Abstract
Compressed sensing (CS) can accelerate magnetic resonance spectroscopic imaging (MRSI), facilitating its widespread clinical integration. The objective of this study was to assess the effect of different undersampling strategy on CS-MRSI reconstruction quality. Phantom data were acquired on a Philips 3 T Ingenia scanner. Four types of undersampling masks, corresponding to each strategy, namely, low resolution, variable density, iterative design, and a priori were simulated in Matlab and retrospectively applied to the test 1X MRSI data to generate undersampled datasets corresponding to the 2X - 5X, and 7X accelerations for each type of mask. Reconstruction parameters were kept the same in each case(all masks and accelerations) to ensure that any resulting differences can be attributed to the type of mask being employed. The reconstructed datasets from each mask were statistically compared with the reference 1X, and assessed using metrics like the root mean square error and metabolite ratios. Simulation results indicate that both the a priori and variable density undersampling masks maintain high fidelity with the 1X up to five-fold acceleration. The low resolution mask based reconstructions showed statistically significant differences from the 1X with the reconstruction failing at 3X, while the iterative design reconstructions maintained fidelity with the 1X till 4X acceleration. In summary, a pilot study was conducted to identify an optimal sampling mask in CS-MRSI. Simulation results demonstrate that the a priori and variable density masks can provide statistically similar results to the fully sampled reference. Future work would involve implementing these two masks prospectively on a clinical scanner.
| Original language | English (US) |
|---|---|
| Title of host publication | Medical Imaging 2017 |
| Subtitle of host publication | Biomedical Applications in Molecular, Structural, and Functional Imaging |
| Publisher | SPIE |
| Volume | 10137 |
| ISBN (Electronic) | 9781510607194 |
| DOIs | |
| State | Published - 2017 |
| Event | Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging - Orlando, United States Duration: Feb 12 2017 → Feb 14 2017 |
Other
| Other | Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging |
|---|---|
| Country | United States |
| City | Orlando |
| Period | 2/12/17 → 2/14/17 |
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Keywords
- Compressed sensing
- Fast imaging
- Metabolic imaging
- MRSI
- Optimal sampling mask
- Undersampling
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics
- Electronic, Optical and Magnetic Materials
- Biomaterials
- Radiology Nuclear Medicine and imaging
Cite this
Undersampling strategies for compressed sensing accelerated MR spectroscopic imaging. / Vidya Shankar, Rohini; Hu, Houchun Harry; Bikkamane Jayadev, Nutandev; Chang, John C.; Kodibagkar, Vikram.
Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging. Vol. 10137 SPIE, 2017. 101372I.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Undersampling strategies for compressed sensing accelerated MR spectroscopic imaging
AU - Vidya Shankar, Rohini
AU - Hu, Houchun Harry
AU - Bikkamane Jayadev, Nutandev
AU - Chang, John C.
AU - Kodibagkar, Vikram
PY - 2017
Y1 - 2017
N2 - Compressed sensing (CS) can accelerate magnetic resonance spectroscopic imaging (MRSI), facilitating its widespread clinical integration. The objective of this study was to assess the effect of different undersampling strategy on CS-MRSI reconstruction quality. Phantom data were acquired on a Philips 3 T Ingenia scanner. Four types of undersampling masks, corresponding to each strategy, namely, low resolution, variable density, iterative design, and a priori were simulated in Matlab and retrospectively applied to the test 1X MRSI data to generate undersampled datasets corresponding to the 2X - 5X, and 7X accelerations for each type of mask. Reconstruction parameters were kept the same in each case(all masks and accelerations) to ensure that any resulting differences can be attributed to the type of mask being employed. The reconstructed datasets from each mask were statistically compared with the reference 1X, and assessed using metrics like the root mean square error and metabolite ratios. Simulation results indicate that both the a priori and variable density undersampling masks maintain high fidelity with the 1X up to five-fold acceleration. The low resolution mask based reconstructions showed statistically significant differences from the 1X with the reconstruction failing at 3X, while the iterative design reconstructions maintained fidelity with the 1X till 4X acceleration. In summary, a pilot study was conducted to identify an optimal sampling mask in CS-MRSI. Simulation results demonstrate that the a priori and variable density masks can provide statistically similar results to the fully sampled reference. Future work would involve implementing these two masks prospectively on a clinical scanner.
AB - Compressed sensing (CS) can accelerate magnetic resonance spectroscopic imaging (MRSI), facilitating its widespread clinical integration. The objective of this study was to assess the effect of different undersampling strategy on CS-MRSI reconstruction quality. Phantom data were acquired on a Philips 3 T Ingenia scanner. Four types of undersampling masks, corresponding to each strategy, namely, low resolution, variable density, iterative design, and a priori were simulated in Matlab and retrospectively applied to the test 1X MRSI data to generate undersampled datasets corresponding to the 2X - 5X, and 7X accelerations for each type of mask. Reconstruction parameters were kept the same in each case(all masks and accelerations) to ensure that any resulting differences can be attributed to the type of mask being employed. The reconstructed datasets from each mask were statistically compared with the reference 1X, and assessed using metrics like the root mean square error and metabolite ratios. Simulation results indicate that both the a priori and variable density undersampling masks maintain high fidelity with the 1X up to five-fold acceleration. The low resolution mask based reconstructions showed statistically significant differences from the 1X with the reconstruction failing at 3X, while the iterative design reconstructions maintained fidelity with the 1X till 4X acceleration. In summary, a pilot study was conducted to identify an optimal sampling mask in CS-MRSI. Simulation results demonstrate that the a priori and variable density masks can provide statistically similar results to the fully sampled reference. Future work would involve implementing these two masks prospectively on a clinical scanner.
KW - Compressed sensing
KW - Fast imaging
KW - Metabolic imaging
KW - MRSI
KW - Optimal sampling mask
KW - Undersampling
UR - http://www.scopus.com/inward/record.url?scp=85020260611&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020260611&partnerID=8YFLogxK
U2 - 10.1117/12.2254614
DO - 10.1117/12.2254614
M3 - Conference contribution
AN - SCOPUS:85020260611
VL - 10137
BT - Medical Imaging 2017
PB - SPIE
ER -