Sagar Mandava1,2, Mahesh B Keerthivasan1,2, Zhitao Li1,2, Diego R Martin2, Maria I Altbach2,3, Ali Bilgin1,2,3. 1. Department of Electrical and Computer Engineering, University of Arizona, Tucson, Arizona. 2. Department of Medical Imaging, University of Arizona, Tucson, Arizona. 3. Department of Biomedical Engineering, University of Arizona, Tucson, Arizona.
Abstract
PURPOSE: A new reconstruction method for multi-contrast imaging and parameter mapping based on a union of local subspaces constraint is presented. THEORY: Subspace constrained reconstructions use a predetermined subspace to explicitly constrain the relaxation signals. The choice of subspace size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:mrow><mml:mo>(</mml:mo> <mml:mi>K</mml:mi> <mml:mo>)</mml:mo></mml:mrow> </mml:mrow> </mml:math> impacts the approximation error vs noise-amplification tradeoff associated with these methods. A different approach is used in the model consistency constraint (MOCCO) framework to leverage the subspace model to enforce a softer penalty. Our proposed method, MOCCO-LS, augments the MOCCO model with a union of local subspaces (LS) approach. The union of local subspaces model is coupled with spatial support constraints and incorporated into the MOCCO framework to regularize the contrast signals in the scene. METHODS: The performance of the MOCCO-LS method was evaluated in vivo on T1 and T2 mapping of the human brain and with Monte-Carlo simulations and compared against MOCCO and the explicit subspace constrained models. RESULTS: The results demonstrate a clear improvement in the multi-contrast images and parameter maps. We sweep across the model order space <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:mrow><mml:mo>(</mml:mo> <mml:mi>K</mml:mi> <mml:mo>)</mml:mo></mml:mrow> </mml:mrow> </mml:math> to compare the different reconstructions and demonstrate that the reconstructions have different preferential operating points. Experiments on T2 mapping show that the proposed method yields substantial improvements in performance even when operating at very high acceleration rates. CONCLUSIONS: The use of a union of local subspace constraints coupled with a sparsity promoting penalty leads to improved reconstruction quality of multi-contrast images and parameter maps.
PURPOSE: A new reconstruction method for multi-contrast imaging and parameter mapping based on a union of local subspaces constraint is presented. THEORY: Subspace constrained reconstructions use a predetermined subspace to explicitly constrain the relaxation signals. The choice of subspace size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:mrow><mml:mo>(</mml:mo> <mml:mi>K</mml:mi> <mml:mo>)</mml:mo></mml:mrow> </mml:mrow> </mml:math> impacts the approximation error vs noise-amplification tradeoff associated with these methods. A different approach is used in the model consistency constraint (MOCCO) framework to leverage the subspace model to enforce a softer penalty. Our proposed method, MOCCO-LS, augments the MOCCO model with a union of local subspaces (LS) approach. The union of local subspaces model is coupled with spatial support constraints and incorporated into the MOCCO framework to regularize the contrast signals in the scene. METHODS: The performance of the MOCCO-LS method was evaluated in vivo on T1 and T2 mapping of the human brain and with Monte-Carlo simulations and compared against MOCCO and the explicit subspace constrained models. RESULTS: The results demonstrate a clear improvement in the multi-contrast images and parameter maps. We sweep across the model order space <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:mrow><mml:mo>(</mml:mo> <mml:mi>K</mml:mi> <mml:mo>)</mml:mo></mml:mrow> </mml:mrow> </mml:math> to compare the different reconstructions and demonstrate that the reconstructions have different preferential operating points. Experiments on T2 mapping show that the proposed method yields substantial improvements in performance even when operating at very high acceleration rates. CONCLUSIONS: The use of a union of local subspace constraints coupled with a sparsity promoting penalty leads to improved reconstruction quality of multi-contrast images and parameter maps.
Authors: Zhiyang Fu; Sagar Mandava; Mahesh B Keerthivasan; Zhitao Li; Kevin Johnson; Diego R Martin; Maria I Altbach; Ali Bilgin Journal: Magn Reson Imaging Date: 2020-09-01 Impact factor: 2.546
Authors: Sagar Mandava; Mahesh B Keerthivasan; Diego R Martin; Maria I Altbach; Ali Bilgin Journal: Phys Med Biol Date: 2021-02-11 Impact factor: 3.609