Tensor-valued diffusion encoding for diffusional variance decomposition (DIVIDE): Technical feasibility in clinical MRI systems

• Published in: PloS one Vol. 14; no. 3; p. e0214238
• Main Authors: Szczepankiewicz, Filip, Sjölund, Jens, Ståhlberg, Freddy, Lätt, Jimmy, Nilsson, Markus
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 28.03.2019; Public Library of Science (PLoS)
• Subjects: Accessibility; Algorithms; Amplitudes; Anisotropy; Annan fysik; Biology and Life Sciences; Brain; Brain - diagnostic imaging; Clinical Medicine; Coding; Computer and Information Sciences; Decomposition; Design of experiments; Design parameters; Diagnostic imaging; Diffusion; Diffusion Tensor Imaging - instrumentation; Diffusion Tensor Imaging - methods; Experimental design; Feasibility Studies; Female; Fysik; Hardware; Humans; Image Processing, Computer-Assisted - instrumentation; Image Processing, Computer-Assisted - methods; Imaging techniques; Klinisk medicin; Magnetic resonance imaging; Male; Medical and Health Sciences; Medical imaging; Medicin och hälsovetenskap; Medicine and Health Sciences; Natural Sciences; Naturvetenskap; Neuroimaging; NMR; Nuclear magnetic resonance; Other Physics Topics; Physical Sciences; Radiologi och bildbehandling; Radiology and Medical Imaging; Reproducibility; Research and Analysis Methods; Signal-To-Noise Ratio; Spinal cord; Tensors; Tissues; Variance; Waveforms; France; Sweden
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0214238

Microstructure imaging techniques based on tensor-valued diffusion encoding have gained popularity within the MRI research community. Unlike conventional diffusion encoding-applied along a single direction in each shot-tensor-valued encoding employs diffusion encoding along multiple directions within a single preparation of the signal. The benefit is that such encoding may probe tissue features that are not accessible by conventional encoding. For example, diffusional variance decomposition (DIVIDE) takes advantage of tensor-valued encoding to probe microscopic diffusion anisotropy independent of orientation coherence. The drawback is that tensor-valued encoding generally requires gradient waveforms that are more demanding on hardware; it has therefore been used primarily in MRI systems with relatively high performance. The purpose of this work was to explore tensor-valued diffusion encoding on clinical MRI systems with varying performance to test its technical feasibility within the context of DIVIDE. We performed whole-brain imaging with linear and spherical b-tensor encoding at field strengths between 1.5 and 7 T, and at maximal gradient amplitudes between 45 and 80 mT/m. Asymmetric gradient waveforms were optimized numerically to yield b-values up to 2 ms/μm2. Technical feasibility was assessed in terms of the repeatability, SNR, and quality of DIVIDE parameter maps. Variable system performance resulted in echo times between 83 to 115 ms and total acquisition times of 6 to 9 minutes when using 80 signal samples and resolution 2×2×4 mm3. As expected, the repeatability, signal-to-noise ratio and parameter map quality depended on hardware performance. We conclude that tensor-valued encoding is feasible for a wide range of MRI systems-even at 1.5 T with maximal gradient waveform amplitudes of 33 mT/m-and baseline experimental design and quality parameters for all included configurations. This demonstrates that tissue features, beyond those accessible by conventional diffusion encoding, can be explored on a wide range of MRI systems.