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Taku Hatano

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YNIMG Journal 2022 Journal Article

Rigid real-time prospective motion-corrected three-dimensional multiparametric mapping of the human brain

  • Shohei Fujita
  • Akifumi Hagiwara
  • Naoyuki Takei
  • Issei Fukunaga
  • Yasuhiro Hagiwara
  • Takashi Ogawa
  • Taku Hatano
  • Dan Rettmann

PURPOSE: To develop a rigid real-time prospective motion-corrected multiparametric mapping technique and to test the performance of quantitative estimates. METHODS: Motion tracking and correction were performed by integrating single-shot spiral navigators into a multiparametric imaging technique, three-dimensional quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS). The spiral navigator was optimized, and quantitative measurements were validated using a standard system phantom. The effect of motion correction on whole-brain T1 and T2 mapping under different types of head motion during the scan was evaluated in 10 healthy volunteers. Finally, six patients with Parkinson's disease, which is known to be associated with a high prevalence of motion artifacts, were scanned to evaluate the effectiveness of our method in the real world. RESULTS: The phantom study demonstrated that the proposed motion correction method did not introduce quantitative bias. Improved parametric map quality and repeatability were shown in volunteer experiments with both in-plane and through-plane motions, comparable to the no-motion ground truth. In real-life validation in patients, the approach showed improved parametric map quality compared to images obtained without motion correction. CONCLUSIONS: Real-time prospective motion-corrected multiparametric relaxometry based on 3D-QALAS provided robust and repeatable whole-brain multiparametric mapping.

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YNICL Journal 2018 Journal Article

Connectome analysis with diffusion MRI in idiopathic Parkinson's disease: Evaluation using multi-shell, multi-tissue, constrained spherical deconvolution

  • Koji Kamagata
  • Andrew Zalesky
  • Taku Hatano
  • Maria Angelique Di Biase
  • Omar El Samad
  • Shinji Saiki
  • Keigo Shimoji
  • Kanako K. Kumamaru

Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects extensive regions of the central nervous system. In this work, we evaluated the structural connectome of patients with PD, as mapped by diffusion-weighted MRI tractography and a multi-shell, multi-tissue (MSMT) constrained spherical deconvolution (CSD) method to increase the precision of tractography at tissue interfaces. The connectome was mapped with probabilistic MSMT-CSD in 21 patients with PD and in 21 age- and gender-matched controls. Mapping was also performed by deterministic single-shell, single tissue (SSST)-CSD tracking and probabilistic SSST-CSD tracking for comparison. A support vector machine was trained to predict diagnosis based on a linear combination of graph metrics. We showed that probabilistic MSMT-CSD could detect significantly reduced global strength, efficiency, clustering, and small-worldness, and increased global path length in patients with PD relative to healthy controls; by contrast, probabilistic SSST-CSD only detected the difference in global strength and small-worldness. In patients with PD, probabilistic MSMT-CSD also detected a significant reduction in local efficiency and detected clustering in the motor, frontal temporoparietal associative, limbic, basal ganglia, and thalamic areas. The network-based statistic identified a subnetwork of reduced connectivity by MSMT-CSD and probabilistic SSST-CSD in patients with PD, involving key components of the cortico-basal ganglia-thalamocortical network. Finally, probabilistic MSMT-CSD had superior diagnostic accuracy compared with conventional probabilistic SSST-CSD and deterministic SSST-CSD tracking. In conclusion, probabilistic MSMT-CSD detected a greater extent of connectome pathology in patients with PD, including those with cortico-basal ganglia-thalamocortical network disruptions. Connectome analysis based on probabilistic MSMT-CSD may be useful when evaluating the extent of white matter connectivity disruptions in PD.

Details DOI
v2026.09.13