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Volume 8
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Article

Post-Processing Bias Field Inhomogeneity Correction for Assessing Background Parenchymal Enhancement on Breast MRI as a Quantitative Marker of Treatment Response

by
Alex Anh-Tu Nguyen
1,
Natsuko Onishi
1,*,
Julia Carmona-Bozo
1,
Wen Li
1,
John Kornak
2,
David C. Newitt
1 and
Nola M. Hylton
1
1
Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA
2
Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94143, USA
*
Author to whom correspondence should be addressed.
Tomography 2022, 8(2), 891-904; https://doi.org/10.3390/tomography8020072
Submission received: 2 February 2022 / Revised: 15 March 2022 / Accepted: 16 March 2022 / Published: 22 March 2022
(This article belongs to the Special Issue Quantitative Imaging Network)
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Abstract

Background parenchymal enhancement (BPE) of breast fibroglandular tissue (FGT) in dynamic contrast-enhanced breast magnetic resonance imaging (MRI) has shown an association with response to neoadjuvant chemotherapy (NAC) in patients with breast cancer. Fully automated segmentation of FGT for BPE calculation is a challenge when image artifacts are present. Low spatial frequency intensity nonuniformity due to coil sensitivity variations is known as bias or inhomogeneity and can affect FGT segmentation and subsequent BPE measurement. In this study, we utilized the N4ITK algorithm for bias correction over a restricted bilateral breast volume and compared the contralateral FGT segmentations based on uncorrected and bias-corrected images in three MRI examinations at pre-treatment, early treatment and inter-regimen timepoints during NAC. A retrospective analysis of 2 cohorts was performed: one with 735 patients enrolled in the multi-center I-SPY 2 TRIAL and the sub-cohort of 340 patients meeting a high-quality benchmark for segmentation. Bias correction substantially increased the FGT segmentation quality for 6.3–8.0% of examinations, while it substantially decreased the quality for no examination. Our results showed improvement in segmentation quality and a small but statistically significant increase in the resulting BPE measurement after bias correction at all timepoints in both cohorts. Continuing studies are examining the effects on pCR prediction.
Keywords: bias correction; breast cancer; breast MRI; background parenchymal enhancement; neoadjuvant chemotherapy bias correction; breast cancer; breast MRI; background parenchymal enhancement; neoadjuvant chemotherapy
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MDPI and ACS Style

Nguyen, A.A.-T.; Onishi, N.; Carmona-Bozo, J.; Li, W.; Kornak, J.; Newitt, D.C.; Hylton, N.M. Post-Processing Bias Field Inhomogeneity Correction for Assessing Background Parenchymal Enhancement on Breast MRI as a Quantitative Marker of Treatment Response. Tomography 2022, 8, 891-904. https://doi.org/10.3390/tomography8020072

AMA Style

Nguyen AA-T, Onishi N, Carmona-Bozo J, Li W, Kornak J, Newitt DC, Hylton NM. Post-Processing Bias Field Inhomogeneity Correction for Assessing Background Parenchymal Enhancement on Breast MRI as a Quantitative Marker of Treatment Response. Tomography. 2022; 8(2):891-904. https://doi.org/10.3390/tomography8020072

Chicago/Turabian Style

Nguyen, Alex Anh-Tu, Natsuko Onishi, Julia Carmona-Bozo, Wen Li, John Kornak, David C. Newitt, and Nola M. Hylton. 2022. "Post-Processing Bias Field Inhomogeneity Correction for Assessing Background Parenchymal Enhancement on Breast MRI as a Quantitative Marker of Treatment Response" Tomography 8, no. 2: 891-904. https://doi.org/10.3390/tomography8020072

APA Style

Nguyen, A. A.-T., Onishi, N., Carmona-Bozo, J., Li, W., Kornak, J., Newitt, D. C., & Hylton, N. M. (2022). Post-Processing Bias Field Inhomogeneity Correction for Assessing Background Parenchymal Enhancement on Breast MRI as a Quantitative Marker of Treatment Response. Tomography, 8(2), 891-904. https://doi.org/10.3390/tomography8020072

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