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

人工智能 计算机科学 计算机视觉 图像质量 双三次插值 相似性(几何) 图像分辨率 模式识别(心理学) 图像(数学) 线性插值
作者
Tianyu Liu,Shuai Han,Linru Xie,Wenyu Xing,Chengcheng Liu,Boyi Li,Dean Ta
出处
期刊:Physics in Medicine and Biology [IOP Publishing]
卷期号:69 (12): 125026-125026 被引量:16
标识
DOI:10.1088/1361-6560/ad4086
摘要

Abstract 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 2× , 4× , and 8× 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 (2× , 4× , and 8× ), 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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
TonyXWZhang完成签到,获得积分10
刚刚
小鱼发布了新的文献求助10
1秒前
燕十三完成签到,获得积分10
1秒前
醋醋发布了新的文献求助10
1秒前
Sakura发布了新的文献求助10
2秒前
小怪完成签到,获得积分10
2秒前
LLL发布了新的文献求助10
2秒前
哈哈哈发布了新的文献求助10
2秒前
今日上上签完成签到,获得积分10
2秒前
小金鱼发布了新的文献求助20
2秒前
springlrt的应助被Lny采纳,获得20
2秒前
Michelle完成签到 ,获得积分10
2秒前
xiaopanpan完成签到 ,获得积分10
2秒前
科研通AI6.4的应助被rick采纳,获得10
3秒前
睡不醒的喵完成签到,获得积分10
3秒前
常姗完成签到,获得积分10
3秒前
3秒前
复杂含之完成签到,获得积分10
3秒前
wanci的应助被DWRH采纳,获得10
3秒前
4秒前
HY发布了新的文献求助10
4秒前
安详凡松发布了新的文献求助10
4秒前
5秒前
荣高烽完成签到,获得积分10
5秒前
科研通AI6.4的应助被22采纳,获得10
5秒前
鸥羡完成签到,获得积分10
5秒前
5秒前
5秒前
5秒前
ljjjjj完成签到,获得积分10
5秒前
6秒前
谁是mxh发布了新的文献求助10
6秒前
6秒前
6秒前
7秒前
大方树叶发布了新的文献求助10
7秒前
7秒前
科研通AI6.4的应助被复杂含之采纳,获得10
8秒前
密斯特蟹完成签到,获得积分10
8秒前
起风了1995完成签到,获得积分10
8秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Das Matthäusevangelium: Kapitel 1:1–17 1000
IRTSD: Open-Source Data and Toolset for Electromagnetic Transient Analysis of Disturbances and IBR Control Malfunctions in Transmission Systems 1000
Entre Praga y Madrid : los contactos checoslovaco-españoles (1948-1977) 600
Les Mots du discours 600
A Consideration on Use of Material Grades for Hull Structural Members Based on Fracture Toughness(Second Report) 500
AI Disclosure in Annual Reports: Evolution, Determinants, and Capital Market Participants’ Perceptions 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 工程类 有机化学 化学工程 生物化学 物理 复合材料 内科学 催化作用 光电子学 人工智能 细胞生物学 无机化学 物理化学 数学
热门帖子
关注 科研通微信公众号,转发送积分 8116181
求助须知:如何正确求助?哪些是违规求助? 9522110
关于积分的说明 21537360
捐赠科研通 7694465
什么是DOI,文献DOI怎么找? 3413819
关于科研通互助平台的介绍 2540033
邀请新用户注册赠送积分活动 2453711