Super-resolution reconstruction of ultrasound image using a modified diffusion model

被引:1
|
作者
Liu, Tianyu [1 ]
Han, Shuai [1 ]
Xie, Linru [1 ]
Xing, Wenyu [1 ]
Liu, Chengcheng [1 ,2 ]
Li, Boyi [1 ]
Ta, Dean [2 ,3 ]
机构
[1] Fudan Univ, Inst Biomed Engn & Technol, Acad Engn & Technol, Shanghai 200433, Peoples R China
[2] Fudan Univ, State Key Lab Integrated Chips & Syst, Shanghai 201203, Peoples R China
[3] Fudan Univ, Sch Informat Sci & Technol, Dept Biomed Engn, Shanghai 200438, Peoples R China
来源
PHYSICS IN MEDICINE AND BIOLOGY | 2024年 / 69卷 / 12期
基金
中国国家自然科学基金;
关键词
ultrasound image; super-resolution; diffusion model; multi-layer self-attention; wavelet transform; RESOLUTION;
D O I
10.1088/1361-6560/ad4086
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Objective. This study aims to perform super-resolution (SR) reconstruction of ultrasound images using a modified diffusion model, designated as the diffusion model for ultrasound image super-resolution (DMUISR). SR involves converting low-resolution images to high-resolution ones, and the proposed model is designed to enhance the suitability of diffusion models for this task in the context of ultrasound imaging. Approach. DMUISR incorporates a multi-layer self-attention (MLSA) mechanism and a wavelet-transform based low-resolution image (WTLR) encoder to enhance its suitability for ultrasound image SR tasks. The model takes interpolated and magnified images as input and outputs high-quality, detailed SR images. The study utilized 1,334 ultrasound images from the public fetal head-circumference dataset (HC18) for evaluation. Main results. Experiments were conducted at 2x , 4x , and 8x magnification factors. DMUISR outperformed mainstream ultrasound SR methods (Bicubic, VDSR, DECUSR, DRCN, REDNet, SRGAN) across all scales, providing high-quality images with clear structures and rich detailed textures in both hard and soft tissue regions. DMUISR successfully accomplished multiscale SR reconstruction while suppressing over-smoothing and mode collapse problems. Quantitative results showed that DMUISR achieved the best performance in terms of learned perceptual image patch similarity, with a significant decrease of over 50% at all three magnification factors (2x , 4x , and 8x ), as well as improvements in peak signal-to-noise ratio and structural similarity index measure. Ablation experiments validated the effectiveness of the MLSA mechanism and WTLR encoder in improving DMUISR's SR performance. Furthermore, by reducing the number of diffusion steps, the computational time of DMUISR was shortened to nearly one-tenth of its original while maintaining image quality without significant degradation. Significance. This study demonstrates that the modified diffusion model, DMUISR, provides superior performance for SR reconstruction of ultrasound images and has potential in improving imaging quality in the medical ultrasound field.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Improved Super-Resolution Image Reconstruction Algorithm
    Qu Haicheng
    Tang Bowen
    Yuan Guisen
    LASER & OPTOELECTRONICS PROGRESS, 2021, 58 (02)
  • [42] Super-resolution image reconstruction for mobile devices
    Chung-Hua Chu
    Multimedia Systems, 2013, 19 : 315 - 337
  • [43] Survey of single image super-resolution reconstruction
    Li, Kai
    Yang, Shenghao
    Dong, Runting
    Wang, Xiaoying
    Huang, Jianqiang
    IET IMAGE PROCESSING, 2020, 14 (11) : 2273 - 2290
  • [44] Image acquisition modeling for super-resolution reconstruction
    Gevrekei, M
    Gunturk, BK
    2005 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), VOLS 1-5, 2005, : 2157 - 2160
  • [45] Performance Verification of Super-Resolution Image Reconstruction
    Sugie, Masaki
    Gohshi, Seiichi
    2013 INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING AND COMMUNICATIONS SYSTEMS (ISPACS), 2013, : 547 - 552
  • [46] Super-resolution image reconstruction: A technical overview
    Park, SC
    Park, MK
    Kang, MG
    IEEE SIGNAL PROCESSING MAGAZINE, 2003, 20 (03) : 21 - 36
  • [47] Super-resolution reconstruction method of image registration
    Qin, Feng-Qing
    He, Xiao-Hai
    Chen, Wei-Long
    Wu, Wei
    Yang, Xiao-Min
    Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2009, 17 (02): : 409 - 416
  • [48] Single image super-resolution reconstruction method
    Tao, Hongjiu
    Rao, Junfei
    Zhou, Zude
    Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering), 2004, 28 (06):
  • [49] An Overview of Image Super-resolution Reconstruction Algorithm
    Niu, Xiaoming
    2018 11TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 2, 2018, : 16 - 18
  • [50] Super-resolution image reconstruction for mobile devices
    Chu, Chung-Hua
    MULTIMEDIA SYSTEMS, 2013, 19 (04) : 315 - 337