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Published in: European Radiology 12/2022

Open Access 25-06-2022 | Musculoskeletal

Deep learning–based acceleration of Compressed Sense MR imaging of the ankle

Authors: Sarah C. Foreman, Jan Neumann, Jessie Han, Norbert Harrasser, Kilian Weiss, Johannes M. Peeters, Dimitrios C. Karampinos, Marcus R. Makowski, Alexandra S. Gersing, Klaus Woertler

Published in: European Radiology | Issue 12/2022

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Abstract

Objectives

To evaluate a compressed sensing artificial intelligence framework (CSAI) to accelerate MRI acquisition of the ankle.

Methods

Thirty patients were scanned at 3T. Axial T2-w, coronal T1-w, and coronal/sagittal intermediate-w scans with fat saturation were acquired using compressed sensing only (12:44 min, CS), CSAI with an acceleration factor of 4.6–5.3 (6:45 min, CSAI2x), and CSAI with an acceleration factor of 6.9–7.7 (4:46 min, CSAI3x). Moreover, a high-resolution axial T2-w scan was obtained using CSAI with a similar scan duration compared to CS. Depiction and presence of abnormalities were graded. Signal-to-noise and contrast-to-noise were calculated. Wilcoxon signed-rank test and Cohen’s kappa were used to compare CSAI with CS sequences.

Results

The correlation was perfect between CS and CSAI2x (κ = 1.0) and excellent for CS and CSAI3x (κ = 0.86–1.0). No significant differences were found for the depiction of structures between CS and CSAI2x and the same abnormalities were detected in both protocols. For CSAI3x the depiction was graded lower (p ≤ 0.001), though most abnormalities were also detected. For CSAI2x contrast-to-noise fluid/muscle was higher compared to CS (p ≤ 0.05), while no differences were found for other tissues. Signal-to-noise and contrast-to-noise were higher for CSAI3x compared to CS (p ≤ 0.05). The high - resolution axial T2-w sequence specifically improved the depiction of tendons and the tibial nerve (p ≤ 0.005).

Conclusions

Acquisition times can be reduced by 47% using CSAI compared to CS without decreasing diagnostic image quality. Reducing acquisition times by 63% is feasible but should be reserved for specific patients. The depiction of specific structures is improved using a high-resolution axial T2-w CSAI scan.

