Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model.

Abstract:

OBJECTIVES:To evaluate the influence of MRI scanning parameters on texture analysis features. METHODS:Publicly available data from the Reference Image Database to Evaluate Therapy Response (RIDER) project sponsored by The Cancer Imaging Archive included MRIs on a phantom comprised of 18 25-mm doped, gel-filled tubes, and 1 20-mm tube containing 0.25 mM Gd-DTPA (EuroSpinII Test Object5, Diagnostic Sonar, Ltd, West Lothian, Scotland). MRIs performed on a 1.5 T GE HD, 1.5 T Siemens Espree (VB13), or 3.0 T GE HD with TwinSpeed gradients with an eight-channel head coil included T1WIs with multiple flip angles (flip-angle = 2,5,10,15,20,25,30), TR/TE = 4.09-5.47/0.90-1.35 ms, NEX = 1 and DCE with 30° flip-angle, TR/TE=4.09-5.47/0.90-1.35, and NEX = 1,4. DICOM data were imported into an in-house developed texture analysis program which extracted 41-texture features including histogram, gray-level co-occurrence matrix (GLCM), and gray-level run-length (GLRL). Two-tailed t tests, corrected for multiple comparisons (Q values) were calculated to compare changes in texture features with variations in MRI scanning parameters (magnet strength, flip-angle, number of excitations (NEX), scanner platform). RESULTS:Significant differences were seen in histogram features (mean, median, standard deviation, range) with variations in NEX (Q = 0.003-0.045) and scanner platform (Q < 0.0001), GLCM features (entropy, contrast, energy, and homogeneity) with NEX (Q = 0.001-0.018) and scanner platform (Q < 0.0001), GLRL features (long-run emphasis, high gray-level run emphasis, high gray-level emphasis) with magnet strength (Q = 0.0003), NEX (Q = 0.003-0.022) and scanner platform (Q < 0.0001). CONCLUSION:Significant differences were seen in many texture features with variations in MRI acquisition emphasizing the need for standardized MRI technique.

journal_name

J Appl Clin Med Phys

authors

Buch K,Kuno H,Qureshi MM,Li B,Sakai O

doi

10.1002/acm2.12482

subject

Has Abstract

pub_date

2018-11-01 00:00:00

pages

253-264

issue

6

issn

1526-9914

journal_volume

19

pub_type

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