Garrett Simpson1, John C Ford1, Ricardo Llorente1, Lorraine Portelance1, Fei Yang1, Eric A Mellon1, Nesrin Dogan2. 1. Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA. 2. Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA. Electronic address: ndogan@med.miami.edu.
Abstract
PURPOSE: The purpose of this work was to investigate the impact of quantization preprocessing parameter selection on variability and repeatability of texture features derived from low field strength magnetic resonance (MR) images. METHODS: Texture features were extracted from low field strength images of a daily image QA phantom with four texture inserts. Feature variability over time was quantified using all combinations of three quantization algorithms and four different numbers of gray level intensities. In addition, texture features were extracted using the same combinations from the low field strength MR images of the gross tumor volume (GTV) and left kidney of patients with repeated set up scans. The impact of region of interest (ROI) preprocessing on repeatability was investigated with a test-retest study design. RESULTS: The phantom ROIs quantized to 64 Gy level intensities using the histogram equalization method resulted in the greatest number of features with the least variability. There was no clear method that resulted in the highest repeatability in the GTV or left kidney. However, eight texture features extracted from the GTV were repeatable regardless of ROI processing combination. CONCLUSION: Low field strength MR images can provide a stable basis for texture analysis with ROIs quantized to 64 Gy levels using histogram equalization, but there is no clear optimal combination for repeatability.
PURPOSE: The purpose of this work was to investigate the impact of quantization preprocessing parameter selection on variability and repeatability of texture features derived from low field strength magnetic resonance (MR) images. METHODS: Texture features were extracted from low field strength images of a daily image QA phantom with four texture inserts. Feature variability over time was quantified using all combinations of three quantization algorithms and four different numbers of gray level intensities. In addition, texture features were extracted using the same combinations from the low field strength MR images of the gross tumor volume (GTV) and left kidney of patients with repeated set up scans. The impact of region of interest (ROI) preprocessing on repeatability was investigated with a test-retest study design. RESULTS: The phantom ROIs quantized to 64 Gy level intensities using the histogram equalization method resulted in the greatest number of features with the least variability. There was no clear method that resulted in the highest repeatability in the GTV or left kidney. However, eight texture features extracted from the GTV were repeatable regardless of ROI processing combination. CONCLUSION: Low field strength MR images can provide a stable basis for texture analysis with ROIs quantized to 64 Gy levels using histogram equalization, but there is no clear optimal combination for repeatability.
Authors: Ryder M Schmidt; Rodrigo Delgadillo; John C Ford; Kyle R Padgett; Matthew Studenski; Matthew C Abramowitz; Benjamin Spieler; Yihang Xu; Fei Yang; Nesrin Dogan Journal: Sci Rep Date: 2021-11-23 Impact factor: 4.379
Authors: Garrett Simpson; William Jin; Benjamin Spieler; Lorraine Portelance; Eric Mellon; Deukwoo Kwon; John C Ford; Nesrin Dogan Journal: Front Oncol Date: 2022-04-19 Impact factor: 5.738
Authors: R W Y Granzier; A Ibrahim; S Primakov; S A Keek; I Halilaj; A Zwanenburg; S M E Engelen; M B I Lobbes; P Lambin; H C Woodruff; M L Smidt Journal: J Magn Reson Imaging Date: 2021-12-22 Impact factor: 5.119
Authors: Luís Vinícius de Moura; Christian Mattjie; Caroline Machado Dartora; Rodrigo C Barros; Ana Maria Marques da Silva Journal: Front Digit Health Date: 2022-01-17