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Local contrast‐enhanced MR images via high dynamic range processing
Author(s) -
Chandra Shekhar S.,
Engstrom Craig,
Fripp Jurgen,
Neubert Ales,
Jin Jin,
Walker Duncan,
Salvado Olivier,
Ho Charles,
Crozier Stuart
Publication year - 2018
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27109
Subject(s) - voxel , computer science , segmentation , artificial intelligence , computer vision , breast mri , visualization , contrast (vision) , feature (linguistics) , pattern recognition (psychology) , medicine , linguistics , philosophy , cancer , breast cancer , mammography
Purpose To develop a local contrast‐enhancing and feature‐preserving high dynamic range (HDR) image processing algorithm for multichannel and multisequence MR images of multiple body regions and tissues, and to evaluate its performance for structure visualization, bias field (correction) mitigation, and automated tissue segmentation. Methods A multiscale‐shape and detail‐enhancement HDR‐MRI algorithm is applied to data sets of multichannel and multisequence MR images of the brain, knee, breast, and hip. In multisequence 3T hip images, agreement between automatic cartilage segmentations and corresponding synthesized HDR‐MRI series were computed for mean voxel overlap established from manual segmentations for a series of cases. Qualitative comparisons between the developed HDR‐MRI and standard synthesis methods were performed on multichannel 7T brain and knee data, and multisequence 3T breast and knee data. Results The synthesized HDR‐MRI series provided excellent enhancement of fine‐scale structure from multiple scales and contrasts, while substantially reducing bias field effects in 7T brain gradient echo, T 1 and T 2 breast images and 7T knee multichannel images. Evaluation of the HDR‐MRI approach on 3T hip multisequence images showed superior outcomes for automatic cartilage segmentations with respect to manual segmentation, particularly around regions with hyperintense synovial fluid, across a set of 3D sequences. Conclusion The successful combination of multichannel/sequence MR images into a single‐fused HDR‐MR image format provided consolidated visualization of tissues within 1 omnibus image, enhanced definition of thin, complex anatomical structures in the presence of variable or hyperintense signals, and improved tissue (cartilage) segmentation outcomes.