Literature DB >> 25355066

Consistent intensity inhomogeneity correction in water-fat MRI.

Thord Andersson1,2, Thobias Romu1,2, Anette Karlsson1,2, Bengt Norén2,3, Mikael F Forsgren2,4, Örjan Smedby2,3, Stergios Kechagias5, Sven Almer6,7, Peter Lundberg2,4, Magnus Borga1,2, Olof Dahlqvist Leinhard2,8.   

Abstract

PURPOSE: To quantitatively and qualitatively evaluate the water-signal performance of the consistent intensity inhomogeneity correction (CIIC) method to correct for intensity inhomogeneities
METHODS: Water-fat volumes were acquired using 1.5 Tesla (T) and 3.0T symmetrically sampled 2-point Dixon three-dimensional MRI. Two datasets: (i) 10 muscle tissue regions of interest (ROIs) from 10 subjects acquired with both 1.5T and 3.0T whole-body MRI. (ii) Seven liver tissue ROIs from 36 patients imaged using 1.5T MRI at six time points after Gd-EOB-DTPA injection. The performance of CIIC was evaluated quantitatively by analyzing its impact on the dispersion and bias of the water image ROI intensities, and qualitatively using side-by-side image comparisons.
RESULTS: CIIC significantly ( P1.5T≤2.3×10-4,P3.0T≤1.0×10-6) decreased the nonphysiological intensity variance while preserving the average intensity levels. The side-by-side comparisons showed improved intensity consistency ( Pint⁡≤10-6) while not introducing artifacts ( Part=0.024) nor changed appearances ( Papp≤10-6).
CONCLUSION: CIIC improves the spatiotemporal intensity consistency in regions of a homogenous tissue type.
© 2014 Wiley Periodicals, Inc.

Entities:  

Keywords:  Dixon imaging; fat quantification; inhomogeneity correction; intensity correction; water; water-fat imaging

Mesh:

Year:  2014        PMID: 25355066     DOI: 10.1002/jmri.24778

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  10 in total

1.  Quantifying Abdominal Adipose Tissue and Thigh Muscle Volume and Hepatic Proton Density Fat Fraction: Repeatability and Accuracy of an MR Imaging-based, Semiautomated Analysis Method.

Authors:  Michael S Middleton; William Haufe; Jonathan Hooker; Magnus Borga; Olof Dahlqvist Leinhard; Thobias Romu; Patrik Tunón; Gavin Hamilton; Tanya Wolfson; Anthony Gamst; Rohit Loomba; Claude B Sirlin
Journal:  Radiology       Date:  2017-03-09       Impact factor: 11.105

Review 2.  MRI adipose tissue and muscle composition analysis-a review of automation techniques.

Authors:  Magnus Borga
Journal:  Br J Radiol       Date:  2018-07-24       Impact factor: 3.039

3.  A quantitative alternative to the Goutallier classification system using Lava Flex and Ideal MRI techniques: volumetric intramuscular fatty infiltration of the supraspinatus muscle, a cadaveric study.

Authors:  Jose H Trevino; Krzysztof R Gorny; Angel Gomez-Cintron; Chunfeng Zhao; Hugo Giambini
Journal:  MAGMA       Date:  2019-09-04       Impact factor: 2.310

4.  Extensibility of the supraspinatus muscle can be predicted by combining shear wave elastography and magnetic resonance imaging-measured quantitative metrics of stiffness and volumetric fat infiltration: A cadaveric study.

Authors:  Hugo Giambini; Taku Hatta; Asghar Rezaei; Kai-Nan An
Journal:  Clin Biomech (Bristol, Avon)       Date:  2018-07-03       Impact factor: 2.063

5.  Intramuscular fat infiltration evaluated by magnetic resonance imaging predicts the extensibility of the supraspinatus muscle.

Authors:  Hugo Giambini; Taku Hatta; Krzysztof R Gorny; Per Widholm; Anette Karlsson; Olof D Leinhard; Mark C Adkins; Chunfeng Zhao; Kai-Nan An
Journal:  Muscle Nerve       Date:  2017-05-15       Impact factor: 3.217

Review 6.  Segmentation and quantification of adipose tissue by magnetic resonance imaging.

Authors:  Houchun Harry Hu; Jun Chen; Wei Shen
Journal:  MAGMA       Date:  2015-09-04       Impact factor: 2.310

7.  Evaluation of bone marrow infiltration in multiple myeloma using whole-body diffusion-weighted imaging and T1-weighted water-fat separation Dixon.

Authors:  Xiaodong Ji; Wenyang Huang; Huazheng Dong; Zhiwei Shen; Meizhu Zheng; Dehui Zou; Wen Shen; Shuang Xia
Journal:  Quant Imaging Med Surg       Date:  2021-02

8.  Manually defining regions of interest when quantifying paravertebral muscles fatty infiltration from axial magnetic resonance imaging: a proposed method for the lumbar spine with anatomical cross-reference.

Authors:  Rebecca J Crawford; Jon Cornwall; Rebecca Abbott; James M Elliott
Journal:  BMC Musculoskelet Disord       Date:  2017-01-19       Impact factor: 2.362

9.  Model-inferred mechanisms of liver function from magnetic resonance imaging data: Validation and variation across a clinically relevant cohort.

Authors:  Mikael F Forsgren; Markus Karlsson; Olof Dahlqvist Leinhard; Nils Dahlström; Bengt Norén; Thobias Romu; Simone Ignatova; Mattias Ekstedt; Stergios Kechagias; Peter Lundberg; Gunnar Cedersund
Journal:  PLoS Comput Biol       Date:  2019-06-25       Impact factor: 4.475

10.  Inter-station intensity standardization for whole-body MR data.

Authors:  Oleh Dzyubachyk; Marius Staring; Monique Reijnierse; Boudewijn P F Lelieveldt; Rob J van der Geest
Journal:  Magn Reson Med       Date:  2016-02-01       Impact factor: 4.668

  10 in total

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