Higher b-values improve the correlation between diffusion MRI and the cortical microarchitecture

Evaluation of deep learning reconstruction on diffusion-weighted imaging quality and apparent diffusion coefficient using an ice-water phantom

This study assessed the influence of deep learning reconstruction (DLR) on the quality of diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) using an ice-water phantom. An ice-water phantom with known diffusion properties (true ADC = 1.1 × 10-3 mm2/s at 0 °C) was imaged at various b-values (0, 1000, 2000, and 4000 s/mm2) using a 3 T magnetic resonance imaging scanner with slice thicknesses of 1.5 and 3.0 mm. All DWIs were reconstructed with or without DLR. ADC maps were generated using combinations of b-values 0 and 1000, 0 and 2000, and 0 and 4000 s/mm2. Based on the quantitative imaging biomarker alliance profile, the signal-to-noise ratio (SNRs) in DWIs was calculated, and the accuracy, precision, and within-subject parameter variance (wCV) of the ADCs were evaluated. DLR improved the SNR in DWIs with b-values ranging from 0 to 2000s/mm2; however, its effectiveness was diminished at 4000 s/mm2. There was no noticeable difference in the ADCs of images generated with or without implementing DLR. For a slice thickness of 1.5 mm and combined b-values of 0 and 4000 s/mm2, the ADC values were 0.97 × 10-3and 0.98 × 10-3mm2/s with and without DLR, respectively, both being lower than the true ADC value. Furthermore, DLR enhanced the precision and wCV of the ADC measurements. DLR can enhance the SNR, repeatability, and precision of ADC measurements; however, it does not improve their accuracies.

High B-value diffusion tensor imaging for early detection of hippocampal microstructural alteration in a mouse model of multiple sclerosis

Several studies have highlighted the value of diffusion tensor imaging (DTI) with strong diffusion weighting to reveal white matter microstructural lesions, but data in gray matter (GM) remains scarce. Herein, the effects of b-values combined with different numbers of diffusion-encoding directions (NDIRs) on DTI metrics to capture the normal hippocampal microstructure and its early alterations were investigated in a mouse model of multiple sclerosis (experimental autoimmune encephalomyelitis [EAE]). Two initial DTI datasets (B2700-43Dir acquired with b = 2700 s.mm⁻² and NDIR = 43; B1000-22Dir acquired with b = 1000 s.mm⁻² and NDIR = 22) were collected from 18 normal and 18 EAE mice at 4.7 T. Three additional datasets (B2700-22Dir, B2700-12Dir and B1000-12Dir) were extracted from the initial datasets. In healthy mice, we found a significant influence of b-values and NDIR on all DTI metrics. Confronting unsupervised hippocampal layers classification to the true anatomical classification highlighted the remarkable discrimination of the molecular layer with B2700-43Dir compared with the other datasets. Only DTI from the B2700 datasets captured the dendritic loss occurring in the molecular layer of EAE mice. Our findings stress the needs for both high b-values and sufficient NDIR to achieve a GM DTI with more biologically meaningful correlations, though DTI-metrics should be interpreted with caution in these settings.