Key Points

• Prospective study showed that CSAI enables reduction in acquisition times by 47% without decreasing diagnostic image quality.
• Reducing acquisition times by 63% still produces images with an acceptable diagnostic accuracy but should be reserved for specific patients.
• CSAI may be implemented to scan at a higher resolution compared to standard CS images without increasing acquisition times.
Literature
1.
go back to reference Rosenberg ZS, Beltran J, Bencardino JT (2000) From the RSNA Refresher Courses. Radiological Society of North America. MR imaging of the ankle and foot. Radiographics 20 Spec No:S153-179 Rosenberg ZS, Beltran J, Bencardino JT (2000) From the RSNA Refresher Courses. Radiological Society of North America. MR imaging of the ankle and foot. Radiographics 20 Spec No:S153-179
2.
go back to reference Gehrmann RM, Rajan S, Patel DV, Bibbo C (2005) Athletes' ankle injuries: diagnosis and management. Am J Orthop (Belle Mead NJ) 34:551–561PubMed Gehrmann RM, Rajan S, Patel DV, Bibbo C (2005) Athletes' ankle injuries: diagnosis and management. Am J Orthop (Belle Mead NJ) 34:551–561PubMed
3.
go back to reference Mengiardi B, Pfirrmann CW, Vienne P, Hodler J, Zanetti M (2007) Medial collateral ligament complex of the ankle: MR appearance in asymptomatic subjects. Radiology 242:817–824CrossRefPubMed Mengiardi B, Pfirrmann CW, Vienne P, Hodler J, Zanetti M (2007) Medial collateral ligament complex of the ankle: MR appearance in asymptomatic subjects. Radiology 242:817–824CrossRefPubMed
4.
go back to reference Alves T, Dong Q, Jacobson J, Yablon C, Gandikota G (2019) Normal and injured ankle ligaments on ultrasonography with magnetic resonance imaging correlation. J Ultrasound Med 38:513–528CrossRefPubMed Alves T, Dong Q, Jacobson J, Yablon C, Gandikota G (2019) Normal and injured ankle ligaments on ultrasonography with magnetic resonance imaging correlation. J Ultrasound Med 38:513–528CrossRefPubMed
5.
go back to reference Salat P, Le V, Veljkovic A, Cresswell ME (2018) Imaging in foot and ankle instability. Foot Ankle Clin 23(499-522):e428 Salat P, Le V, Veljkovic A, Cresswell ME (2018) Imaging in foot and ankle instability. Foot Ankle Clin 23(499-522):e428
6.
go back to reference Vega J, Malagelada F, Manzanares Cespedes MC, Dalmau-Pastor M (2020) The lateral fibulotalocalcaneal ligament complex: an ankle stabilizing isometric structure. Knee Surg Sports Traumatol Arthrosc 28:8–17CrossRefPubMed Vega J, Malagelada F, Manzanares Cespedes MC, Dalmau-Pastor M (2020) The lateral fibulotalocalcaneal ligament complex: an ankle stabilizing isometric structure. Knee Surg Sports Traumatol Arthrosc 28:8–17CrossRefPubMed
7.
go back to reference Holmer P, Sondergaard L, Konradsen L, Nielsen PT, Jorgensen LN (1994) Epidemiology of sprains in the lateral ankle and foot. Foot Ankle Int 15:72–74CrossRefPubMed Holmer P, Sondergaard L, Konradsen L, Nielsen PT, Jorgensen LN (1994) Epidemiology of sprains in the lateral ankle and foot. Foot Ankle Int 15:72–74CrossRefPubMed
10.
11.
go back to reference Geethanath S, Reddy R, Konar AS et al (2013) Compressed sensing MRI: a review. Crit Rev Biomed Eng 41:183–204CrossRefPubMed Geethanath S, Reddy R, Konar AS et al (2013) Compressed sensing MRI: a review. Crit Rev Biomed Eng 41:183–204CrossRefPubMed
12.
go back to reference Feng L, Benkert T, Block KT, Sodickson DK, Otazo R, Chandarana H (2017) Compressed sensing for body MRI. J Magn Reson Imaging 45:966–987CrossRefPubMed Feng L, Benkert T, Block KT, Sodickson DK, Otazo R, Chandarana H (2017) Compressed sensing for body MRI. J Magn Reson Imaging 45:966–987CrossRefPubMed
13.
go back to reference Gersing AS, Bodden J, Neumann J et al (2019) Accelerating anatomical 2D turbo spin echo imaging of the ankle using compressed sensing. Eur J Radiol 118:277–284CrossRefPubMed Gersing AS, Bodden J, Neumann J et al (2019) Accelerating anatomical 2D turbo spin echo imaging of the ankle using compressed sensing. Eur J Radiol 118:277–284CrossRefPubMed
14.
go back to reference Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P (1999) SENSE: sensitivity encoding for fast MRI. Magn Reson Med 42:952–962CrossRefPubMed Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P (1999) SENSE: sensitivity encoding for fast MRI. Magn Reson Med 42:952–962CrossRefPubMed
16.
go back to reference Hammernik K, Klatzer T, Kobler E et al (2018) Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med 79:3055–3071CrossRefPubMed Hammernik K, Klatzer T, Kobler E et al (2018) Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med 79:3055–3071CrossRefPubMed
17.
go back to reference Akcakaya M, Moeller S, Weingartner S, Ugurbil K (2019) Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: database-free deep learning for fast imaging. Magn Reson Med 81:439–453CrossRefPubMed Akcakaya M, Moeller S, Weingartner S, Ugurbil K (2019) Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: database-free deep learning for fast imaging. Magn Reson Med 81:439–453CrossRefPubMed
