Random forest flair reconstruction from T1, T2, and PD-weighted MRI

Amod Jog; Aaron Carass; Dzung L. Pham; Jerry L. Prince

Random forest flair reconstruction from T₁, T₂, and P_D-weighted MRI

Amod Jog, Aaron Carass, Dzung L. Pham, Jerry L. Prince

Whiting School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Scopus citations

Abstract

Fluid Attenuated Inversion Recovery (FLAIR) is a commonly acquired pulse sequence for multiple sclerosis (MS) patients. MS white matter lesions appear hyperintense in FLAIR images and have excellent contrast with the surrounding tissue. Hence, FLAIR images are commonly used in automated lesion segmentation algorithms to easily and quickly delineate the lesions. This expedites the lesion load computation and correlation with disease progression. Unfortunately for numerous reasons the acquired FLAIR images can be of a poor quality and suffer from various artifacts. In the most extreme cases the data is absent, which poses a problem when consistently processing a large data set. We propose to fill in this gap by reconstructing a FLAIR image given the corresponding T₁-weighted, T₂-weighted, and P_D-weighted images of the same subject using random forest regression. We show that the images we produce are similar to true high quality FLAIR images and also provide a good surrogate for tissue segmentation.

Original language	English (US)
Title of host publication	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1079-1082
Number of pages	4
ISBN (Electronic)	9781467319591
State	Published - Jul 29 2014
Event	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China Duration: Apr 29 2014 → May 2 2014

Publication series

Name	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Other

Other	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Country/Territory	China
City	Beijing
Period	4/29/14 → 5/2/14

Keywords

Brain
Image reconstruction
Regression

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Cite this

Random forest flair reconstruction from T₁, T₂, and P_D-weighted MRI. / Jog, Amod; Carass, Aaron; Pham, Dzung L. et al.
2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1079-1082 6868061 (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jog, A, Carass, A, Pham, DL & Prince, JL 2014, Random forest flair reconstruction from T₁, T₂, and P_D-weighted MRI. in 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014., 6868061, 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Institute of Electrical and Electronics Engineers Inc., pp. 1079-1082, 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Beijing, China, 4/29/14.

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abstract = "Fluid Attenuated Inversion Recovery (FLAIR) is a commonly acquired pulse sequence for multiple sclerosis (MS) patients. MS white matter lesions appear hyperintense in FLAIR images and have excellent contrast with the surrounding tissue. Hence, FLAIR images are commonly used in automated lesion segmentation algorithms to easily and quickly delineate the lesions. This expedites the lesion load computation and correlation with disease progression. Unfortunately for numerous reasons the acquired FLAIR images can be of a poor quality and suffer from various artifacts. In the most extreme cases the data is absent, which poses a problem when consistently processing a large data set. We propose to fill in this gap by reconstructing a FLAIR image given the corresponding T1-weighted, T2-weighted, and PD-weighted images of the same subject using random forest regression. We show that the images we produce are similar to true high quality FLAIR images and also provide a good surrogate for tissue segmentation.",

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AB - Fluid Attenuated Inversion Recovery (FLAIR) is a commonly acquired pulse sequence for multiple sclerosis (MS) patients. MS white matter lesions appear hyperintense in FLAIR images and have excellent contrast with the surrounding tissue. Hence, FLAIR images are commonly used in automated lesion segmentation algorithms to easily and quickly delineate the lesions. This expedites the lesion load computation and correlation with disease progression. Unfortunately for numerous reasons the acquired FLAIR images can be of a poor quality and suffer from various artifacts. In the most extreme cases the data is absent, which poses a problem when consistently processing a large data set. We propose to fill in this gap by reconstructing a FLAIR image given the corresponding T1-weighted, T2-weighted, and PD-weighted images of the same subject using random forest regression. We show that the images we produce are similar to true high quality FLAIR images and also provide a good surrogate for tissue segmentation.

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