NeuroImage

Multi-delay arterial spin labeling (MDASL) can quantitatively measure cerebral blood flow (CBF) and arterial transit time (ATT), which is particularly suitable for pediatric perfusion imaging. Here we present a high resolution (iso-2mm) MDASL protocol and performed test-retest scans on 21 typically developing children aged 8 to 17 years. We further proposed a Transformer-based deep learning (DL) model with k-space weighted image average (KWIA) denoised images as reference for training the model....

Neuroimaging of certain pathologies requires both multi-parametric qualitative and quantitative imaging. The role of the quantitative MRI (qMRI) is well accepted but suffers from long acquisition times leading to patient discomfort, especially in geriatric and pediatric patients. Previous studies show that synthetic MRI can be used in order to reduce the scan time and provide qMRI as well as multi-contrast data. However, this approach suffers from artifacts such as partial volume and flow. In or...

In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and artificial intelligence. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses only one T1w MRI pre-processing step to simplify implementation and increase accessibility in research settings. Our model only requires rigid imag...

BackgroundPerfusion assessment in cerebrovascular disease is essential for evaluating cerebral hemodynamics and guides many current treatment decisions. Dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) is of great utility to generate perfusion parameter maps, but its reliance on a contrast agent with associated health risks and technical challenges limit its usability. We hypothesized that native Time-of-flight magnetic resonance angiography (TOF-MRA) can be used to generat...

Brain age gap, the difference between estimated brain age and chronological age via magnetic resonance imaging, has emerged as a pivotal biomarker in the detection of brain abnormalities. While deep learning is accurate in estimating brain age, the absence of uncertainty estimation may pose risks in clinical use. Moreover, current 3D brain age models are intricate, and using 2D slices hinders comprehensive dimensional data integration. Here, we introduced Spectral-normalized Neural Gaussian Proc...

We present the Gonzo dataset: Brain MRI and derivative data from one healthy male human volunteer ("Gonzo") before and during the 72 hours after intrathecal injection of the contrast agent gadobutrol into the cerebrospinal fluid (CSF) of the spinal canal. The MRI data records include images highlighting the temporal and spatial evolution of the contrast agent in CSF, brain, and adjacent structures. In addition to raw MRI, we provide derivatives that enable numerical simulations of the transport ...

PurposeTo provide a tool for the automatic segmentation of an arteriogram of the brain from MRA images and the estimation of arterial tortuosity as a summary marker. MethodsA deep learning model was trained and validated on a previously published set of semi-automatically segmented brain arteriograms. We tested whether arterial tortuosity estimated from a large number of age-representative subjects (N = 478) would reproduce previously published statistics of increasing tortuosity with age. Res...

PurposePrior work has shown that whole-band linear regression of nuisance signals can introduce artifactual connectivity in high-frequency resting-state fMRI. Errors of motion regressors and non-stationarity of nuisance signals exacerbate artifacts. Here, we introduce spectral-temporal segmentation of regression vectors to decouple regression in different frequency bands to reduce motion artifacts. MethodsAn alternative approach to whole-band linear nuisance regression is introduced in the pres...

Neuroimaging faces a reproducibility crisis, where studies on small, heterogeneous datasets produce unreliable brain-wide associations and AI models that fail to generalize. To address this, we introduce GenBrain, a generative foundation model pretrained on approximately 1.2 million 3D scans from over 44,000 individuals across 34 imaging modalities to learn a population prior of brain structure and function. Crucially, GenBrain enables rapid, data-efficient adaptation, allowing any targeted stud...

Establishing reliable and time efficient pipelines for structural MRI segmentation, parcellation and surface reconstruction, is essential to explore the potential clinical applications of research-grade morphometry tools. The integration between deep-learning based methods for fast whole-brain segmentation and the well known surface reconstruction algorithms is a viable alternative to perform this task. In this work, we applied this idea with three deep-learning based cortical parcellation model...

