Faculty Collaboration Database

Noise-robust foreground segmentation of multispectral imaging calibration volume in the presence of metallic implants for spectral range estimation in phantom and in-vivo data. Magn Reson Imaging 2025 Oct;122:110432

Date

06/04/2025

Pubmed ID

40460945

DOI

10.1016/j.mri.2025.110432

Scopus ID

2-s2.0-105007056887 (requires institutional sign-in at Scopus site)

Abstract

PURPOSE: Multispectral Imaging (MSI) methods can use a calibration scan to estimate an off-resonance field-map to determine the spectral range required to cover off-resonant signal in the presence of metallic implants of various shape and composition. Background signal noise can corrupt the field-map estimation in this calibration process. Previous work on foreground segmentation used a cumulative distribution function (CDF) to remove signal extrema, which can remove regions of true off-resonance signal from the calibration analysis. The purpose of this work is to develop a foreground segmentation method robust to background noise in both phantom and in-vivo data to support calibrating the spectral range needed for MSI acquisitions.

METHODS: The proposed method uses information from individual spectral bins, rather than a composite bin-combined image, for segmentation. Ten phantom (seven with metal) and ten in-vivo (six with metal) data were acquired using a prototype MSI spectral calibration sequence. Field-maps were estimated and spectral range estimates from the unmasked field-map and the proposed method were computed and compared using a paired sample Wilcoxon signed-rank test.

RESULTS: The proposed method achieved a noise-robust foreground segmentation in both phantom and in-vivo data, in the presence or absence of metal devices. The Wilcoxon test showed a statistically significant difference between the spectral range estimates from the unmasked field-map and proposed method for both the phantom and in-vivo data (p-value: 0.002).

CONCLUSION: Noise-robust foreground segmentation achieved by the proposed method can improve the accuracy and robustness of spectral range estimates for time-efficient and reduced artifact multispectral imaging.

Author List

Chebrolu VV, Nittka M, von Deuster C, Sharafi A, Nencka A, Potter HG, Koch KM

Authors

Kevin M. Koch PhD Adjunct Professor in the Radiology department at Medical College of Wisconsin
Andrew S. Nencka PhD Center Director, Professor in the Radiology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Algorithms
Artifacts
Calibration
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Metals
Phantoms, Imaging
Prostheses and Implants
Reproducibility of Results
Sensitivity and Specificity
Signal-To-Noise Ratio