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Objective . Diffusion tensor imaging (DTI) is excellent for non-invasively quantifying tissue microstructure. Theoretically DTI can be achieved with six different diffusion weighted images and one reference image, but the tensor estimation accuracy is poor in this case. Increasing the number of diffusion directions has benefits for the tensor estimation accuracy, which results in long scan time and makes DTI sensitive to motion. It would be beneficial to decrease the scan time of DTI by using fewer diffusion-weighted images without compromising reconstruction quality. Approach . A novel DTI scan scheme was proposed to achieve fast DTI, where only three diffusion directions per slice was required under a specific direction switching manner, and a deep-learning based reconstruction method was utilized using multi-slice information sharing and corresponding T 1 -weighted image for high-quality DTI reconstruction. A network with two encoders developed from U-Net was implemented for better utilizing the diffusion data redundancy between neighboring slices. The method performed direct nonlinear mapping from diffusion-weighted images to diffusion tensor. Main results . The performance of the proposed method was verified on the Human Connectome Project public data and clinical patient data. High-quality mean diffusivity, fractional anisotropy, and directionally encoded colormap can be achieved with only three diffusion directions per slice. Significance . High-quality DTI-derived maps can be achieved in less than one minute of scan time. The great reduction of scan time will help push the wider application of DTI in clinical practice., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective . Most quantitative magnetic resonance imaging (qMRI) methods are time-consuming. Multiple overlapping-echo detachment (MOLED) imaging can achieve quantitative parametric mapping of a single slice within around one hundred milliseconds. Nevertheless, imaging the whole brain, which involves multiple slices, still takes a few seconds. To further accelerate qMRI, we introduce multiband SENSE (MB-SENSE) technology to MOLED to realize simultaneous multi-slice T 2 mapping. Approach. The multiband MOLED (MB-MOLED) pulse sequence was carried out to acquire raw overlapping-echo signals, and deep learning was utilized to reconstruct T 2 maps. To address the issue of image quality degradation due to a high multiband factor MB, a plug-and-play (PnP) algorithm with prior denoisers (DRUNet) was applied. U-Net was used for T 2 map reconstruction. Numerical simulations, water phantom experiments and human brain experiments were conducted to validate our proposed approach. Main results. Numerical simulations show that PnP algorithm effectively improved the quality of reconstructed T 2 maps at low signal-to-noise ratios. Water phantom experiments indicate that MB-MOLED inherited the advantages of MOLED and its results were in good agreement with the results of reference method. In vivo experiments for MB = 1, 2, 4 without the PnP algorithm, and 4 with PnP algorithm indicate that the use of PnP algorithm improved the quality of reconstructed T 2 maps at a high MB. For the first time, with MB = 4, T 2 mapping of the whole brain was achieved within 600 ms. Significance. MOLED and MB-SENSE can be combined effectively. This method enables sub-second T 2 mapping of the whole brain. The PnP algorithm can improve the quality of reconstructed T 2 maps. The novel approach shows significant promise in applications necessitating high temporal resolution, such as functional and dynamic qMRI., (© 2023 Institute of Physics and Engineering in Medicine.)