Improved image quality in contrast-enhanced 3D-T1 weighted sequence by compressed sensing-based deep-learning reconstruction for the evaluation of head and neck

被引:5
作者
Fujima, Noriyuki [1 ]
Nakagawa, Junichi [1 ]
Ikebe, Yohei [2 ,3 ]
Kameda, Hiroyuki [4 ]
Harada, Taisuke [1 ]
Shimizu, Yukie [1 ]
Tsushima, Nayuta [5 ,6 ]
Kano, Satoshi [5 ,6 ]
Homma, Akihiro [5 ,6 ]
Kwon, Jihun [7 ]
Yoneyama, Masami [7 ]
Kudo, Kohsuke [1 ,2 ,8 ,9 ]
机构
[1] Hokkaido Univ Hosp, Dept Diagnost & Intervent Radiol, N14 W5,Kita Ku, Sapporo, Hokkaido 0608638, Japan
[2] Hokkaido Univ, Grad Sch Med, Dept Diagnost Imaging, N15 W7,Kita Ku, Sapporo, Hokkaido 0608638, Japan
[3] Hokkaido Univ, Fac Med, Ctr Cause Death Invest, N15 W7,Kita Ku, Sapporo, Hokkaido 0608638, Japan
[4] Hokkaido Univ, Fac Dent Med, Dept Radiol, N13 W7,Kita Ku, Sapporo, Hokkaido 0608586, Japan
[5] Hokkaido Univ, Fac Med, Dept Otolaryngol Head & Neck Surg, N15 W7,Kita Ku, Sapporo, Hokkaido 0608638, Japan
[6] Hokkaido Univ, Grad Sch Med, N15 W7,Kita Ku, Sapporo, Hokkaido 0608638, Japan
[7] Philips Japan, 3-37 Kohnan 2 Chome,Minato Ku, Tokyo 1088507, Japan
[8] Hokkaido Univ, Fac Med, Clin AI Human Resources Dev Program, N15 W7,Kita Ku, Sapporo, Hokkaido 0608638, Japan
[9] Hokkaido Univ, Fac Med, Global Ctr Biomed Sci & Engn, N14 W5,Kita Ku, Sapporo, Hokkaido 0608638, Japan
基金
日本学术振兴会;
关键词
Head and neck; Contrast-enhanced; 3D-T1WI; Compressed sensing; Deep learning; Reconstruction; INTELLIGENCE; MRI;
D O I
10.1016/j.mri.2024.02.006
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Purpose: To assess the utility of deep learning (DL)-based image reconstruction with the combination of compressed sensing (CS) denoising cycle by comparing images reconstructed by conventional CS-based method without DL in fat-suppressed (Fs)-contrast enhanced (CE) three-dimensional (3D) T1-weighted images (T1WIs) of the head and neck. Materials and methods: We retrospectively analyzed the cases of 39 patients who had undergone head and neck Fs-CE 3D T1WI applying reconstructions based on conventional CS and CS augmented by DL, respectively. In the qualitative assessment, we evaluated overall image quality, visualization of anatomical structures, degree of artifacts, lesion conspicuity, and lesion edge sharpness based on a five-point system. In the quantitative assessment, we calculated the signal-to-noise ratios (SNRs) of the lesion and the posterior neck muscle and the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle. Results: For all items of the qualitative analysis, significantly higher scores were awarded to images with DLbased reconstruction (p < 0.001). In the quantitative analysis, DL-based reconstruction resulted in significantly higher values for both the SNR of lesions (p < 0.001) and posterior neck muscles (p < 0.001). Significantly higher CNRs were also observed in images with DL-based reconstruction (p < 0.001). Conclusion: DL-based image reconstruction integrating into the CS-based denoising cycle offered superior image quality compared to the conventional CS method. This technique will be useful for the assessment of patients with head and neck disease.
引用
收藏
页码:111 / 115
页数:5
相关论文
共 44 条
[31]   Accelerated T2-weighted MRI of the liver at 3 T using a single-shot technique with deep learning-based image reconstruction: impact on the image quality and lesion detection [J].
Luke A. Ginocchio ;
Paul N. Smereka ;
Angela Tong ;
Vinay Prabhu ;
Dominik Nickel ;
Simon Arberet ;
Hersh Chandarana ;
Krishna P. Shanbhogue .
Abdominal Radiology, 2023, 48 :282-290
[32]   Accelerated T2-weighted MRI of the liver at 3 T using a single-shot technique with deep learning-based image reconstruction: impact on the image quality and lesion detection [J].
