Open AccessQuantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model
Author(s) -
Buch Karen,
Kuno Hirofumi,
Qureshi Muhammad M.,
Li Baojun,
Sakai Osamu
Publication year - 2018
Publication title -
journal of applied clinical medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.83
H-Index - 48
ISSN - 1526-9914
DOI - 10.1002/acm2.12482
Subject(s) - flip angle , scanner , nuclear medicine , imaging phantom , histogram , standard deviation , grey level , materials science , physics , magnetic resonance imaging , nuclear magnetic resonance , mathematics , artificial intelligence , medicine , computer science , optics , statistics , radiology , pixel , image (mathematics)
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 MRI s on a phantom comprised of 18 25‐mm doped, gel‐filled tubes, and 1 20‐mm tube containing 0.25 mM Gd‐ DTPA (EuroSpin II Test Object5, Diagnostic Sonar, Ltd, West Lothian, Scotland). MRI s performed on a 1.5 T GE HD , 1.5 T Siemens Espree ( VB 13), or 3.0 T GE HD with TwinSpeed gradients with an eight‐channel head coil included T1 WI s 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.