Multi‐band‐ and in‐plane‐accelerated diffusion MRI enabled by model‐based deep learning in q‐space and its extension to learning in the spherical harmonic domain

2021 ◽  
Author(s):  
Merry Mani ◽  
Baolian Yang ◽  
Girish Bathla ◽  
Vincent Magnotta ◽  
Mathews Jacob
Keyword(s):  
Deep Learning ◽  
Spherical Harmonic ◽  
Diffusion Mri ◽  
Model Based ◽  
Multi Band

scholarly journals MoDL-MUSSELS: Model-Based Deep Learning for Multishot Sensitivity-Encoded Diffusion MRI

2020 ◽  
Vol 39 (4) ◽  
pp. 1268-1277 ◽  
Author(s):  
Hemant K. Aggarwal ◽  
Merry P. Mani ◽  
Mathews Jacob
Keyword(s):  
Deep Learning ◽  
Diffusion Mri ◽  
Model Based

scholarly journals Deep learning–based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms

2021 ◽  
Author(s):  
Luuk J. Oostveen ◽  
Frederick J. A. Meijer ◽  
Frank de Lange ◽  
Ewoud J. Smit ◽  
Sjoert A. Pegge ◽  
...  
Keyword(s):  
Deep Learning ◽  
White Matter ◽  
Image Quality ◽  
Iterative Reconstruction ◽  
Quality Characteristics ◽  
Model Based ◽  
Hybrid Iterative Reconstruction ◽  
Contrast Ct ◽  
Lower Noise ◽  
Reconstruction Time

Abstract Objectives To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT). Methods Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired t tests. Results For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively. Conclusions With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times. Key Points • Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction. • Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction. • Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.

Accelerated model-based quantitative diffusion MRI: A feasibility study for musculoskeletal application

2021 ◽  
Author(s):  
Thomas Hüfken ◽  
Jannik M. Arbogast ◽  
Anna-Katinka Bracher ◽  
Meinrad Beer ◽  
Henning Neubauer ◽  
...  
Keyword(s):  
Feasibility Study ◽  
Diffusion Mri ◽  
Model Based
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