18.
go back to reference Arshad M, Qureshi M, Inam O, Omer H (2021) Transfer learning in deep neural network based under-sampled MR image reconstruction. Magn Reson Imaging 76:96–107CrossRefPubMed Arshad M, Qureshi M, Inam O, Omer H (2021) Transfer learning in deep neural network based under-sampled MR image reconstruction. Magn Reson Imaging 76:96–107CrossRefPubMed
19.
go back to reference Zhao D, Huang Y, Zhao F, Qin B, Zheng J (2021) Reference-driven undersampled MR image reconstruction using wavelet sparsity-constrained deep image prior. Comput Math Methods Med 2021:8865582CrossRefPubMedCentralPubMed Zhao D, Huang Y, Zhao F, Qin B, Zheng J (2021) Reference-driven undersampled MR image reconstruction using wavelet sparsity-constrained deep image prior. Comput Math Methods Med 2021:8865582CrossRefPubMedCentralPubMed
20.
go back to reference Herrmann J, Koerzdoerfer G, Nickel D et al (2021) Feasibility and implementation of a deep learning MR reconstruction for TSE sequences in musculoskeletal imaging. Diagnostics (Basel) 11 Herrmann J, Koerzdoerfer G, Nickel D et al (2021) Feasibility and implementation of a deep learning MR reconstruction for TSE sequences in musculoskeletal imaging. Diagnostics (Basel) 11
22.
go back to reference Zhang J, Ghanem B ( 2018) ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing,. EEE/CVF Conference on Computer Vision and Pattern Recognition. 10.1109:1828-1837 Zhang J, Ghanem B ( 2018) ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing,. EEE/CVF Conference on Computer Vision and Pattern Recognition. 10.1109:1828-1837
23.
go back to reference Notohamiprodjo M, Kuschel B, Horng A et al (2012) 3D-MRI of the ankle with optimized 3D-SPACE. Invest Radiol 47:231–239CrossRefPubMed Notohamiprodjo M, Kuschel B, Horng A et al (2012) 3D-MRI of the ankle with optimized 3D-SPACE. Invest Radiol 47:231–239CrossRefPubMed
24.
go back to reference Dietrich O, Raya JG, Reeder SB, Reiser MF, Schoenberg SO (2007) Measurement of signal-to-noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters. J Magn Reson Imaging 26:375–385CrossRefPubMed Dietrich O, Raya JG, Reeder SB, Reiser MF, Schoenberg SO (2007) Measurement of signal-to-noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters. J Magn Reson Imaging 26:375–385CrossRefPubMed
25.
go back to reference Schweitzer ME, Tran D, Deely DM, Hume EL (1995) Medial collateral ligament injuries: evaluation of multiple signs, prevalence and location of associated bone bruises, and assessment with MR imaging. Radiology 194:825–829CrossRefPubMed Schweitzer ME, Tran D, Deely DM, Hume EL (1995) Medial collateral ligament injuries: evaluation of multiple signs, prevalence and location of associated bone bruises, and assessment with MR imaging. Radiology 194:825–829CrossRefPubMed
28.
go back to reference Koonjoo N, Zhu B, Bagnall GC, Bhutto D, Rosen MS (2021) Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction. Sci Rep 11:8248CrossRefPubMedCentralPubMed Koonjoo N, Zhu B, Bagnall GC, Bhutto D, Rosen MS (2021) Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction. Sci Rep 11:8248CrossRefPubMedCentralPubMed
29.
go back to reference Koch KM, Sherafati M, Arpinar VE et al (2021) Analysis and evaluation of a deep learning reconstruction approach with denoising for orthopedic MRI. Radiol Artif Intell 3:e200278CrossRefPubMedCentralPubMed Koch KM, Sherafati M, Arpinar VE et al (2021) Analysis and evaluation of a deep learning reconstruction approach with denoising for orthopedic MRI. Radiol Artif Intell 3:e200278CrossRefPubMedCentralPubMed
30.
go back to reference Liu F, Samsonov A, Chen L, Kijowski R, Feng L (2019) SANTIS: Sampling-Augmented Neural neTwork with incoherent structure for MR image reconstruction. Magn Reson Med 82:1890–1904CrossRefPubMedCentralPubMed Liu F, Samsonov A, Chen L, Kijowski R, Feng L (2019) SANTIS: Sampling-Augmented Neural neTwork with incoherent structure for MR image reconstruction. Magn Reson Med 82:1890–1904CrossRefPubMedCentralPubMed
31.
go back to reference Altman DG, Royston P (2000) What do we mean by validating a prognostic model? Stat Med 19:453–473CrossRefPubMed Altman DG, Royston P (2000) What do we mean by validating a prognostic model? Stat Med 19:453–473CrossRefPubMed
Metadata
Title
Deep learning–based acceleration of Compressed Sense MR imaging of the ankle
Authors
Sarah C. Foreman
Jan Neumann
Jessie Han
Norbert Harrasser
Kilian Weiss
Johannes M. Peeters
Dimitrios C. Karampinos
Marcus R. Makowski
Alexandra S. Gersing
Klaus Woertler
Publication date
25-06-2022
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 12/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-022-08919-9

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