Deep learning for radial SMS myocardial perfusion reconstruction using the 3D residual booster U-net

Le, Johnathan; Tian, Ye; Mendes, Jason; Wilson, Brent D.; Ibrahim, Mark; DiBella, Edward; Adluru, Ganesh

doi:10.1016/j.mri.2021.08.007

Cited by 15 publications

(13 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DL reconstruction has gained interest in perfusion CMR, but has been limited to data‐driven image enhancement approaches that learn a mapping between aliased and artifact‐free images. 66 , 67 , 68 PG‐DL approaches, which have been shown to outperform image enhancement methods 62 , 87 , 88 have remained elusive for perfusion CMR. One of the main challenges for PG‐DL techniques has been related to generalizability with SNR changes, 64 limiting the use of such reconstructions across perfusion time‐frames, which is the main issue tackled in this study.…”

Section: Discussionmentioning

confidence: 99%

“…Another challenge for DL reconstruction in perfusion CMR has been the lack of gold‐standard reference data. Aforementioned data‐driven DL methods 66 , 67 , 68 were trained using supervision with compressed sensing reconstructions, limiting the performance of DL reconstruction. On the other hand, PG‐DL methods enable self‐supervised training from undersampled k‐space data only, 61 , 78 , 80 , 89 , 90 without a reference image.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“… 64 An alternative way to train PG‐DL reconstruction for perfusion CMR would be using a spatio‐temporal network, yet this has its own challenges including memory limitations 65 and difficulty of procuring high‐quality training databases due to differences in contrast uptakes/breathing patterns among subjects. Thus, application of PG‐DL reconstruction to perfusion CMR has been difficult, and existing DL methods for perfusion CMR reconstruction have been limited to data‐driven image enhancement networks, 66 , 67 , 68 which are trained in a supervised manner using conventional compressed sensing reconstruction outputs as reference images. While this line of work improves reconstruction speed, the reconstruction quality is inherently limited by the conventional reconstruction used as reference for supervised training, which in turn hinders the true potential of DL reconstruction for perfusion CMR.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Signal intensity informed multi‐coil encoding operator for physics‐guided deep learning reconstruction of highly accelerated myocardial perfusion CMR

Demirel

Shenoy

Moeller

et al. 2022

Magnetic Resonance in Med

View full text Add to dashboard Cite

To develop a physics-guided deep learning (PG-DL) reconstruction strategy based on a signal intensity informed multi-coil (SIIM) encoding operator for highly-accelerated simultaneous multislice (SMS) myocardial perfusion cardiac MRI (CMR). Methods: First-pass perfusion CMR acquires highly-accelerated images with dynamically varying signal intensity/SNR following the administration of a gadolinium-based contrast agent. Thus, using PG-DL reconstruction with a conventional multi-coil encoding operator leads to analogous signal intensity variations across different time-frames at the network output, creating difficulties in generalization for varying SNR levels. We propose to use a SIIM encoding operator to capture the signal intensity/SNR variations across time-frames in a reformulated encoding operator. This leads to a more uniform/flat contrast at the output of the PG-DL network, facilitating generalizability across time-frames. PG-DL reconstruction with the proposed SIIM encoding operator is compared to PG-DL with conventional encoding operator, split slice-GRAPPA, locally low-rank (LLR) regularized reconstruction, low-rank plus sparse (L + S) reconstruction, and regularized ROCK-SPIRiT. Results: Results on highly accelerated free-breathing first pass myocardial perfusion CMR at three-fold SMS and four-fold in-plane acceleration show that the proposed method improves upon the reconstruction methods use for comparison. Substantial noise reduction is achieved compared to split slice-GRAPPA, and aliasing artifacts reduction compared to LLR regularized reconstruction, L + S reconstruction and PG-DL with conventional encoding. Furthermore, a qualitative reader study indicated that proposed method outperformed all methods. Conclusion: PG-DL reconstruction with the proposed SIIM encoding operator improves generalization across different time-frames /SNRs in highly accelerated perfusion CMR.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Signal intensity informed multi‐coil encoding operator for physics‐guided deep learning reconstruction of highly accelerated myocardial perfusion CMR

Demirel

Shenoy

Moeller

et al. 2022

Magnetic Resonance in Med

View full text Add to dashboard Cite

show abstract

“…Using deep convolutional neural networks (CNN) for CMR image reconstruction not only boosts reconstruction speed and simplifies parameter tuning, but also maintains high image quality. Recent advances in CMR image reconstruction using CNN [11][12][13][14][15][16][17][18][19][20][21][22][23][24] have been proposed, but most of the works were based on Cartesian imaging. Fan et al 11 and Hauptmann et al 12 demonstrated dynamic radial CMR image reconstruction using 3D U-Net-based networks.…”

mentioning

confidence: 99%

DEep learning‐based rapid Spiral Image REconstruction (DESIRE) for high‐resolution spiral first‐pass myocardial perfusion imaging

