Integrated Registration and Harmonization Framework for Quantitative T2-weighted MRI Analysis following Prostate Cancer Radiotherapy

Motivation: The reliable evaluation of T2-weighted MRI (T2w) signal change in prostate cancer following radiotherapy (RT) is challenging due to deformations (physiological and RT-related) and scanner/protocol acquisition variability. Goal(s): To develop an automated and reproducible methodology for quantification of T2w signal change in longitudinal studies following RT. Approach: The methodology includes T2w image intensity harmonization and deformable registration of post-RT to pre-RT images for automated detection of prostate, peripheral zone and tumor volume. Results: The repeatability in T2w intensity estimation improved following the automatic registration relative to manual contours; and the quantitative changes of T2w reached significance when pre- and post-RT series were compared. Impact: The developed methodology allows to automatically detect ROIs in post-RT MRI exams, reduces data acquisition-related variation and improves imaging features’ repeatability, thereby enables the quantitative characterization of RT-induced changes in T2w.

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Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/0543
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Integrated Registration and Harmonization Framework for Quantitative T2-weighted MRI Analysis following Prostate Cancer Radiotherapy

Radka Stoyanova, Matthew K. Abramowitz, Sanoj Punnen, Ahmad Algohary et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Radiomics and Machine Learning in Medical Imaging
article

Integrated Registration and Harmonization Framework for Quantitative T2-weighted MRI Analysis following Prostate Cancer Radiotherapy

Radka Stoyanova, Matthew K. Abramowitz, Sanoj Punnen, Ahmad Algohary, Evangelia I. Zacharaki, Adrian Breto, Veronica Wallaengen, Alan Pollack
article en

Abstract

Motivation: The reliable evaluation of T2-weighted MRI (T2w) signal change in prostate cancer following radiotherapy (RT) is challenging due to deformations (physiological and RT-related) and scanner/protocol acquisition variability. Goal(s): To develop an automated and reproducible methodology for quantification of T2w signal change in longitudinal studies following RT. Approach: The methodology includes T2w image intensity harmonization and deformable registration of post-RT to pre-RT images for automated detection of prostate, peripheral zone and tumor volume. Results: The repeatability in T2w intensity estimation improved following the automatic registration relative to manual contours; and the quantitative changes of T2w reached significance when pre- and post-RT series were compared. Impact: The developed methodology allows to automatically detect ROIs in post-RT MRI exams, reduces data acquisition-related variation and improves imaging features’ repeatability, thereby enables the quantitative characterization of RT-induced changes in T2w.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
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Openalex Percentile: Top 63%
Radiomics and Machine Learning in Medical Imaging
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Integrated Registration and Harmonization Framework for Quantitative T2-weighted MRI Analysis following Prostate Cancer Radiotherapy — Radka Stoyanova, Matthew K. Abramowitz, et al. · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (2024) | TGRS Research Map | TGRS