Suguru Yokosawa

Optimization of Scan Parameters to Reduce Acquisition Time for Diffusion Kurtosis Imaging at 1.5T

PURPOSE To shorten acquisition of diffusion kurtosis imaging (DKI) in 1.5-tesla magnetic resonance (MR) imaging, we investigated the effects of the number of b-values, diffusion direction, and number of signal averages (NSA) on the accuracy of DKI metrics. METHODS We obtained 2 image datasets with 30 gradient directions, 6 b-values up to 2500 s/mm(2), and 2 signal averages from 5 healthy volunteers and generated DKI metrics, i.e., mean, axial, and radial kurtosis (MK, K∥, and K⊥) maps, from various combinations of the datasets. These maps were estimated by using the intraclass correlation coefficient (ICC) with those from the full datasets. RESULTS The MK and K⊥ maps generated from the datasets including only the b-value of 2500 s/mm(2) showed excellent agreement (ICC, 0.96 to 0.99). Under the same acquisition time and diffusion directions, agreement was better of MK, K∥, and K⊥ maps obtained with 3 b-values (0, 1000, and 2500 s/mm(2)) and 4 signal averages than maps obtained with any other combination of numbers of b-value and varied NSA. Good agreement (ICC > 0.6) required at least 20 diffusion directions in all the metrics. CONCLUSION MK and K⊥ maps with ICC greater than 0.95 can be obtained at 1.5T within 10 min (b-value = 0, 1000, and 2500 s/mm(2); 20 diffusion directions; 4 signal averages; slice thickness, 6 mm with no interslice gap; number of slices, 12).

Differentiation among parkinsonisms using quantitative diffusion kurtosis imaging.

Differential diagnoses among Parkinson’s disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy syndrome (PSPS) are often difficult. Hence, we investigated whether diffusion kurtosis imaging (DKI) could detect pathological changes that occur in these disorders and be used to differentiate between such patients. Fourteen patients (five with PD, four MSA, and five PSPS) and six healthy controls were examined using a 1.5-T scanner. Mean kurtosis (MK), fractional anisotropy, and mean diffusivity maps were generated, and these values of the midbrain tegmentum (MBT) and pontine crossing tract (PCT), as well as MBT/PCT ratios, were obtained. We found no significant differences in MBT and PCT values on DKI maps among the groups. In contrast, MBT/PCT ratios from MK maps were significantly increased in the MSA group and decreased in the PSPS group compared with the other groups. MBT/PCT ratios from mean diffusivity maps showed a significant increase in the PSPS group. Therefore, quantitative DKI analyses, particularly the MBT/PCT ratio from MK maps, can differentiate patients with parkinsonisms.

Quantitative Histological Validation of Diffusion Tensor MRI with Two-Photon Microscopy of Cleared Mouse Brain

1Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan 2Research Institute for Diseases of Old Age, Juntendo University Graduate School of Medicine 3Research & Development Group, Hitachi Ltd. 4Department of Radiology, University of Tokyo 5Department of Radiological Sciences, Graduate School of Human Health Sciences 6Department of Neuropathology, Tokyo Medical and Dental University (Received November 22, 2015; Accepted February 10, 2016; published online March 30, 2016)

See-through Brains and Diffusion Tensor MRI Clarified Fiber Connections: A Preliminary Microstructural Study in a Mouse with Callosal Agenesis

Clearing methods that render the brain optically transparent allow high-resolution three-dimensional (3D) imaging of neural networks. We used diffusion tensor imaging (DTI) and two-photon imaging of cleared brains to analyze white matter in BTBR mice. We confirmed corpus callosum agenesis and identified an abnormal commissure close to the third ventricle. DTI and cleared-brain two-photon imaging revealed that these commissural fibers constituted a frontal clustering of the ventral hippocampal commissure and provided a detailed assessment of white matter structure in mice.

The Relationship between Neurite Density Measured with Confocal Microscopy in a Cleared Mouse Brain and Metrics Obtained from Diffusion Tensor and Diffusion Kurtosis Imaging

Purpose: Diffusional kurtosis imaging (DKI) enables sensitive measurement of tissue microstructure by quantifying the non-Gaussian diffusion of water. Although DKI is widely applied in many situations, histological correlation with DKI analysis is lacking. The purpose of this study was to determine the relationship between DKI metrics and neurite density measured using confocal microscopy of a cleared mouse brain. Methods: One thy-1 yellow fluorescent protein 16 mouse was deeply anesthetized and perfusion fixation was performed. The brain was carefully dissected out and whole-brain MRI was performed using a 7T animal MRI system. DKI and diffusion tensor imaging (DTI) data were obtained. After the MRI scan, brain sections were prepared and then cleared using aminoalcohols (CUBIC). Confocal microscopy was performed using a two-photon confocal microscope with a laser. Forty-eight ROIs were set on the caudate putamen, seven ROIs on the anterior commissure, and seven ROIs on the ventral hippocampal commissure on the confocal microscopic image and a corresponding MR image. In each ROI, histological neurite density and the metrics of DKI and DTI were calculated. The correlations between diffusion metrics and neurite density were analyzed using Pearson correlation coefficient analysis. Results: Mean kurtosis (MK) (P = 5.2 × 10−9, r = 0.73) and radial kurtosis (P = 2.3 × 10−9, r = 0.74) strongly correlated with neurite density in the caudate putamen. The correlation between fractional anisotropy (FA) and neurite density was moderate (P = 0.0030, r = 0.42). In the anterior commissure and the ventral hippocampal commissure, neurite density and FA are very strongly correlated (P = 1.3 × 10−5, r = 0.90). MK in these areas were very high value and showed no significant correlation (P = 0.48). Conclusion: DKI accurately reflected neurite density in the area with crossing fibers, potentially allowing evaluation of complex microstructures.