Deep Learning Reconstruction for Accelerated Spine MRI: Prospective                     Analysis of Interchangeability

Almansour, Haidara; Herrmann, Judith; Gassenmaier, Sebastian; Afat, Saif; Jacoby, Johann; Koerzdoerfer, Gregor; Nickel, Dominik; Mostapha, Mahmoud; Nadar, Mariappan S.; Othman, Ahmed E.

doi:10.1148/radiol.212922

Cited by 40 publications

(30 citation statements)

References 16 publications

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“…DL reconstruction, especially in areas of the reconstructed image with low signal-to-noise ratios. 13 These artifacts appear as stripe patterns aligned with the phase encoding direction. 23 However, in our study sample, we found no evidence of differences in artifacts, image quality, or diagnostic confidence between standard and accelerated imaging.…”

Section: Discussionmentioning

confidence: 99%

“…These instabilities can result in certain small pathological findings being “masked” or artifacts being introduced 22 . In the field of DL imaging, certain artifacts have been noted in the literature, such as banding artifacts typically associated with Cartesian DL reconstruction, especially in areas of the reconstructed image with low signal‐to‐noise ratios 13 . These artifacts appear as stripe patterns aligned with the phase encoding direction 23 .…”

Section: Discussionmentioning

confidence: 99%

“…12 Although synthetic T2-weighted images generated by DL algorithms might also have artifacts, they have the advantage of reducing scan times. [13][14][15] By integrating DL reconstruction techniques into medical imaging, there is potential to increase diagnostic accuracy, improve diagnostic efficiency, and enhance the effectiveness of therapeutic interventions.…”

Section: Introductionmentioning

confidence: 99%

“…Studies have demonstrated the effectiveness of these AI‐based methods in improving the quality of T2‐weighted fluid‐attenuated inversion recovery sequences 11 and reducing acquisition time in various imaging scenarios, such as non‐neuroradiologic imaging 12 . Although synthetic T2‐weighted images generated by DL algorithms might also have artifacts, they have the advantage of reducing scan times 13–15 …”

Section: Introductionmentioning

confidence: 99%

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Deep learning‐accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality

Estler,

Zerweck,

Brunnée

et al. 2024

Journal of Neuroimaging

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Background and PurposeThis study explores the use of deep learning (DL) techniques in MRI of the orbit to enhance imaging. Standard protocols, although detailed, have lengthy acquisition times. We investigate DL‐based methods for T2‐weighted and T1‐weighted, fat‐saturated, contrast‐enhanced turbo spin echo (TSE) sequences, aiming to improve image quality, reduce acquisition time, minimize artifacts, and enhance diagnostic confidence in orbital imaging.MethodsIn a 3‐Tesla MRI study of 50 patients evaluating orbital diseases from March to July 2023, conventional (TSES) and DL TSE sequences (TSEDL) were used. Two neuroradiologists independently assessed the image datasets for image quality, diagnostic confidence, noise levels, artifacts, and image sharpness using a randomized and blinded 4‐point Likert scale.ResultsTSEDL significantly reduced image noise and artifacts, enhanced image sharpness, and decreased scan time, outperforming TSES (p < .05). TSEDL showed superior overall image quality and diagnostic confidence, with relevant findings effectively detected in both DL‐based and conventional images. In 94% of cases, readers preferred accelerated imaging.ConclusionThe study proved that using DL for MRI image reconstruction in orbital scans significantly cut acquisition time by 69%. This approach also enhanced image quality, reduced image noise, sharpened images, and boosted diagnostic confidence.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep learning‐accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality

Estler,

Zerweck,

Brunnée

et al. 2024

Journal of Neuroimaging

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“…6 In the past years, methods such as parallel imaging, simultaneous multislice techniques, and compressed sensing were used to optimize DWI in terms of image quality and acquisition time. 12 The method of image reconstruction with DL reconstruction networks has been recently introduced as a promising technique to further shorten acquisition time and has already been performed on DWI for breast, 1 liver, 13 and prostate 14 ; T2-weighted turbo spin echo sequences for the breast, 15 spine, 16 and prostate [17][18][19][20] ; and on T1-weighted imaging of the chest, 21 each resulted in similar or better image quality for DL sequences.…”

mentioning

confidence: 99%

Accelerated Diffusion-Weighted Imaging in 3 T Breast MRI Using a Deep Learning Reconstruction Algorithm With Superresolution Processing

