Deep Learning–Based Super-Resolution Reconstruction on Undersampled Brain Diffusion-Weighted MRI for Infarction Stroke: A Comparison to Conventional Iterative Reconstruction

References

1. 1.↵
   1. Gaddamanugu S,
   2. Shafaat O,
   3. Sotoudeh H, et al

   . Clinical applications of diffusion-weighted sequence in brain imaging: beyond stroke. Neuroradiology 2022;64:15–30 doi:10.1007/s00234-021-02819-3 pmid:34596716

   CrossRefPubMed
2. 2.↵
   1. Maguida G,
   2. Shuaib A

   . Collateral circulation in ischemic stroke: an updated review. J Stroke 2023;25:179–98 doi:10.5853/jos.2022.02936 pmid:36907186

   CrossRefPubMed
3. 3.↵
   1. Popiela T J,
   2. Krzyściak W,
   3. Pilato F, et al

   . The assessment of endovascular therapies in ischemic stroke: management, problems and future approaches. J Clin Med 2022;11:1864 doi:10.3390/jcm11071864

   CrossRefPubMed
4. 4.↵
   1. Doubal FN,
   2. Dennis MS,
   3. Wardlaw JM

   . Characteristics of patients with minor ischaemic strokes and negative MRI: a cross-sectional study. J Neurol Neurosurg Psychiatry 2011;82:540–42 doi:10.1136/jnnp.2009.190298 pmid:20584742

   Abstract/FREE Full Text
5. 5.↵
   1. Kawano H,
   2. Hirano T,
   3. Nakajima M, et al

   . Diffusion-weighted magnetic resonance imaging may underestimate acute ischemic lesions: cautions on neglecting a computed tomography-diffusion-weighted imaging discrepancy. Stroke 2013;44:1056–61 doi:10.1161/STROKEAHA.111.000254 pmid:23412380

   Abstract/FREE Full Text
6. 6.↵
   1. Sylaja PN,
   2. Coutts SB,
   3. Krol A, et al

   ; VISION Study Group. When to expect negative diffusion-weighted images in stroke and transient ischemic attack. Stroke 2008;39:1898–1900 doi:10.1161/STROKEAHA.107.497453 pmid:18420957

   Abstract/FREE Full Text
7. 7.↵
   1. Tamada T,
   2. Ueda Y,
   3. Kido A, et al

   . Clinical application of single-shot echo-planar diffusion-weighted imaging with compressed SENSE in prostate MRI at 3T: preliminary experience. MAGMA 2022;35:549–56 doi:10.1007/s10334-022-01010-w pmid:35403993

   CrossRefPubMed
8. 8.↵
   1. Kaga T,
   2. Noda Y,
   3. Mori T, et al

   . Diffusion-weighted imaging of the abdomen using echo planar imaging with compressed SENSE: feasibility, image quality, and ADC value evaluation. Eur J Radiol 2021;142:109889 doi:10.1016/j.ejrad.2021.109889 pmid:34388627

   CrossRefPubMed
9. 9.↵
   1. Kim M,
   2. Lee SM,
   3. Park C, et al

   . Deep learning-enhanced parallel imaging and simultaneous multislice acceleration reconstruction in knee MRI. Invest Radiol 2022;57:826–33 doi:10.1097/RLI.0000000000000900 pmid:35776434

   CrossRefPubMed
10. 10.↵
    1. Wang Q,
    2. Zhao W,
    3. Xing X, et al

    . Feasibility of AI-assisted compressed sensing protocols in knee MR imaging: a prospective multi-reader study. Eur Radiol 2023;33:8585–96 doi:10.1007/s00330-023-09823-6 pmid:37382615

    CrossRefPubMed
11. 11.↵
    1. Pezzotti N,
    2. Yousefi S,
    3. Elmahdy MS, et al

    . An adaptive intelligence algorithm for undersampled knee MRI reconstruction. IEEE Access 2020;8:204825–38 doi:10.1109/ACCESS.2020.3034287

    CrossRef
12. 12.↵
    1. Yao T,
    2. St Clair N,
    3. Miller GF, et al

    . A deep learning pipeline for assessing ventricular volumes from a cardiac MRI registry of patients with single ventricle physiology. Radiology Artif Intell 2024;6:e230132 pmid:38166332

    CrossRefPubMed
13. 13.↵
    1. Knuth F,
    2. Tohidinezhad F,
    3. Winter RM, et al

    . Quantitative MRI-based radiomics analysis identifies blood flow feature associated to overall survival for rectal cancer patients. Sci Rep 2024;14:258 doi:10.1038/s41598-023-50966-9 pmid:38167665

    CrossRefPubMed
14. 14.↵
    1. Li Y,
    2. Lv X,
    3. Chen C, et al

    . A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer. Eur Radiology Exp 2024;8:2 doi:10.1186/s41747-023-00396-z pmid:38169047

    CrossRefPubMed
15. 15.↵
    1. Ueda T,
    2. Ohno Y,
    3. Yamamoto K, et al

    . Deep learning reconstruction of diffusion-weighted MRI improves image quality for prostatic imaging. Radiology 2022;303:373–81 doi:10.1148/radiol.204097 pmid:35103536

