Statistical image quantification toward optimal scan fusion and change quantification

Vaclav Potesil; Xiang Sean Zhou

doi:10.1117/12.710320

30 March 2007 Statistical image quantification toward optimal scan fusion and change quantification

Vaclav Potesil, Xiang Sean Zhou

Proceedings Volume 6514, Medical Imaging 2007: Computer-Aided Diagnosis; 651412 (2007) https://doi.org/10.1117/12.710320
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

Recent advance of imaging technology has brought new challenges and opportunities for automatic and quantitative analysis of medical images. With broader accessibility of more imaging modalities for more patients, fusion of modalities/scans from one time point and longitudinal analysis of changes across time points have become the two most critical differentiators to support more informed, more reliable and more reproducible diagnosis and therapy decisions. Unfortunately, scan fusion and longitudinal analysis are both inherently plagued with increased levels of statistical errors. A lack of comprehensive analysis by imaging scientists and a lack of full awareness by physicians pose potential risks in clinical practice. In this paper, we discuss several key error factors affecting imaging quantification, studying their interactions, and introducing a simulation strategy to establish general error bounds for change quantification across time. We quantitatively show that image resolution, voxel anisotropy, lesion size, eccentricity, and orientation are all contributing factors to quantification error; and there is an intricate relationship between voxel anisotropy and lesion shape in affecting quantification error. Specifically, when two or more scans are to be fused at feature level, optimal linear fusion analysis reveals that scans with voxel anisotropy aligned with lesion elongation should receive a higher weight than other scans. As a result of such optimal linear fusion, we will achieve a lower variance than naïve averaging. Simulated experiments are used to validate theoretical predictions. Future work based on the proposed simulation methods may lead to general guidelines and error lower bounds for quantitative image analysis and change detection.

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Vaclav Potesil and Xiang Sean Zhou "Statistical image quantification toward optimal scan fusion and change quantification", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 651412 (30 March 2007); https://doi.org/10.1117/12.710320

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KEYWORDS

Anisotropy

Monte Carlo methods

Error analysis

Image fusion

Data fusion

Magnetic resonance imaging

Statistical analysis

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