Ziwu Zhou1,2, Fei Han1,2, Songlin Yu3,4, Dandan Yu3, Stanislas Rapacchi1, Hee Kwon Song5, Danny J J Wang6, Peng Hu1,2, Lirong Yan6. 1. Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA. 2. Department of Bioengineering, University of California, Los Angeles, California, USA. 3. Department of Neurology, University of California, Los Angeles, California, USA. 4. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. 5. Department of Radiology, University of Pennsylvania Medical Center, Philadelphia, Pennsylvania, USA. 6. Laboratory of Functional MRI Technology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Abstract
PURPOSE: To evaluate the feasibility and performance of compressed sensing (CS) with magnitude subtraction regularization in accelerating non-contrast-enhanced dynamic intracranial MR angiography (NCE-dMRA). METHODS: A CS algorithm was introduced in NCE-dMRA by exploiting the sparsity of the magnitude difference of the control and label images. The NCE-dMRA data were acquired using golden-angle stack-of-stars trajectory on six healthy volunteers and one patient with arteriovenous fistula. Images were reconstructed using (i) the proposed magnitude-subtraction CS (MS-CS); (ii) complex-subtraction CS; (iii) independent CS; and (iv) view-sharing with k-space weighted image contrast (KWIC). The dMRA image quality was compared across the four reconstruction strategies. The proposed MS-CS method was further compared with KWIC for temporal fidelity of depicting dynamic flow. RESULTS: The proposed MS-CS method was able to reconstruct NCE-dMRA images with detailed vascular structures and clean background. It provided better subjective image quality than the other two CS strategies (P < 0.05). Compared with KWIC, MS-CS showed similar image quality, but reduced temporal blurring in delineating the fine distal arteries. CONCLUSIONS: The MS-CS method is a promising CS technique for accelerating NCE-dMRA acquisition without compromising image quality and temporal fidelity. Magn Reson Med 79:867-878, 2018.
PURPOSE: To evaluate the feasibility and performance of compressed sensing (CS) with magnitude subtraction regularization in accelerating non-contrast-enhanced dynamic intracranial MR angiography (NCE-dMRA). METHODS: A CS algorithm was introduced in NCE-dMRA by exploiting the sparsity of the magnitude difference of the control and label images. The NCE-dMRA data were acquired using golden-angle stack-of-stars trajectory on six healthy volunteers and one patient with arteriovenous fistula. Images were reconstructed using (i) the proposed magnitude-subtraction CS (MS-CS); (ii) complex-subtraction CS; (iii) independent CS; and (iv) view-sharing with k-space weighted image contrast (KWIC). The dMRA image quality was compared across the four reconstruction strategies. The proposed MS-CS method was further compared with KWIC for temporal fidelity of depicting dynamic flow. RESULTS: The proposed MS-CS method was able to reconstruct NCE-dMRA images with detailed vascular structures and clean background. It provided better subjective image quality than the other two CS strategies (P < 0.05). Compared with KWIC, MS-CS showed similar image quality, but reduced temporal blurring in delineating the fine distal arteries. CONCLUSIONS: The MS-CS method is a promising CS technique for accelerating NCE-dMRA acquisition without compromising image quality and temporal fidelity. Magn Reson Med 79:867-878, 2018.
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