et al. 2021

View full text Add to dashboard Cite

The objective of the current study was to develop and evaluate a DEep learning‐based rapid Spiral Image REconstruction (DESIRE) technique for high‐resolution spiral first‐pass myocardial perfusion imaging with whole‐heart coverage, to provide fast and accurate image reconstruction for both single‐slice (SS) and simultaneous multislice (SMS) acquisitions. Three‐dimensional U‐Net–based image enhancement architectures were evaluated for high‐resolution spiral perfusion imaging at 3 T. The SS and SMS MB = 2 networks were trained on SS perfusion images from 156 slices from 20 subjects. Structural similarity index (SSIM), peak signal‐to‐noise ratio (PSNR), and normalized root mean square error (NRMSE) were assessed, and prospective images were blindly graded by two experienced cardiologists (5: excellent; 1: poor). Excellent performance was demonstrated for the proposed technique. For SS, SSIM, PSNR, and NRMSE were 0.977 [0.972, 0.982], 42.113 [40.174, 43.493] dB, and 0.102 [0.080, 0.125], respectively, for the best network. For SMS MB = 2 retrospective data, SSIM, PSNR, and NRMSE were 0.961 [0.950, 0.969], 40.834 [39.619, 42.004] dB, and 0.107 [0.086, 0.133], respectively, for the best network. The image quality scores were 4.5 [4.1, 4.8], 4.5 [4.3, 4.6], 3.5 [3.3, 4], and 3.5 [3.3, 3.8] for SS DESIRE, SS L1‐SPIRiT, MB = 2 DESIRE, and MB = 2 SMS‐slice‐L1‐SPIRiT, respectively, showing no statistically significant difference (p = 1 and p = 1 for SS and SMS, respectively) between L1‐SPIRiT and the proposed DESIRE technique. The network inference time was ~100 ms per dynamic perfusion series with DESIRE, while the reconstruction time of L1‐SPIRiT with GPU acceleration was ~ 30 min. It was concluded that DESIRE enabled fast and high‐quality image reconstruction for both SS and SMS MB = 2 whole‐heart high‐resolution spiral perfusion imaging.

show abstract

Deep learning‐based rapid image reconstruction and motion correction for high‐resolution cartesian first‐pass myocardial perfusion imaging at 3T

Wang,

Salerno

2024

Magnetic Resonance in Med

View full text Add to dashboard Cite

PurposeTo develop and evaluate a deep learning (DL) ‐based rapid image reconstruction and motion correction technique for high‐resolution Cartesian first‐pass myocardial perfusion imaging at 3T with whole‐heart coverage for both single‐slice (SS) and simultaneous multi‐slice (SMS) acquisitions.Methods3D physics‐driven unrolled network architectures were utilized for the reconstruction of high‐resolution Cartesian perfusion imaging. The SS and SMS multiband (MB) = 2 networks were trained from 135 slices from 20 subjects. Structural similarity index (SSIM), peak SNR (PSNR), and normalized RMS error (NRMSE) were assessed, and prospective images were blindly graded by two experienced cardiologists (5, excellent; 1, poor). For respiratory motion correction, a 2D U‐Net based motion corrected network was proposed, and the temporal fidelity and second‐order derivative were calculated to assess the performance of the motion correction.ResultsExcellent performance was demonstrated in the proposed technique with high SSIM and PSNR, and low NRMSE. Image quality scores were (4.3 [4.3, 4.4], 4.5 [4.4, 4.6], 4.3 [4.3, 4.4], and 4.5 [4.3, 4.5]) for SS DL and SS L1‐SENSE, MB = 2 DL and MB = 2 SMS‐L1‐SENSE, respectively, showing no statistically significant difference (p > 0.05 for SS and SMS) between (SMS)‐L1‐SENSE and the proposed DL technique. The network inference time was around 4 s per dynamic perfusion series with 40 frames while the time of (SMS)‐L1‐SENSE with GPU acceleration was approximately 30 min.ConclusionThe proposed DL‐based image reconstruction and motion correction technique enabled rapid and high‐quality reconstruction for SS and SMS MB = 2 high‐resolution Cartesian first‐pass perfusion imaging at 3T.

show abstract

Deep learning for radial SMS myocardial perfusion reconstruction using the 3D residual booster U-net

Cited by 15 publications

References 27 publications

Signal intensity informed multi‐coil encoding operator for physics‐guided deep learning reconstruction of highly accelerated myocardial perfusion CMR

Signal intensity informed multi‐coil encoding operator for physics‐guided deep learning reconstruction of highly accelerated myocardial perfusion CMR

DEep learning‐based rapid Spiral Image REconstruction (DESIRE) for high‐resolution spiral first‐pass myocardial perfusion imaging

Deep learning‐based rapid image reconstruction and motion correction for high‐resolution cartesian first‐pass myocardial perfusion imaging at 3T

Contact Info

Product

Resources

About