Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Skeletal Radiology

24-04-2024 | Magnetic Resonance Imaging | Scientific Article

Improved Resolution and Image Quality of Musculoskeletal Magnetic Resonance Imaging using Deep Learning-based Denoising Reconstruction: A Prospective Clinical Study

Authors: Hung P. Do, Carly A. Lockard, Dawn Berkeley, Brian Tymkiw, Nathan Dulude, Scott Tashman, Garry Gold, Jordan Gross, Erin Kelly, Charles P. Ho

Published in: Skeletal Radiology

Login to get access

Abstract

Objective

To prospectively evaluate a deep learning-based denoising reconstruction (DLR) for improved resolution and image quality in musculoskeletal (MSK) magnetic resonance imaging (MRI).

Methods

Images from 137 contrast-weighted sequences in 40 MSK patients were evaluated. Each sequence was performed twice, first with the routine parameters and reconstructed with a routine reconstruction filter (REF), then with higher resolution and reconstructed with DLR, and with three conventional reconstruction filters (NL2, GA43, GA53). The five reconstructions (REF, DLR, NL2, GA43, and GA53) were de-identified, randomized, and blindly reviewed by three MSK radiologists using eight scoring criteria and a forced ranking. Quantitative SNR, CNR, and structure’s full width at half maximum (FWHM) for resolution assessment were measured and compared. To account for repeated measures, Generalized Estimating Equations (GEE) with Bonferroni adjustment was used to compare the reader’s scores, SNR, CNR, and FWHM between DLR vs. NL2, GA43, GA53, and REF.

Results

Compared to the routine REF images, the resolution was improved by 47.61% with DLR from 0.39 ± 0.15 mm2 to 0.20 ± 0.06 mm2 (p < 0.001). Per-sequence average scan time was shortened by 7.93% with DLR from 165.58 ± 21.86 s to 152.45 ± 25.65 s (p < 0.001). Based on the average scores, DLR images were rated significantly higher in all image quality criteria and the forced ranking (p < 0.001).

