Random forest regression for magnetic resonance image synthesis.
Random forest regression for magnetic resonance image synthesis.
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DOI：
10.1016/j.media.2016.08.009
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发表时间：
2017-01
影响因子：
10.9
通讯作者：
Prince, Jerry L.
中科院分区：
文献类型：
--
作者：
Jog, Amod;Carass, Aaron;Roy, Snehashis;Pham, Dzung L.;Prince, Jerry L.
通过选择不同的脉冲序列及其参数，磁共振成像（MRI）可以产生各种各样的组织对比度。然而，这种灵活性可能会在数据集或扫描会话中产生MRI采集的不一致性，进而导致不一致的自动图像分析。尽管MR图像的图像合成已被证明有助于解决该问题，但已报道无法合成包括颅骨和流体衰减反转恢复（FLAIR）图像的T2加权脑图像。本文所述的称为REPLICA的方法解决了这些限制。REPLICA是一种有监督的随机森林图像合成方法，它学习非线性回归，以预测给定特定输入组织对比度的替代组织对比度的强度。实验结果包括合成和真实的图像之间的直接图像比较，合成和真实的图像上的图像分析任务的结果，以及与其他国家的最先进的图像合成方法的比较。REPLICA是计算速度快，并被证明是与其他方法的任务，他们能够执行。此外，REPLICA能够合成全头部的T2加权图像和FLAIR图像，并在不同成像数据集之间执行强度标准化。
By choosing different pulse sequences and their parameters, magnetic resonance imaging (MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield inconsistencies with MRI acquisitions across datasets or scanning sessions that can in turn cause inconsistent automated image analysis. Although image synthesis of MR images has been shown to be helpful in addressing this problem, an inability to synthesize both T2-weighted brain images that include the skull and FLuid Attenuated Inversion Recovery (FLAIR) images has been reported. The method described herein, called REPLICA, addresses these limitations. REPLICA is a supervised random forest image synthesis approach that learns a nonlinear regression to predict intensities of alternate tissue contrasts given specific input tissue contrasts. Experimental results include direct image comparisons between synthetic and real images, results from image analysis tasks on both synthetic and real images, and comparison against other state-of-the-art image synthesis methods. REPLICA is computationally fast, and is shown to be comparable to other methods on tasks they are able to perform. Additionally REPLICA has the capability to synthesize both T2-weighted images of the full head and FLAIR images, and perform intensity standardization between different imaging datasets.
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简单的范式去除外部组织：算法和分析。
影响因子：
5.7
作者：
Carass, Aaron;Cuzzocreo, Jennifer;Wheeler, M. Bryan;Bazin, Pierre-Louis;Resnick, Susan M.;Prince, Jerry L.
通讯作者：
Prince, Jerry L.
影响因子：
5.7
作者：
Landman BA;Huang AJ;Gifford A;Vikram DS;Lim IA;Farrell JA;Bogovic JA;Hua J;Chen M;Jarso S;Smith SA;Joel S;Mori S;Pekar JJ;Barker PB;Prince JL;van Zijl PC
通讯作者：
van Zijl PC
影响因子：
10.6
作者：
Burgos, Ninon;Cardoso, M. Jorge;Ourselin, Sebastien
通讯作者：
Ourselin, Sebastien
DOI：
10.1073/pnas.90.24.11944
发表时间：
1993-12-15
影响因子：
11.1
作者：
MILLER, MI;CHRISTENSEN, GE;GRENANDER, U
通讯作者：
GRENANDER, U
影响因子：
8.1
作者：
Llado, Xavier;Oliver, Arnau;Rovira, Alex
通讯作者：
Rovira, Alex