    CrossRefPubMed
16. 16.↵
    1. Kim DK,
    2. Lee S-Y,
    3. Lee J, et al

    . Deep learning-based k-space-to-image reconstruction and super resolution for diffusion-weighted imaging in whole-spine MRI. Magn Reson Imaging 2024;105:82–91 doi:10.1016/j.MRI.2023.11.003 pmid:37939970

    CrossRefPubMed
17. 17.↵
    1. Lustig M,
    2. Pauly JM

    . SPIRiT: iterative self-consistent parallel imaging reconstruction from arbitrary k-space. Magn Reson Med 2010;64:457–71 doi:10.1002/mrm.22428 pmid:20665790

    CrossRefPubMed
18. 18.↵
    1. Chaudhari AS,
    2. Fang Z,
    3. Kogan F, et al

    . Super-resolution musculoskeletal MRI using deep learning. Magn Reson Med 2018;80:2139–54 doi:10.1002/mrm.27178 pmid:29582464

    CrossRefPubMed
19. 19.↵
    1. Bischoff LM,
    2. Peeters JM,
    3. Weinhold L, et al

    . Deep learning super-resolution reconstruction for fast and motion-robust T2-weighted prostate MRI. Radiology 2023;308:e230427 doi:10.1148/radiol.230427 pmid:37750774

    CrossRefPubMed
20. 20.↵
    1. Pezzotti N,
    2. de Weerdt E,
    3. Yousefi S, et al

    . Adaptive-CS-Net: FastMRI with Adaptive Intelligence. arXiv e-prints 2019 arXiv:1912.12259
21. 21.↵
    1. Jurka M,
    2. Macova I,
    3. Wagnerova M, et al

    . Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time. Quant Imaging Med Surg 2024;14:3534–43 doi:10.21037/qims-23-1488 pmid:38720867

    CrossRefPubMed
22. 22.↵
    1. Dong C,
    2. Loy CC,
    3. He K, et al

    . Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 2016;38:295–307 doi:10.1109/TPAMI.2015.2439281 pmid:26761735

    CrossRefPubMed
23. 23.↵
    1. Li Y,
    2. Sixou B,
    3. Peyrin F

    . A review of the deep learning methods for medical images super resolution problems. IRBM 2020;42:120–33 doi:10.1016/j.irbm.2020.08.004

    CrossRef
24. 24.↵
    1. Yeoh H,
    2. Hong SH,
    3. Ahn C, et al

    . Deep learning algorithm for simultaneous noise reduction and edge sharpening in low-dose CT images: a pilot study using lumbar spine CT. Korean J Radiol 2021;22:1850–57 doi:10.3348/kjr.2021.0140 pmid:34431248

    CrossRefPubMed
25. 25.↵
    1. Tatsugami F,
    2. Higaki T,
    3. Nakamura Y, et al

    . Deep learning-based image restoration algorithm for coronary CT angiography. Eur Radiol 2019;29:5322–29 doi:10.1007/s00330-019-06183-y pmid:30963270

    CrossRefPubMed
26. 26.↵
    1. Tatsugami F,
    2. Higaki T,
    3. Sakane H, et al

    . Coronary artery stent evaluation with model-based iterative reconstruction at coronary CT angiography. Acad Radiol 2017;24:975–81 doi:10.1016/j.acra.2016.12.020 pmid:28214228

    CrossRefPubMed
27. 27.↵
    1. Jia H,
    2. Yin Q,
    3. Lu M

    . Blind-noise image denoising with block-matching domain transformation filtering and improved guided filtering. Sci Rep 2022;12:16195 doi:10.1038/s41598-022-20578-w pmid:36171466

    CrossRefPubMed
28. 28.↵
    1. Yang J,
    2. Wang C,
    3. Xiang J, et al

    . Oral CT image processing based on oral CT image filtering algorithm. Comput Intell Neurosci 2022;2022:6041872 doi:10.1155/2022/6041872 pmid:36172316

    CrossRefPubMed
29. 29.↵
    1. Dwork N,
    2. O’Connor D,
    3. Baron CA, et al

    . Utilizing the wavelet transform’s structure in compressed sensing. Signal Image Video Process 2021;15:1407–14 doi:10.1007/s11760-021-01872-y pmid:34531930

    CrossRefPubMed
30. 30.↵
    1. Fervers P,
    2. Zaeske C,
    3. Rauen P, et al

    . Conventional and deep-learning-based image reconstructions of undersampled K-space data of the lumbar spine using compressed sensing in MRI: a comparative study on 20 subjects. Diagnostics (Basel) 2023;13:418 doi:10.3390/diagnostics13030418

    CrossRefPubMed
31. 31.↵
    1. Chen Y,
    2. Li J,
    3. Qu X, et al

    . Partial Fourier transform reconstruction for single-shot MRI with linear frequency-swept excitation. Magn Reson Med 2013;69:1326–36 doi:10.1002/mrm.24366 pmid:22706702

    CrossRefPubMed
32. 32.↵
    1. Zhu H,
    2. Jiang L,
    3. Zhang H, et al

    . An automatic machine learning approach for ischemic stroke onset time identification based on DWI and FLAIR imaging. Neuroimage Clin 2021;31:102744 doi:10.1016/j.nicl.2021.102744 pmid:34245995

    CrossRefPubMed