TY - JOUR
T1 - Accelerating compressed sensing reconstruction of subsampled radial k-space data using geometrically-derived density compensation
AU - Hong, Kyung Pyo
AU - Schiffers, Florian
AU - DiCarlo, Amanda L.
AU - Rigsby, Cynthia K.
AU - Haji-Valizadeh, Hassan
AU - Lee, Daniel C.
AU - Markl, Michael
AU - Katsaggelos, Aggelos K.
AU - Kim, Daniel
N1 - Funding Information:
The authors are grateful for funding support from the National Institutes of Health (R01HL116895, R01HL138578, R21EB024315, R21AG055954, R01HL151079, 1R21EB030806-01A1) and American Heart Association (19IPLOI34760317). Dr Hassan Haji-valizadeh contributed to this work while as a Northwestern University employee but is now a Canon Medical Research USA employee. We thank Dr Sebastian Rosenzweig for assisting us with the RING method.
Publisher Copyright:
© 2021 Institute of Physics and Engineering in Medicine
PY - 2021/11/7
Y1 - 2021/11/7
N2 - Objective. To accelerate compressed sensing (CS) reconstruction of subsampled radial k-space data using a geometrically-derived density compensation function (gDCF) without significant loss in image quality. Approach. We developed a theoretical framework to calculate a gDCF based on Nyquist distance along the radial and circumferential directions of a discrete polar coordinate system. Our gDCF was compared against standard DCF (e.g. ramp filter) and another commonly used DCF (modified Shepp-Logan (SL) filter). The resulting image quality produced by each DCF was quantified using normalized root-mean-square-error (NRMSE), blur metric (1 = blurriest; 0 = sharpest), and structural similarity index (SSIM; 1 = perfect match; 0 = no match) compared with the reference. For filtered backprojection (FBP) of phantom data obtained at the Nyquist sampling rate, Cartesian k-space sampling was used as the reference. For CS reconstruction of subsampled cardiac magnetic resonance imaging datasets (real-time cardiac cine data with 11 projections per frame, n = 20 patients; cardiac perfusion data with 30 projections per frame, n = 19 patients), CS reconstruction without DCF was used as the reference. Main results. The NRMSE, SSIM, and blur metrics of the phantom data were good for all DCFs, confirming that our gDCF produces uniform densities at the upper limit (Nyquist). For CS reconstruction of subsampled real-time cine and cardiac perfusion datasets, the image quality metrics (SSIM, NRMSE) were significantly (p < 0.05) higher for our gDCF than other DCFs, and the reconstruction time was significantly (p < 0.05) faster for our gDCF (reference) than no DCF (11.9%-52.9% slower), standard DCF (23.9%-57.6% slower), and modified SL filter (13.5%-34.8% slower). Significance. The proposed gDCF accelerates CS reconstruction of subsampled radial k-space data without significant loss in image quality compared with no DCF as the reference.
AB - Objective. To accelerate compressed sensing (CS) reconstruction of subsampled radial k-space data using a geometrically-derived density compensation function (gDCF) without significant loss in image quality. Approach. We developed a theoretical framework to calculate a gDCF based on Nyquist distance along the radial and circumferential directions of a discrete polar coordinate system. Our gDCF was compared against standard DCF (e.g. ramp filter) and another commonly used DCF (modified Shepp-Logan (SL) filter). The resulting image quality produced by each DCF was quantified using normalized root-mean-square-error (NRMSE), blur metric (1 = blurriest; 0 = sharpest), and structural similarity index (SSIM; 1 = perfect match; 0 = no match) compared with the reference. For filtered backprojection (FBP) of phantom data obtained at the Nyquist sampling rate, Cartesian k-space sampling was used as the reference. For CS reconstruction of subsampled cardiac magnetic resonance imaging datasets (real-time cardiac cine data with 11 projections per frame, n = 20 patients; cardiac perfusion data with 30 projections per frame, n = 19 patients), CS reconstruction without DCF was used as the reference. Main results. The NRMSE, SSIM, and blur metrics of the phantom data were good for all DCFs, confirming that our gDCF produces uniform densities at the upper limit (Nyquist). For CS reconstruction of subsampled real-time cine and cardiac perfusion datasets, the image quality metrics (SSIM, NRMSE) were significantly (p < 0.05) higher for our gDCF than other DCFs, and the reconstruction time was significantly (p < 0.05) faster for our gDCF (reference) than no DCF (11.9%-52.9% slower), standard DCF (23.9%-57.6% slower), and modified SL filter (13.5%-34.8% slower). Significance. The proposed gDCF accelerates CS reconstruction of subsampled radial k-space data without significant loss in image quality compared with no DCF as the reference.
KW - Compressed sensing
KW - Density compensation
KW - Filtered backprojection
KW - Image reconstruction
KW - MRI
KW - Radial k-space sampling
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U2 - 10.1088/1361-6560/ac2c9d
DO - 10.1088/1361-6560/ac2c9d
M3 - Article
C2 - 34607316
AN - SCOPUS:85118246740
SN - 0031-9155
VL - 66
JO - Physics in Medicine and Biology
JF - Physics in Medicine and Biology
IS - 21
M1 - 21NT01
ER -