Litcius/Paper detail

In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution

Fuyixue Wang, Zijing Dong, Qiyuan Tian, Congyu Liao, Qiuyun Fan, W. Scott Hoge, Boris Keil, Jon̈athan R. Polimeni, Lawrence L. Wald, Susie Y. Huang, Kawin Setsompop

2021Scientific Data77 citationsDOIOpen Access PDF

Abstract

Abstract We present a whole-brain in vivo diffusion MRI (dMRI) dataset acquired at 760 μm isotropic resolution and sampled at 1260 q-space points across 9 two-hour sessions on a single healthy participant. The creation of this benchmark dataset is possible through the synergistic use of advanced acquisition hardware and software including the high-gradient-strength Connectom scanner, a custom-built 64-channel phased-array coil, a personalized motion-robust head stabilizer, a recently developed SNR-efficient dMRI acquisition method, and parallel imaging reconstruction with advanced ghost reduction algorithm. With its unprecedented resolution, SNR and image quality, we envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance the understanding of human brain structures and connectivity. This comprehensive dataset can also be used as a test bed for new modeling, sub-sampling strategies, denoising and processing algorithms, potentially providing a common testing platform for further development of in vivo high resolution dMRI techniques. Whole brain anatomical T 1 -weighted and T 2 -weighted images at submillimeter scale along with field maps are also made available.

Topics & Concepts

Computer scienceDiffusion MRIBenchmark (surveying)Artificial intelligenceHuman Connectome ProjectScannerImage qualitySampling (signal processing)Pattern recognition (psychology)Computer visionMagnetic resonance imagingImage (mathematics)Functional connectivityGeologyMedicineBiologyNeuroscienceFilter (signal processing)GeodesyRadiologyAdvanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsMRI in cancer diagnosis