Conclusion

This prospective clinical evaluation demonstrated that DLR allows approximately two times finer resolution and improved image quality compared to the standard-of-care images.
Appendix
Available only for authorised users
Literature
1.
McRobbie DW, Moore EA, Graves MJ, Prince MR. MRI from Picture to Proton. Cambridge University Press; 2017.CrossRef
2.
Kidoh M, Shinoda K, Kitajima M, Isogawa K, Nambu M, Uetani H, et al. Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers. Magn Reson Med Sci. 2020;19:195–206.CrossRefPubMed
3.
Lebel RM. Performance characterization of a novel deep learning-based MR image reconstruction pipeline. 2020; arXiv preprint arXiv:2008.06559.
4.
Koch KM, Sherafati M, Arpinar VE, Bhave S, Ausman R, Nencka AS, et al. Analysis and Evaluation of a Deep Learning Reconstruction Approach with Denoising for Orthopedic MRI. Radiol Artif Intell. 2021;3: e200278.CrossRefPubMedPubMedCentral
5.
Ueda T, Ohno Y, Yamamoto K, Murayama K, Ikedo M, Yui M, et al. Deep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic Imaging. Radiology. 2022;303:373–81.CrossRefPubMed
6.
Kashiwagi N, Sakai M, Tsukabe A, Yamashita Y, Fujiwara M, Yamagata K, et al. Ultrafast cervcial spine MRI protocol using deep learning-based reconstruction: diagnostic equivalence to a conventional protocol. Eur J Radiol. 2022;156:110531.
7.
Kim M, Kim HS, Kim HJ, Park JE, Park SY, Kim Y-H, et al. Thin-Slice Pituitary MRI with Deep Learning–based Reconstruction: Diagnostic Performance in a Postoperative Setting. Radiology. 2021;298:114–22.CrossRefPubMed
8.
Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17:261–72.CrossRefPubMedPubMedCentral
9.
Van Rossum G, Drake FL. Python 3 Reference Manual. Scotts Valley, CA: CreateSpace; 2009.
10.
11.
McKinney W. Data Structures for Statistical Computing in Python. Proc 9th Python Sci Conf. 2010;56–61.
12.
13.
Sandino CM, Cheng JY, Chen F, Mardani M, Pauly JM, Vasanawala SS. Compressed Sensing: From Research to Clinical Practice With Deep Neural Networks: Shortening Scan Times for Magnetic Resonance Imaging. IEEE Signal Process Mag. 2020;37:117–27.CrossRef
14.
R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2021.
15.
Liang K-Y, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73:13–22.CrossRef
16.
Zeger SL, Liang K-Y. Longitudinal Data Analysis for Discrete and Continuous Outcomes. Biometrics. 1986;42:121.CrossRefPubMed
17.
Hardin JW, Hilbe JM. Generalized Estimating Equations. New York: Chapman and Hall/CRC; 2002.CrossRef
18.
Højsgaard S, Halekoh U, Yan J. The R Package geepack for Generalized Estimating Equations. J Stat Softw. 2005;15:1–11.
19.
20.
Yan J. geepack: Yet Another Package for Generalized Estimating Equations. R-News. 2002;2(3):12–4.
21.
Gwet KL. Handbook of inter-rater reliability: the definitive guide to measuring the extent of agreement among raters. 5. ed. Gaithersburg, MD: Advanced Analytics, LLC; 2021.
22.
Gwet KL. Computing inter-rater reliability and its variance in the presence of high agreement. Br J Math Stat Psychol. 2008;61:29–48.CrossRefPubMed
23.
Gwet KL. irrCAC: Computing Chance-Corrected Agreement Coefficients (CAC). R package version 1.0. 2019.
24.
Altman DG. Practical Statistics for Medical Research. Chapman & Hall / CRC Press; 1990.CrossRef
25.
Hunter JD. Matplotlib: A 2D Graphics Environment. Comput Sci Eng. 2007;9:90–5.CrossRef
26.
Waskom ML. seaborn: statistical data visualization. J Open Source Softw. 2021;6:3021.CrossRef
27.
Wickham H. ggplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag; 2016.CrossRef
28.
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans Image Process. 2004;13:600–12.CrossRefPubMed
29.
Hore A,  Ziou D. Image quality metrics: PSNR vs. SSIM. The 20th IntConf Pattern Recognit Istanbul, Turkey: IEEE. 2010;2366–2369.
30.
Wang Z, Simoncelli EP,  Bovik AC.  Multiscale structural similarity for image quality assessment. The thirty-seventh asilomar conference on signals, systems & computers, Pacific Grove, CA, USA: IEEE. 2003;2:1398–1402.
31.
Wang Zhou, Bovik AC. Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures. IEEE Signal Process Mag. 2009;26:98–117.CrossRef
32.
Knoll F, Murrell T, Sriram A, Yakubova N, Zbontar J, Rabbat M, et al. Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. Magn Reson Med. 2020;84:3054–70.CrossRefPubMedPubMedCentral
33.
Chaudhari AS, Sandino CM, Cole EK, Larson DB, Gold GE, Vasanawala SS, et al. Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices. J Magn Reson Imaging. 2021;54:357–71.CrossRefPubMed
34.
Muckley MJ, Riemenschneider B, Radmanesh A, Kim S, Jeong G, Ko J, et al. Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. IEEE Trans Med Imaging. 2021;40:2306–17.CrossRefPubMedPubMedCentral
Metadata
Title
Improved Resolution and Image Quality of Musculoskeletal Magnetic Resonance Imaging using Deep Learning-based Denoising Reconstruction: A Prospective Clinical Study
Authors
Hung P. Do
Carly A. Lockard
Dawn Berkeley
Brian Tymkiw
Nathan Dulude
Scott Tashman
Garry Gold
Jordan Gross
Erin Kelly
Charles P. Ho
Publication date
24-04-2024
Publisher
Springer Berlin Heidelberg
Published in
Skeletal Radiology
Print ISSN: 0364-2348
Electronic ISSN: 1432-2161
DOI
https://doi.org/10.1007/s00256-024-04679-3