Tractography passes the test: results from the diffusion-simulated connectivity (DiSCo) challenge.
Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.
View details for DOI 10.1016/j.neuroimage.2023.120231
View details for PubMedID 37330025
Stepwise Stochastic Dictionary Adaptation Improves Microstructure Reconstruction with Orientation Distribution Function Fingerprinting.
Computational diffusion MRI : 13th International Workshop, CDMRI 2022, held in conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, Proceedings. CDMRI (Workshop) (13th : 2022 : Singapore, Singapore)
2022; 13722: 89-100
Fitting of the multicompartment biophysical model of white matter is an ill-posed optimization problem. One approach to make it computationally tractable is through Orientation Distribution Function (ODF) Fingerprinting. However, the accuracy of this method relies solely on ODF dictionary generation mechanisms which either sample the microstructure parameters on a multidimensional grid or draw them randomly with a uniform distribution. In this paper, we propose a stepwise stochastic adaptation mechanism to generate ODF dictionaries tailored specifically to the diffusion-weighted images in hand. The results we obtained on a diffusion phantom and in vivo human brain images show that our reconstructed diffusivities are less noisy and the separation of a free water fraction is more pronounced than for the prior (uniform) distribution of ODF dictionaries.
View details for DOI 10.1007/978-3-031-21206-2_8
View details for PubMedID 36695675
View details for PubMedCentralID PMC9870046
Performance of orientation distribution function-fingerprinting with a biophysical multicompartment diffusion model.
Magnetic resonance in medicine
PURPOSE: Orientation Distribution Function (ODF) peak finding methods typically fail to reconstruct fibers crossing at shallow angles below 40°, leading to errors in tractography. ODF-Fingerprinting (ODF-FP) with the biophysical multicompartment diffusion model allows for breaking this barrier.METHODS: A randomized mechanism to generate a multidimensional ODF-dictionary that covers biologically plausible ranges of intra- and extra-axonal diffusivities and fraction volumes is introduced. This enables ODF-FP to address the high variability of brain tissue. The performance of the proposed approach is evaluated on both numerical simulations and a reconstruction of major fascicles from high- and low-resolution in vivo diffusion images.RESULTS: ODF-FP with the suggested modifications correctly identifies fibers crossing at angles as shallow as 10 degrees in the simulated data. In vivo, our approach reaches 56% of true positives in determining fiber directions, resulting in visibly more accurate reconstruction of pyramidal tracts, arcuate fasciculus, and optic radiations than the state-of-the-art techniques. Moreover, the estimated diffusivity values and fraction volumes in corpus callosum conform with the values reported in the literature.CONCLUSION: The modified ODF-FP outperforms commonly used fiber reconstruction methods at shallow angles, which improves deterministic tractography outcomes of major fascicles. In addition, the proposed approach allows for linearization of the microstructure parameters fitting problem.
View details for DOI 10.1002/mrm.29208
View details for PubMedID 35225365
Using fMRI connectivity to define a treatment-resistant form of post-traumatic stress disorder.
Science translational medicine
2019; 11 (486)
A mechanistic understanding of the pathology of psychiatric disorders has been hampered by extensive heterogeneity in biology, symptoms, and behavior within diagnostic categories that are defined subjectively. We investigated whether leveraging individual differences in information-processing impairments in patients with post-traumatic stress disorder (PTSD) could reveal phenotypes within the disorder. We found that a subgroup of patients with PTSD from two independent cohorts displayed both aberrant functional connectivity within the ventral attention network (VAN) as revealed by functional magnetic resonance imaging (fMRI) neuroimaging and impaired verbal memory on a word list learning task. This combined phenotype was not associated with differences in symptoms or comorbidities, but nonetheless could be used to predict a poor response to psychotherapy, the best-validated treatment for PTSD. Using concurrent focal noninvasive transcranial magnetic stimulation and electroencephalography, we then identified alterations in neural signal flow in the VAN that were evoked by direct stimulation of that network. These alterations were associated with individual differences in functional fMRI connectivity within the VAN. Our findings define specific neurobiological mechanisms in a subgroup of patients with PTSD that could contribute to the poor response to psychotherapy.
View details for PubMedID 30944165
- Using fMRI connectivity to define a treatment-resistant form of post-traumatic stress disorder SCIENCE TRANSLATIONAL MEDICINE 2019; 11 (486)
Informatics methods to enable sharing of quantitative imaging research data
MAGNETIC RESONANCE IMAGING
2012; 30 (9): 1249-1256
The National Cancer Institute Quantitative Research Network (QIN) is a collaborative research network whose goal is to share data, algorithms and research tools to accelerate quantitative imaging research. A challenge is the variability in tools and analysis platforms used in quantitative imaging. Our goal was to understand the extent of this variation and to develop an approach to enable sharing data and to promote reuse of quantitative imaging data in the community.We performed a survey of the current tools in use by the QIN member sites for representation and storage of their QIN research data including images, image meta-data and clinical data. We identified existing systems and standards for data sharing and their gaps for the QIN use case. We then proposed a system architecture to enable data sharing and collaborative experimentation within the QIN.There are a variety of tools currently used by each QIN institution. We developed a general information system architecture to support the QIN goals. We also describe the remaining architecture gaps we are developing to enable members to share research images and image meta-data across the network.As a research network, the QIN will stimulate quantitative imaging research by pooling data, algorithms and research tools. However, there are gaps in current functional requirements that will need to be met by future informatics development. Special attention must be given to the technical requirements needed to translate these methods into the clinical research workflow to enable validation and qualification of these novel imaging biomarkers.
View details for DOI 10.1016/j.mri.2012.04.007
View details for Web of Science ID 000309946000006
View details for PubMedID 22770688
View details for PubMedCentralID PMC3466343
MRI of articular cartilage in OA: novel pulse sequences and compositional/functional markers
Workshop for Consensus on Osteoarthritis Imaging
W B SAUNDERS CO LTD. 2006: A76–A86
Osteoarthritis (OA) is a leading cause of disability worldwide. Magnetic resonance imaging (MRI), with its unique ability to image and characterize soft tissue non-invasively, has proven valuable in assessing cartilage in OA. The development of new, fast imaging methods with high contrast show promise to improve the magnetic resonance (MR) evaluation of this disease. In addition to morphologic MRI methods, MRI contrast mechanisms under development may reveal detailed information about the physiology of cartilage. It is anticipated that these and other MRI techniques will play an increasingly important role in assessing the success or failure of therapies for OA. On December 5 and 6, 2002, OMERACT (Outcome Measures in Rheumatology Clinical Trials) and OARSI (Osteoarthritis Research Society International) held a workshop in Bethesda, MD aiming at providing a state-of-the-art review of imaging outcome measures for OA of the knee to help guide scientists and pharmaceutical companies in the use of MRI in multi-site studies of OA. Applications of MRI were initially reviewed by a multidisciplinary, international panel of expert scientists and physicians from academia, the pharmaceutical industry and regulatory agencies. The findings of the panel were then presented to a wider group of participants for open discussion. The following report summarizes the results of these discussions with respect to novel MRI pulse sequences for evaluating articular cartilage of the knee in OA and notes any additional advances that have been made since.
View details for Web of Science ID 000238959700011
View details for PubMedID 16716605