跨模态医学图像预测综述
Review of Cross-Modality Medical Image Prediction
查看参考文献48篇
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文摘
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医学影像技术与设备的进步在生物医学领域的各项研究中发挥着重要作用.跨模态医学图像预测旨在由一种模态图像预测另一种模态图像.本文详细综述了由MRI预测CT图像、7T-Like图像重构、PET预测及其他医学模态预测研究,阐述了各类模态预测的必要性及存在的挑战,说明各类预测方法的特点并进行性能比较,最终得出结论:基于深度学习的跨模态预测在预测精度和预测时间两方面更具优势. |
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其他语种文摘
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Advances in medical imaging technologies and equipment play an important role in the biomedical researches. Cross-modality image-prediction technology predicts one modal image from that of another modal. This paper presents an overview of the literatures on medical imaging prediction technology and its applications, such as predicting Computed Tomography images from Magnetic Resonance (MR) images,7T-like MR image reconstruction, and predicting positron emission tomography images. The aim is twofold: the necessity and challenge for different modality medical image prediction technology; the overview and comparison of various methods in the field. We conclude that the cross-modality image prediction based on the deep learning technology has superiority in both predicting time and precision. |
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来源
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电子学报
,2019,47(1):220-226 【核心库】
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DOI
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10.3969/j.issn.0372-2112.2019.01.029
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关键词
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深度学习
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CT预测
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7T-Like图像重构
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PET预测
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地址
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1.
北京交通大学, 北京, 100044
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北京大学, 北京, 100871
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语种
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中文 |
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文献类型
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综述型 |
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ISSN
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0372-2112 |
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学科
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自动化技术、计算机技术 |
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基金
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国家自然科学基金
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国家自然科学基金
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文献收藏号
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CSCD:6437293
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参考文献 共
48
共3页
|
|
1.
康雁.
医学成像技术与系统,2014
|
CSCD被引
2
次
|
|
|
|
|
2.
田捷.
光学分子影像技术及其应用,2010
|
CSCD被引
1
次
|
|
|
|
|
3.
Greenspan H. Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique.
IEEE Transactions on Medical Imaging,2016,35(5):1153-1159
|
CSCD被引
47
次
|
|
|
|
|
4.
Brenner D J. Computed tomography-an increasing source of radiation exposure.
The New England Journal of Medicine,2007,357(22):2277-2284
|
CSCD被引
286
次
|
|
|
|
|
5.
Edmund J M. A review of substitute CT generation for MRI-only radiation therapy.
Radiation Oncology,2017,12(1):28
|
CSCD被引
12
次
|
|
|
|
|
6.
Pereira G C.
The role of imaging in radiation therapy planning: past,present, and future. 2014,2014:1-9
|
CSCD被引
1
次
|
|
|
|
|
7.
黄力宇.
医学成像的基本原理,2009
|
CSCD被引
1
次
|
|
|
|
|
8.
王海鹏. PET/MRI衰减校正技术研究进展.
中国医学影像技术,2014(2):295-299
|
CSCD被引
5
次
|
|
|
|
|
9.
Hofmann M. MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and atlas registration.
Journal of Nuclear Medicine,2008,49(11):1875-1883
|
CSCD被引
10
次
|
|
|
|
|
10.
Hofmann M. MRI-based attenuation correction for whole-body PET/MRI: quantitative evaluation of segmentation and atlas-based methods.
Journal of Nuclear Medicine,2011,52(9):1392-1399
|
CSCD被引
6
次
|
|
|
|
|
11.
Arabi H. Magnetic resonance imaging-guided attenuation correction in whole-body PET/MRI using a sorted atlas approach.
Medical Image Analysis,2016,31:1
|
CSCD被引
1
次
|
|
|
|
|
12.
Burgos N. Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies.
IEEE Transactions on Medical Imaging,2014,33(12):2332-2341
|
CSCD被引
5
次
|
|
|
|
|
13.
Burgos N. Iterative framework for the joint segmentation and CT synthesis of MR images: application to MRI-only radiotherapy treatment planning.
Physics in Medicine and Biology,2017,62(11):4237-4253
|
CSCD被引
1
次
|
|
|
|
|
14.
Arabi H. One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI.
European Journal of Nuclear Medicine and Molecular Imaging,2016,43(11):2021-2035
|
CSCD被引
2
次
|
|
|
|
|
15.
Andreasen D. A patch-based pseudo-CT approach for MRI-only radiotherapy in the pelvis.
Medical Physics,2016,43(8):4742-4752
|
CSCD被引
6
次
|
|
|
|
|
16.
Torrado-Carvajal A. Fast patchbased pseudo-CT synthesis from T1-weighted MR images for PET/MR attenuation correction in brain studies.
Journal of Nuclear Medicine,2016,57(1):136-143
|
CSCD被引
2
次
|
|
|
|
|
17.
Breiman L. Random forests.
Machine Learning,2001,45(1):5-32
|
CSCD被引
3408
次
|
|
|
|
|
18.
Huynh T. Estimating CT image from MRI data using structured random forest and auto-context model.
IEEE Transactions on Medical Imaging,2015,35(1):174-183
|
CSCD被引
9
次
|
|
|
|
|
19.
Tu Z. Auto-context and its application to high-level vision tasks and 3D brain image segmentation.
IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(10):1744-1757
|
CSCD被引
9
次
|
|
|
|
|
20.
Andreasen D. Computed tomography synthesis from magnetic resonance images in the pelvis using multiple random forests and auto-context features.
Medical Imaging 2016: Image Processing,2016:9784
|
CSCD被引
1
次
|
|
|
|
|
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