These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

210 related articles for article (PubMed ID: 35934207)

  • 1. MR imaging for shoulder diseases: Effect of compressed sensing and deep learning reconstruction on examination time and imaging quality compared with that of parallel imaging.
    Obama Y; Ohno Y; Yamamoto K; Ikedo M; Yui M; Hanamatsu S; Ueda T; Ikeda H; Murayama K; Toyama H
    Magn Reson Imaging; 2022 Dec; 94():56-63. PubMed ID: 35934207
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Efficacy of compressed sensing and deep learning reconstruction for adult female pelvic MRI at 1.5 T.
    Ueda T; Yamamoto K; Yazawa N; Tozawa I; Ikedo M; Yui M; Nagata H; Nomura M; Ozawa Y; Ohno Y
    Eur Radiol Exp; 2024 Sep; 8(1):103. PubMed ID: 39254920
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Comparison of utility of deep learning reconstruction on 3D MRCPs obtained with three different k-space data acquisitions in patients with IPMN.
    Matsuyama T; Ohno Y; Yamamoto K; Ikedo M; Yui M; Furuta M; Fujisawa R; Hanamatsu S; Nagata H; Ueda T; Ikeda H; Takeda S; Iwase A; Fukuba T; Akamatsu H; Hanaoka R; Kato R; Murayama K; Toyama H
    Eur Radiol; 2022 Oct; 32(10):6658-6667. PubMed ID: 35687136
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Comparison of deep learning-based reconstruction of PROPELLER Shoulder MRI with conventional reconstruction.
    Hahn S; Yi J; Lee HJ; Lee Y; Lee J; Wang X; Fung M
    Skeletal Radiol; 2023 Aug; 52(8):1545-1555. PubMed ID: 36943429
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Super-resolution deep learning reconstruction approach for enhanced visualization in lumbar spine MR bone imaging.
    Hokamura M; Nakaura T; Yoshida N; Uetani H; Shiraishi K; Kobayashi N; Matsuo K; Morita K; Nagayama Y; Kidoh M; Yamashita Y; Miyamoto T; Hirai T
    Eur J Radiol; 2024 Sep; 178():111587. PubMed ID: 39002269
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Compressed sensing and parallel imaging accelerated T2 FSE sequence for head and neck MR imaging: Comparison of its utility in routine clinical practice.
    Ikeda H; Ohno Y; Murayama K; Yamamoto K; Iwase A; Fukuba T; Toyama H
    Eur J Radiol; 2021 Feb; 135():109501. PubMed ID: 33395594
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Combination Use of Compressed Sensing and Deep Learning for Shoulder Magnetic Resonance Imaging With Various Sequences.
    Shiraishi K; Nakaura T; Uetani H; Nagayama Y; Kidoh M; Kobayashi N; Morita K; Yamahita Y; Miyamoto T; Hirai T
    J Comput Assist Tomogr; 2023 Mar-Apr 01; 47(2):277-283. PubMed ID: 36944152
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Assessment of deep learning-based reconstruction on T2-weighted and diffusion-weighted prostate MRI image quality.
    Lee KL; Kessler DA; Dezonie S; Chishaya W; Shepherd C; Carmo B; Graves MJ; Barrett T
    Eur J Radiol; 2023 Sep; 166():111017. PubMed ID: 37541181
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Reconstruction of shoulder MRI using deep learning and compressed sensing: a validation study on healthy volunteers.
    Dratsch T; Siedek F; Zäske C; Sonnabend K; Rauen P; Terzis R; Hahnfeldt R; Maintz D; Persigehl T; Bratke G; Iuga A
    Eur Radiol Exp; 2023 Oct; 7(1):66. PubMed ID: 37880546
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Evaluation of a deep learning-based reconstruction method for denoising and image enhancement of shoulder MRI in patients with shoulder pain.
    Feuerriegel GC; Weiss K; Kronthaler S; Leonhardt Y; Neumann J; Wurm M; Lenhart NS; Makowski MR; Schwaiger BJ; Woertler K; Karampinos DC; Gersing AS
    Eur Radiol; 2023 Jul; 33(7):4875-4884. PubMed ID: 36806569
    [TBL] [Abstract][Full Text] [Related]  

