Dr. Milica Medved Profile

Dr. Milica Medved

Technical Director for Body Imaging, MRI RC at Univ of Chicago

SPIE Involvement:

Author

Area of Expertise:

MRI Physics , quantitative MRI methods , diffusion weighted imaging (DWI) , dynamic contrast-enhanced MRI (DCE-MRI) , breast, prostate, pelvic, urogenital, body MRI , cancer imaging

Publications (3)

Proceedings Article | 3 April 2024 Poster + Paper

Radiomics and quantitative multi-parametric MRI for predicting uterine fibroid growth

Karen Drukker, Milica Medved, Carla Harmath, Maryellen Giger, Obianuju Madueke-Laveaux

Proceedings Volume 12927, 129272Y (2024) https://doi.org/10.1117/12.3005190

KEYWORDS: Magnetic resonance imaging, Radiomics, Principal component analysis, Ultrasonography, Tumors, Tissues, Statistical analysis, Image enhancement, Hazard analysis

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SPIE Journal Paper | 28 December 2016

Breast density estimation from high spectral and spatial resolution MRI

Hui Li, William Weiss, Milica Medved, Hiroyuki Abe, Gillian Newstead, Gregory Karczmar, Maryellen Giger

JMI, Vol. 3, Issue 04, 044507, (December 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.4.044507

KEYWORDS: Breast, Magnetic resonance imaging, Skin, Tissues, Image segmentation, Spatial resolution, Breast cancer, Mammography, Chest, Statistical analysis

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SPIE Journal Paper | 7 May 2015

Preliminary assessment of dispersion versus absorption analysis of high spectral and spatial resolution magnetic resonance images in the diagnosis of breast cancer

William Weiss, Milica Medved, Gregory Karczmar, Maryellen Giger

JMI, Vol. 2, Issue 02, 024502, (May 2015) https://doi.org/10.1117/12.10.1117/1.JMI.2.2.024502

KEYWORDS: Breast, Absorption, Magnetic resonance imaging, Magnetism, Data acquisition, Spatial resolution, Distortion, Breast cancer, Shape analysis, Tumors

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Water resonance lineshapes observed in breast lesions imaged with high spectral and spatial resolution (HiSS) magnetic resonance imaging have been shown to contain diagnostically useful non-Lorentzian components. The purpose of this work is to update a previous method of breast lesion diagnosis by including phase-corrected absorption and dispersion spectra. This update includes information about the shape of the complex water resonance, which could improve the performance of a computer-aided diagnosis breast lesion classification scheme. The non-Lorentzian characteristics observed in complex breast lesion water resonance spectra are characterized by comparing a plot of the real versus imaginary components of the spectrum to that of a perfect complex Lorentzian spectrum, a “dispersion versus absorption” (DISPA) analysis technique. Distortion in the shape of the observed spectra indicates underlying physiologic changes, which have been shown to be correlated with malignancy. These spectral shape distortions in each lesion voxel are quantified by summing the deviations in DISPA radius from an ideal complex Lorentzian spectrum over all Fourier components, yielding a “total radial difference” (TRD). We limited our analysis to those voxels in each lesion with the largest TRD. The number of voxels considered was dependent on the lesion size. The TRD was used to classify voxels from 15 malignant and 8 benign lesions (∼2400 voxels after voxel elimination). Lesion discrimination performance was evaluated for both the average and variance of the TRD within each lesion. Area under the receiver operating characteristic curve (ROC AUC) was used to assess both the voxel- and lesion-based discrimination methods in the task of distinguishing between malignant and benign. In the task of distinguishing voxels from malignant and benign lesions, TRD yielded an AUC of 0.89 (95% confidence interval [0.84, 0.91]). In the task of distinguishing malignant from benign lesions, the average radial difference yielded an AUC of 0.90 (95% confidence interval [0.71, 1.00]) and the variance in the radial difference yielded an AUC of 0.84 (95% confidence interval [0.61, 0.99]). We have applied the DISPA spectroscopic analysis method to HiSS data in order to identify and quantify voxels in breast lesions displaying non-Lorentzian characteristics. We have shown that a breast lesion classification scheme based on the absorption and dispersion spectral data obtained from HiSS acquisitions may outperform a similar classifier based on single off-peak component analysis, as it uses shape details of the entire spectrum instead of the magnitude at a single spectral location.

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