PurposeHead-motion tracking and correction remains a key area of research in MRI, but the lack of rigorous and standardized evaluation approaches hinders their optimization and comparison. We introduce an in-vivo framework for assessing the accuracy of intra-MRI head motion tracking, and demonstrates its effectiveness by comparing two methods based on a markerless optical system (MOS) and a fat signal navigator (FatNav). MethodsSix participants underwent 3T brain MRI using a T1-weighted (T1w) p...

High detail and fast magnetic resonance imaging (MRI) sequences are highly demanded in clinical settings, as inadequate imaging information can lead to diagnostic difficulties. MR image super-resolution (SR) is a promising way to address this issue, but its performance is limited due to the practical difficulty of acquiring paired low- and high-resolution (LR and HR) images. Most existing methods generate these pairs by down-sampling HR images, a process that often fails to capture complex degra...

While strong associations of structural magnetic resonance imaging (sMRI) with preterm birth and post-menstrual age (PMA) have been reported, such associations for functional MRI (fMRI) have been considerably weaker. We studied the associations of the aperiodic parameters of neonatal fMRI Blood-oxygen-level-dependent (BOLD) signal power spectrum with preterm birth and PMA at scan using task-free fMRI data from the Developing Human Connectome Project (dHCP). First, we studied the associations of ...

Positron emission tomography (PET) provides quantitative functional imaging of biomarkers unavailable in other modalities, however, images are of relatively low resolution compared to modalities such as magnetic resonance imaging (MRI). A typical approach is to reconstruct to a higher resolution and regularize using a structural image, but there are practical limitations to this approach. Alternatively, post-reconstruction approaches involve image-based correction, but typically rely on a segmen...

Magnetic Resonance Imaging (MRI) visible perivascular spaces (PVS) have been associated with age, decline in cognitive abilities, interrupted sleep, and markers of small vessel disease. Therefore, several computational methods have been developed for their assessment from brain MRI. But the limits of validity of these methods under various spatial resolutions, and the accuracy in detecting and measuring the dimensions of these structures have not been established. We use a digital reference obje...

Development of innovative non-invasive neuroimaging methods and biomarkers are critical for studying brain disease. In this work, we have developed a methodology to characterize the frequency responses and spatial localization of oscillations and movements of cerebrospinal fluid (CSF) flow in the human brain. Using 7 Tesla human MRI and ultrafast echo-planar imaging (EPI), in-vivo images were obtained to capture CSF oscillations and movements. Physiological data was simultaneously collected and ...

PurposeCerebral blood flow (CBF) is commonly measured by pseudo-continuous arterial spin labeling (PCASL) in human research, but recent advancements in methodology have limited data reuse. The object of this work is to harmonize two distinct PCASL techniques within a cohort with a wide range of CBF values. MethodsParticipants had two PCASL sequences collected within a single session: a single post-label delay sequence with a 2D echo-planar imaging (EPI) readout, "CBF2D,1PLD", and a five post-la...

Magnetic Resonance Spectroscopic Imaging (MRSI) enables non-invasive mapping of brain metabolite concentrations but remains computationally intensive and challenging due to a low signal-to-noise ratio (SNR) and overlapping spectral features. Traditional spectral fitting methods, such as LCModel, are time-consuming and often lack comprehensive uncertainty quantification. In this study, we propose Physics-Informed Variational Encoder (PHIVE), a novel deep learning framework that integrates physics...

AO_SCPLOWBSTRACTC_SCPLOWMagnetic resonance imaging (MRI) is a cornerstone of modern neuroimaging, where accurate segmentation of brain structures and lesions is essential for diagnosis, treatment planning, and clinical research. However, most current foundation models are trained on mixed-organ datasets, while the anatomical structures of the brain differ substantially from those of other organs such as the lungs and kidneys. As a result, these models often struggle to adapt to the distinctive c...

We propose a novel explainable AI (XAI) model for classification tasks that can treat multi-modal and multidimensional information. Unlike traditional classification models based on convolutional neural networks or transformers, our approach combines probabilistic circuits with two vision transformers, DINO and CLIP, enabling probabilistic interpretability at the patch level, encoder level, and modality level. To demonstrate this capability, we developed a three-dimensional multimodal classifica...