et al. 2023

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Objectives Diffusion-weighted imaging (DWI) enhances specificity in multiparametric breast MRI but is associated with longer acquisition time. Deep learning (DL) reconstruction may significantly shorten acquisition time and improve spatial resolution. In this prospective study, we evaluated acquisition time and image quality of a DL-accelerated DWI sequence with superresolution processing (DWIDL) in comparison to standard imaging including analysis of lesion conspicuity and contrast of invasive breast cancers (IBCs), benign lesions (BEs), and cysts. Materials and Methods This institutional review board–approved prospective monocentric study enrolled participants who underwent 3 T breast MRI between August and December 2022. Standard DWI (DWISTD; single-shot echo-planar DWI combined with reduced field-of-view excitation; b-values: 50 and 800 s/mm2) was followed by DWIDL with similar acquisition parameters and reduced averages. Quantitative image quality was analyzed for region of interest–based signal-to-noise ratio (SNR) on breast tissue. Apparent diffusion coefficient (ADC), SNR, contrast-to-noise ratio, and contrast (C) values were calculated for biopsy-proven IBCs, BEs, and for cysts. Two radiologists independently assessed image quality, artifacts, and lesion conspicuity in a blinded independent manner. Univariate analysis was performed to test differences and interrater reliability. Results Among 65 participants (54 ± 13 years, 64 women) enrolled in the study, the prevalence of breast cancer was 23%. Average acquisition time was 5:02 minutes for DWISTD and 2:44 minutes for DWIDL (P < 0.001). Signal-to-noise ratio measured in breast tissue was higher for DWISTD (P < 0.001). The mean ADC values for IBC were 0.77 × 10−3 ± 0.13 mm2/s in DWISTD and 0.75 × 10−3 ± 0.12 mm2/s in DWIDL without significant difference when sequences were compared (P = 0.32). Benign lesions presented with mean ADC values of 1.32 × 10−3 ± 0.48 mm2/s in DWISTD and 1.39 × 10−3 ± 0.54 mm2/s in DWIDL (P = 0.12), and cysts presented with 2.18 × 10−3 ± 0.49 mm2/s in DWISTD and 2.31 × 10−3 ± 0.43 mm2/s in DWIDL. All lesions presented with significantly higher contrast in the DWIDL (P < 0.001), whereas SNR and contrast-to-noise ratio did not differ significantly between DWISTD and DWIDL regardless of lesion type. Both sequences demonstrated a high subjective image quality (29/65 for DWISTD vs 20/65 for DWIDL; P < 0.001). The highest lesion conspicuity score was observed more often for DWIDL (P < 0.001) for all lesion types. Artifacts were scored higher for DWIDL (P < 0.001). In general, no additional artifacts were noted in DWIDL. Interrater reliability was substantial to excellent (k = 0.68 to 1.0). Conclusions DWIDL in breast MRI significantly reduced scan time by nearly one half while improving lesion conspicuity and maintaining overall image quality in a prospective clinical cohort.

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Beschleunigte muskuloskeletale Magnetresonanztomographie mit Deep-Learning-gestützter Bildrekonstruktion bei 0,55 T–3 T

Vosshenrich,

Fritz

2024

Radiologie

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Zusammenfassung Klinisches/methodisches Problem Die Magnetresonanztomographie (MRT) ist ein zentraler Bestandteil der muskuloskeletalen Diagnostik. Lange Akquisitionszeiten können jedoch zu Einschränkungen in der klinischen Praxis führen. Radiologische Standardverfahren Die MRT hat sich aufgrund des hohen Auflösungsvermögens und Signal-zu-Rausch-Verhältnisses (SNR) sowie des exzellenten Weichteilkontrastes als Modalität der Wahl in der Diagnostik von Verletzungen und Erkrankungen des muskuloskeletalen Systems etabliert. Methodische Innovationen Kontinuierliche Weiterentwicklungen in der Hard- und Softwaretechnologie haben eine bildqualitäts- und genauigkeitsneutrale Beschleunigung von 2D-Turbo-Spin-Echo(TSE)-Sequenzen um den Faktor 4 ermöglicht. Kürzlich vorgestellte, auf Deep Learning (DL) basierende Bildrekonstruktionsalgorithmen helfen, die Abhängigkeit zwischen SNR, räumlicher Auflösung und Akquisitionszeit weiter zu minimieren und erlauben die Anwendung höherer Beschleunigungsfaktoren. Leistungsfähigkeit Die kombinierte Anwendung fortschrittlicher Beschleunigungstechniken und DL-basierter Bildrekonstruktion birgt enormes Potenzial, um die Effizienz, den Patientenkomfort und die Zugänglichkeit der muskuloskeletalen MRT bei gleichbleibend hoher diagnostischer Genauigkeit zu maximieren. Bewertung DL-rekonstruierte beschleunigte MRT-Untersuchungen haben ihre Praxisreife und ihren Mehrwert innerhalb kürzester Zeit unter Beweis gestellt. Aktuelle wissenschaftliche Erkenntnisse legen nahe, dass das Potenzial dieser Technologie noch nicht ausgeschöpft ist. Empfehlung für die Praxis Beschleunigte MRT-Untersuchungen mit DL-gestützter Bildrekonstruktion können zuverlässig in der Primärdiagnostik und Verlaufskontrolle muskuloskeletaler Fragestellungen eingesetzt werden.

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Deep Learning Reconstruction for Accelerated Spine MRI: Prospective Analysis of Interchangeability

Cited by 40 publications

References 16 publications

Deep learning‐accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality

Deep learning‐accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality

Accelerated Diffusion-Weighted Imaging in 3 T Breast MRI Using a Deep Learning Reconstruction Algorithm With Superresolution Processing

Beschleunigte muskuloskeletale Magnetresonanztomographie mit Deep-Learning-gestützter Bildrekonstruktion bei 0,55 T–3 T

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Deep Learning Reconstruction for Accelerated Spine MRI: Prospective Analysis of Interchangeability

Cited by 40 publications

References 16 publications

Deep learning‐accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality

Deep learning‐accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality

Accelerated Diffusion-Weighted Imaging in 3 T Breast MRI Using a Deep Learning Reconstruction Algorithm With Superresolution Processing

Beschleunigte muskuloskeletale Magnetresonanztomographie mit Deep-Learning-gestützter Bildrekonstruktion bei 0,55 T–3 T

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About

Beschleunigte muskuloskeletale Magnetresonanztomographie mit Deep-Learning-gestützter Bildrekonstruktion bei 0,55 T–3 T