  • 11. AI-assisted compressed sensing and parallel imaging sequences for MRI of patients with nasopharyngeal carcinoma: comparison of their capabilities in terms of examination time and image quality.
    Liu H; Deng D; Zeng W; Huang Y; Zheng C; Li X; Li H; Xie C; He H; Xu G
    Eur Radiol; 2023 Nov; 33(11):7686-7696. PubMed ID: 37219618
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Image Quality and Diagnostic Performance of Accelerated Shoulder MRI With Deep Learning-Based Reconstruction.
    Hahn S; Yi J; Lee HJ; Lee Y; Lim YJ; Bang JY; Kim H; Lee J
    AJR Am J Roentgenol; 2022 Mar; 218(3):506-516. PubMed ID: 34523950
    [No Abstract]   [Full Text] [Related]  

  • 13. Deep learning-based reconstruction and 3D hybrid profile order technique for MRCP at 3T: evaluation of image quality and acquisition time.
    Shiraishi K; Nakaura T; Uetani H; Nagayama Y; Kidoh M; Kobayashi N; Morita K; Yamahita Y; Tanaka Y; Baba H; Hirai T
    Eur Radiol; 2023 Nov; 33(11):7585-7594. PubMed ID: 37178197
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Compressed sensing with deep learning reconstruction: Improving capability of gadolinium-EOB-enhanced 3D T1WI.
    Nagata H; Ohno Y; Yoshikawa T; Yamamoto K; Shinohara M; Ikedo M; Yui M; Matsuyama T; Takahashi T; Bando S; Furuta M; Ueda T; Ozawa Y; Toyama H
    Magn Reson Imaging; 2024 May; 108():67-76. PubMed ID: 38309378
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients.
    Takenaka D; Ozawa Y; Yamamoto K; Shinohara M; Ikedo M; Yui M; Oshima Y; Hamabuchi N; Nagata H; Ueda T; Ikeda H; Iwase A; Yoshikawa T; Toyama H; Ohno Y
    Magn Reson Med Sci; 2024 Oct; 23(4):487-501. PubMed ID: 37661425
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Hybrid deep-learning-based denoising method for compressed sensing in pituitary MRI: comparison with the conventional wavelet-based denoising method.
    Uetani H; Nakaura T; Kitajima M; Morita K; Haraoka K; Shinojima N; Tateishi M; Inoue T; Sasao A; Mukasa A; Azuma M; Ikeda O; Yamashita Y; Hirai T
    Eur Radiol; 2022 Jul; 32(7):4527-4536. PubMed ID: 35169896
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Exploring the impact of super-resolution deep learning on MR angiography image quality.
    Hokamura M; Uetani H; Nakaura T; Matsuo K; Morita K; Nagayama Y; Kidoh M; Yamashita Y; Ueda M; Mukasa A; Hirai T
    Neuroradiology; 2024 Feb; 66(2):217-226. PubMed ID: 38148334
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Fast high-quality MRI protocol of the lumbar spine with deep learning-based algorithm: an image quality and scanning time comparison with standard protocol.
    Zerunian M; Pucciarelli F; Caruso D; De Santis D; Polici M; Masci B; Nacci I; Del Gaudio A; Argento G; Redler A; Laghi A
    Skeletal Radiol; 2024 Jan; 53(1):151-159. PubMed ID: 37369725
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep learning image reconstruction of diffusion-weighted imaging in evaluation of prostate cancer focusing on its clinical implications.
    Jeong J; Yeom SK; Choi IY; Cha SH; Han JS; Lee CH; Sung DJ; Choi JW; Bae JH; Choi H; Kim I; Benkert T; Weiland E
    Quant Imaging Med Surg; 2024 May; 14(5):3432-3446. PubMed ID: 38720859
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Utility of accelerated T2-weighted turbo spin-echo imaging with deep learning reconstruction in female pelvic MRI: a multi-reader study.
    Lee EJ; Hwang J; Park S; Bae SH; Lim J; Chang YW; Hong SS; Oh E; Nam BD; Jeong J; Sung JK; Nickel D
    Eur Radiol; 2023 Nov; 33(11):7697-7706. PubMed ID: 37314472
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.