

Imaging without barriers.

Low-field magnetic resonance imaging can be engineered for widespread point-of-care diagnostics.

Reproducibility of arterial spin labeling cerebral blood flow image processing: A report of the ISMRM open science initiative for perfusion imaging (OSIPI) and the ASL MRI challenge.

Purpose: Arterial spin labeling (ASL) is a widely used contrast-free MRI method for assessing cerebral blood flow (CBF). Despite the generally adopted ASL acquisition guidelines, there is still wide variability in ASL analysis. We explored this variability through the ISMRM-OSIPI ASL-MRI Challenge, aiming to establish best practices for more reproducible ASL analysis.

Developing blood-brain barrier arterial spin labelling as a non-invasive early biomarker of Alzheimer's disease (DEBBIE-AD): a prospective observational multicohort study protocol.

Introduction: Loss of blood-brain barrier (BBB) integrity is hypothesised to be one of the earliest microvascular signs of Alzheimer's disease (AD). Existing BBB integrity imaging methods involve contrast agents or ionising radiation, and pose limitations in terms of cost and logistics. Arterial spin labelling (ASL) perfusion MRI has been recently adapted to map the BBB permeability non-invasively.

Simultaneous estimation of a model-derived input function for quantifying cerebral glucose metabolism with [F]FDG PET.

Background: Quantification of the cerebral metabolic rate of glucose (CMRGlu) by dynamic [F]FDG PET requires invasive arterial sampling. Alternatives to using an arterial input function (AIF) include the simultaneous estimation (SIME) approach, which models the image-derived input function (IDIF) by a series of exponentials with coefficients obtained by fitting time activity curves (TACs) from multiple volumes-of-interest. A limitation of SIME is the assumption that the input function can be modelled accurately by a series of exponentials.

The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma.

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date.

Probing Evidence of Cerebral White Matter Microstructural Disruptions in Ischemic Heart Disease Before and Following Cardiac Rehabilitation: A Diffusion Tensor MR Imaging Study.

Background: Ischemic heart disease (IHD) is linked to brain white matter (WM) breakdown but how age or disease effects WM integrity, and whether it is reversible using cardiac rehabilitation (CR), remains unclear.

Purpose: To assess the effects of brain aging, cardiovascular disease, and CR on WM microstructure in brains of IHD patients following a cardiac event.

Study Type: Retrospective.

Bringing MRI to low- and middle-income countries: Directions, challenges and potential solutions.

The global disparity of magnetic resonance imaging (MRI) is a major challenge, with many low- and middle-income countries (LMICs) experiencing limited access to MRI. The reasons for limited access are technological, economic and social. With the advancement of MRI technology, we explore why these challenges still prevail, highlighting the importance of MRI as the epidemiology of disease changes in LMICs.

The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms.

The Brain Tumor Segmentation (BraTS) Challenge 2023: .

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations.

Imaging blood-brain barrier dysfunction: A state-of-the-art review from a clinical perspective.

The blood-brain barrier (BBB) consists of specialized cells that tightly regulate the in- and outflow of molecules from the blood to brain parenchyma, protecting the brain's microenvironment. If one of the BBB components starts to fail, its dysfunction can lead to a cascade of neuroinflammatory events leading to neuronal dysfunction and degeneration. Preliminary imaging findings suggest that BBB dysfunction could serve as an early diagnostic and prognostic biomarker for a number of neurological diseases.

Hemodialysis-Related Acute Brain Injury Demonstrated by Application of Intradialytic Magnetic Resonance Imaging and Spectroscopy.

Significance Statement: Hemodialysis (HD) results in reduced brain blood flow, and HD-related circulatory stress and regional ischemia are associated with brain injury over time. However, studies to date have not provided definitive direct evidence of acute brain injury during a HD treatment session. Using intradialytic magnetic resonance imaging (MRI) and spectroscopy to examine HD-associated changes in brain structure and neurochemistry, the authors found that multiple white (WM) tracts had diffusion imaging changes characteristic of cytotoxic edema, a consequence of ischemic insult and a precursor to fixed structural WM injury.

The performance of machine learning approaches for attenuation correction of PET in neuroimaging: A meta-analysis.

Purpose: This systematic review provides a consensus on the clinical feasibility of machine learning (ML) methods for brain PET attenuation correction (AC). Performance of ML-AC were compared to clinical standards.

Methods: Two hundred and eighty studies were identified through electronic searches of brain PET studies published between January 1, 2008, and August 1, 2022.

Development of a minimally invasive simultaneous estimation method for quantifying translocator protein binding with [F]FEPPA positron emission tomography.

Background: The purpose of this study was to assess the feasibility of using a minimally invasive simultaneous estimation method (SIME) to quantify the binding of the 18-kDa translocator protein tracer [F]FEPPA. Arterial sampling was avoided by extracting an image-derived input function (IDIF) that was metabolite-corrected using venous blood samples. The possibility of reducing scan duration to 90 min from the recommended 2-3 h was investigated by assuming a uniform non-displaceable distribution volume (V) to simplify the SIME fitting.

A framework for advancing sustainable magnetic resonance imaging access in Africa.

Magnetic resonance imaging (MRI) technology has profoundly transformed current healthcare systems globally, owing to advances in hardware and software research innovations. Despite these advances, MRI remains largely inaccessible to clinicians, patients, and researchers in low-resource areas, such as Africa. The rapidly growing burden of noncommunicable diseases in Africa underscores the importance of improving access to MRI equipment as well as training and research opportunities on the continent.