Purpose: To compare and evaluate intensity-based registration methods for computation of serial x-ray mammogram correspondence.
Methods: X-ray mammograms were simulated from MRIs of 20 women using finite element methods for modeling breast compressions and employing a MRI/x-ray appearance change model. The parameter configurations of three registration methods, affine, fluid, and free-form deformation (FFD), were optimized for registering x-ray mammograms on these simulated images. Five mammography film readers independently identified landmarks (tumor, nipple, and usually two other normal features) on pairs of diagnostic and corresponding prediagnostic digitized images from 52 breast cancer cases. Landmarks were independently reidentified by each reader. Target registration errors were calculated to compare the three registration methods using the reader landmarks as a gold standard. Data were analyzed using multilevel methods.
Results: Between-reader variability varied with landmark (p < 0.01) and screen (p = 0.03), with between-reader mean distance (mm) in point location on the diagnostic/prediagnostic images of 2.50 (95% CI 1.95, 3.15)/2.84 (2.24, 3.55) for nipples and 4.26 (3.43, 5.24)/4.76 (3.85, 5.84) for tumors. Registration accuracy was sensitive to the type of landmark and the amount of breast density. For dense breasts (> or = 40%), the affine and fluid methods outperformed FFD. For breasts with lower density, the affine registration surpassed both fluid and FFD. Mean accuracy (mm) of the affine registration varied between 3.16 (95% CI 2.56, 3.90) for nipple points in breasts with density 20%-39% and 5.73 (4.80, 6.84) for tumor points in breasts with density < 20%.
Conclusions: Affine registration accuracy was comparable to that between independent film readers. More advanced two-dimensional nonrigid registration algorithms were incapable of increasing the accuracy of image alignment when compared to affine registration.
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http://dx.doi.org/10.1118/1.3457470 | DOI Listing |
Bioengineering (Basel)
December 2024
Department of Information Engineering, University of Florence, 50139 Florence, Italy.
Image registration is a crucial post-processing technique in biomedical imaging, enabling the alignment and integration of images from various sources to facilitate accurate diagnosis, treatment planning, and longitudinal studies. This paper explores the application of Scale Invariant Feature Transform (SIFT), a robust feature-based method for the alignment of biomedical images. SIFT is particularly advantageous due to its invariance to scale, rotation, and affine transformations, making it well-suited for handling the diverse and complex nature of biomedical images.
View Article and Find Full Text PDFMed Image Anal
December 2024
Faculty of Biomedical Engineering, Technion, Haifa, Israel. Electronic address:
Quantitative analysis of pseudo-diffusion in diffusion-weighted magnetic resonance imaging (DWI) data shows potential for assessing fetal lung maturation and generating valuable imaging biomarkers. Yet, the clinical utility of DWI data is hindered by unavoidable fetal motion during acquisition. We present IVIM-morph, a self-supervised deep neural network model for motion-corrected quantitative analysis of DWI data using the Intra-voxel Incoherent Motion (IVIM) model.
View Article and Find Full Text PDFJ Imaging Inform Med
November 2024
Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
Registration is the process of spatially and/or temporally aligning different images. It is a critical tool that can facilitate the automatic tracking of pathological changes detected in radiological images and align images captured by different imaging systems and/or those acquired using different acquisition parameters. The longitudinal analysis of clinical changes has a significant role in helping clinicians evaluate disease progression and determine the most suitable course of treatment for patients.
View Article and Find Full Text PDFJ Transl Med
November 2024
Molecular Imaging Facility, Experimental Pharmacology & Translational Science Department, Chiesi Farmaceutici S.P.A, 43122, Parma, Italy.
Background: Drug discovery strongly relies on the thorough evaluation of preclinical experimental studies. In the context of pulmonary fibrosis, micro-computed tomography (µCT) and histology are well-established and complementary tools for assessing, in animal models, disease progression and response to treatment. µCT offers dynamic, real-time insights into disease evolution and the effects of therapies, while histology provides a detailed microscopic examination of lung tissue.
View Article and Find Full Text PDFJ Med Imaging (Bellingham)
November 2024
Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States.
Purpose: Eye morphology varies significantly across the population, especially for the orbit and optic nerve. These variations limit the feasibility and robustness of generalizing population-wise features of eye organs to an unbiased spatial reference.
Approach: To tackle these limitations, we propose a process for creating high-resolution unbiased eye atlases.
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