235 research outputs found
Histological validation of the brain cell body imaging with diffusion MRI at ultrahigh field
Biophysical modelling of diffusion-weighted MRI (DW-MRI) data can help to gain more insight into brain microstructure. However, models need to be validated. This work validates a recently-developed technique for non-invasive mapping of brain cell-body (soma) size/ density with DW-MRI, by using ultrahigh-field DW-MRI experiments and histology of mouse brain. Predictions from numerical simulations are experimentally confirmed and brain’s maps of MR-measured soma size/density are shown to correspond very well with histology. We provide differential contrasts between cell layers that are less expressed in tensor analyses, leading to novel complementary contrasts of the brain tissue. Limitations and future research directions are discussed
Generalized Diffusion MRI Denoising and Super-Resolution using Swin Transformers
Diffusion MRI is a non-invasive, in-vivo medical imaging method able to map
tissue microstructure and structural connectivity of the human brain, as well
as detect changes, such as brain development and injury, not visible by other
clinical neuroimaging techniques. However, acquiring high signal-to-noise ratio
(SNR) datasets with high angular and spatial sampling requires prohibitively
long scan times, limiting usage in many important clinical settings, especially
children, the elderly, and emergency patients with acute neurological disorders
who might not be able to cooperate with the MRI scan without conscious sedation
or general anesthesia. Here, we propose to use a Swin UNEt TRansformers (Swin
UNETR) model, trained on augmented Human Connectome Project (HCP) data and
conditioned on registered T1 scans, to perform generalized denoising and
super-resolution of diffusion MRI invariant to acquisition parameters, patient
populations, scanners, and sites. We qualitatively demonstrate super-resolution
with artificially downsampled HCP data in normal adult volunteers. Our
experiments on two other unrelated datasets, one of children with
neurodevelopmental disorders and one of traumatic brain injury patients, show
that our method demonstrates superior denoising despite wide data distribution
shifts. Further improvement can be achieved via finetuning with just one
additional subject. We apply our model to diffusion tensor (2nd order spherical
harmonic) and higher-order spherical harmonic coefficient estimation and show
results superior to current state-of-the-art methods. Our method can be used
out-of-the-box or minimally finetuned to denoise and super-resolve a wide
variety of diffusion MRI datasets. The code and model are publicly available at
https://github.com/ucsfncl/dmri-swin
Min-Cut Max-Flow for Network Abnormality Detection: Application to Preterm Birth
Neuroimaging studies of structural connectomes typically average the data from many subjects and analyse the average properties of the resulting network. We propose a new framework for individual brain-network structural abnormality detection. The framework uses a graph-based anomaly detection algorithm that allows to detect abnormal structural connectivity on a subject level. The proposed method is generic and can be adapted for a broad range of network abnormality detection problems. In this study, we apply our method to investigate the integrity of white matter tracts of 19-year-old extremely preterm born individuals. We show the feasibility to cast the network abnormality detection problem into a min-cut max-flow problem, and identify consistent abnormal white matter tracts in extremely preterm subjects, including a common network involving the bilateral thalamus and frontal gyri
Connectivity-enhanced diffusion analysis reveals white matter density disruptions in first episode and chronic schizophrenia.
Reduced fractional anisotropy (FA) is a well-established correlate of schizophrenia, but it remains unclear whether these tensor-based differences are the result of axon damage and/or organizational changes and whether the changes are progressive in the adult course of illness. Diffusion MRI data were collected in 81 schizophrenia patients (54 first episode and 27 chronic) and 64 controls. Analysis of FA was combined with "fixel-based" analysis, the latter of which leverages connectivity and crossing-fiber information to assess both fiber bundle density and organizational complexity (i.e., presence and magnitude of off-axis diffusion signal). Compared with controls, patients with schizophrenia displayed clusters of significantly lower FA in the bilateral frontal lobes, right dorsal centrum semiovale, and the left anterior limb of the internal capsule. All FA-based group differences overlapped substantially with regions containing complex fiber architecture. FA within these clusters was positively correlated with principal axis fiber density, but inversely correlated with both secondary/tertiary axis fiber density and voxel-wise fiber complexity. Crossing fiber complexity had the strongest (inverse) association with FA (r = -0.82). When crossing fiber structure was modeled in the MRtrix fixel-based analysis pipeline, patients exhibited significantly lower fiber density compared to controls in the dorsal and posterior corpus callosum (central, postcentral, and forceps major). Findings of lower FA in patients with schizophrenia likely reflect two inversely related signals: reduced density of principal axis fiber tracts and increased off-axis diffusion sources. Whereas the former confirms at least some regions where myelin and or/axon count are lower in schizophrenia, the latter indicates that the FA signal from principal axis fiber coherence is broadly contaminated by macrostructural complexity, and therefore does not necessarily reflect microstructural group differences. These results underline the need to move beyond tensor-based models in favor of acquisition and analysis techniques that can help disambiguate different sources of white matter disruptions associated with schizophrenia
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