Digital reference object toolkit of breast DCE MRI for quantitative evaluation of image reconstruction and analysis methods

计算机科学 加权 正规化(语言学) 人工智能 模式识别(心理学) 计算机视觉 数据挖掘 放射科 医学
作者
Jonghyun Bae,Zhengguo Tan,Eddy Solomon,Zhengnan Huang,Laura Heacock,Linda Moy,Florian Knöll,Sungheon Kim
出处
期刊:Magnetic Resonance in Medicine [Wiley]
卷期号:92 (4): 1728-1742
标识
DOI:10.1002/mrm.30152
摘要

Abstract Purpose To develop a digital reference object (DRO) toolkit to generate realistic breast DCE‐MRI data for quantitative assessment of image reconstruction and data analysis methods. Methods A simulation framework in a form of DRO toolkit has been developed using the ultrafast and conventional breast DCE‐MRI data of 53 women with malignant ( n = 25) or benign ( n = 28) lesions. We segmented five anatomical regions and performed pharmacokinetic analysis to determine the ranges of pharmacokinetic parameters for each segmented region. A database of the segmentations and their pharmacokinetic parameters is included in the DRO toolkit that can generate a large number of realistic breast DCE‐MRI data. We provide two potential examples for our DRO toolkit: assessing the accuracy of an image reconstruction method using undersampled simulated radial k‐space data and assessing the impact of the field inhomogeneity on estimated parameters. Results The estimated pharmacokinetic parameters for each region showed agreement with previously reported values. For the assessment of the reconstruction method, it was found that the temporal regularization resulted in significant underestimation of estimated parameters by up to 57% and 10% with the weighting factor λ = 0.1 and 0.01, respectively. We also demonstrated that spatial discrepancy of and increase to about 33% and 51% without correction for field. Conclusion We have developed a DRO toolkit that includes realistic morphology of tumor lesions along with the expected pharmacokinetic parameter ranges. This simulation framework can generate many images for quantitative assessment of DCE‐MRI reconstruction and analysis methods.

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
知尘发布了新的文献求助10
刚刚
刚刚
刚刚
1秒前
Ava应助汪汪队立大功采纳,获得10
1秒前
JHHHH完成签到,获得积分10
1秒前
故意的姿发布了新的文献求助10
1秒前
NexusExplorer应助xian采纳,获得10
1秒前
橘子小狗发布了新的文献求助10
1秒前
3秒前
3秒前
3秒前
夏时安完成签到 ,获得积分10
3秒前
李依发布了新的文献求助30
3秒前
badada完成签到,获得积分10
3秒前
酷波er应助疯狂的代曼采纳,获得10
3秒前
4秒前
4秒前
5秒前
134完成签到,获得积分10
5秒前
5秒前
nnnn发布了新的文献求助10
5秒前
哈哈哈发布了新的文献求助10
5秒前
5秒前
6秒前
MIZU应助顾思凡采纳,获得10
6秒前
shiyu发布了新的文献求助10
6秒前
高数数完成签到 ,获得积分10
6秒前
6秒前
6秒前
zyl完成签到,获得积分10
7秒前
研途顺利发布了新的文献求助10
7秒前
7秒前
独特的沛凝完成签到,获得积分10
7秒前
刘子龙发布了新的文献求助10
8秒前
冷酷栾发布了新的文献求助10
8秒前
levi发布了新的文献求助10
8秒前
清脆凝云发布了新的文献求助10
8秒前
芒果完成签到,获得积分10
9秒前
charles发布了新的文献求助10
9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
Introduction to strong mixing conditions volume 1-3 5000
Clinical Microbiology Procedures Handbook, Multi-Volume, 5th Edition 2000
The Cambridge History of China: Volume 4, Sui and T'ang China, 589–906 AD, Part Two 1000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1000
Real World Research, 5th Edition 800
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5718886
求助须知:如何正确求助?哪些是违规求助? 5254421
关于积分的说明 15287351
捐赠科研通 4868927
什么是DOI,文献DOI怎么找? 2614473
邀请新用户注册赠送积分活动 1564399
关于科研通互助平台的介绍 1521791