Ginocchio, Luke A. ;
Smereka, Paul N. ;
Tong, Angela ;
Prabhu, Vinay ;
Nickel, Dominik ;
Arberet, Simon ;
Chandarana, Hersh ;
Shanbhogue, Krishna P. .
ABDOMINAL RADIOLOGY, 2023, 48 (01) :282-290
[33]   Evaluation of 3D T1-weighted spoiled gradient echo MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain [J].
Nagaraj, Usha D. ;
Dillman, Jonathan R. ;
Tkach, Jean A. ;
Greer, Joshua S. ;
Leach, James L. .
NEURORADIOLOGY, 2024, 66 (10) :1849-1857
[34]   Accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning–based image reconstruction: qualitative and quantitative comparison of image quality with conventional T2-weighted FS sequence [J].
Krishna Shanbhogue ;
Angela Tong ;
Paul Smereka ;
Dominik Nickel ;
Simon Arberet ;
Rebecca Anthopolos ;
Hersh Chandarana .
European Radiology, 2021, 31 :8447-8457
[35]   Free-Breathing Radial 3D Fat-Suppressed T1-Weighted Gradient Echo Sequence A Viable Alternative for Contrast-Enhanced Liver Imaging in Patients Unable to Suspend Respiration [J].
Chandarana, Hersh ;
Block, Tobias K. ;
Rosenkrantz, Andrew B. ;
Lim, Ruth P. ;
Kim, Danny ;
Mossa, David J. ;
Babb, James S. ;
Kiefer, Berthold ;
Lee, Vivian S. .
INVESTIGATIVE RADIOLOGY, 2011, 46 (10) :648-653
[36]   Accelerated T2-weighted MRI of the Bowel at 3T Using a Single-shot Technique with Deep Learning-based Image Reconstruction: Impact on Image Quality and Disease Detection [J].
Dane, Bari ;
Bagga, Barun ;
Bansal, Bhavik ;
Beier, Sarah ;
Kim, Sooah ;
Reddy, Arthi ;
Fenty, Felicia ;
Keerthivasan, Mahesh ;
Chandarana, Hersh .
ACADEMIC RADIOLOGY, 2025, 32 (01) :210-217
[37]   Accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction: qualitative and quantitative comparison of image quality with conventional T2-weighted FS sequence [J].
Shanbhogue, Krishna ;
Tong, Angela ;
Smereka, Paul ;
Nickel, Dominik ;
Arberet, Simon ;
Anthopolos, Rebecca ;
Chandarana, Hersh .
EUROPEAN RADIOLOGY, 2021, 31 (11) :8447-8457
[38]   The Impact of Model-based Deep-learning Reconstruction Compared with that of Compressed Sensing-Sensitivity Encoding on the Image Quality and Precision of Cine Cardiac MR in Evaluating Left-ventricular Volume and Strain: A Study on Healthy Volunteers [J].
Tsuneta, Satonori ;
Aono, Satoru ;
Kimura, Rina ;
Kwon, Jihun ;
Fujima, Noriyuki ;
Ishizaka, Kinya ;
Nishioka, Noriko ;
Yoneyama, Masami ;
Kato, Fumi ;
Minowa, Kazuyuki ;
Kudo, Kohsuke .
MAGNETIC RESONANCE IN MEDICAL SCIENCES, 2025, 24 (02) :1-11
[39]   LAVA HyperSense and deep-learning reconstruction for near- isotropic (3D) enhanced magnetic resonance enterography in patients with Crohn's disease: utility in noise reduction and image quality improvement [J].
Son, Jung Hee ;
Lee, Yedaun ;
Lee, Ho-Joon ;
Lee, Joonsung ;
Kim, Hyunwoong ;
Lebel, Marc R. .
DIAGNOSTIC AND INTERVENTIONAL RADIOLOGY, 2023, 29 (03) :437-449
[40]   Breath-hold High-resolution T1-weighted Gradient Echo Liver MR Imaging with Compressed Sensing Obtained during the Gadoxetic Acid-enhanced Hepatobiliary Phase: Image Quality and Lesion Visibility Compared with a Standard T1-weighted Sequence [J].
Ihara, Kenichiro ;
Onoda, Hideko ;
Tanabe, Masahiro ;
Iida, Etsushi ;
Ueda, Takaaki ;
Kobayashi, Taiga ;
Higashi, Mayumi ;
Nickel, Marcel Dominik ;
Imai, Hiroshi ;
Ito, Katsuyoshi .
MAGNETIC RESONANCE IN MEDICAL SCIENCES, 2024, 23 (02) :146-152