Jessie Q Xia | PubMed Health

PubMed2005

Computer-aided detection of amorphous calcifications.

OBJECTIVE: Computer-aided detection (CAD) systems have been used successfully to detect malignant calcifications on mammography, with sensitivities ranging from 86% to 99%. Amorphous calcifications are a subset of small indistinct calcifications of intermediate concern that have a 20% likelihood of being malignant and that are frequently overlooked on mammography. The purpose of our study was to determine the sensitivity of one commercially available CAD system for detecting amorphous calcifications. MATERIALS AND METHODS: A commercially available CAD system evaluated mammograms of 82 patients with 85 mammographically detected and histologically sampled groups of amorphous calcifications (21 malignant, 14 high risk, and 50 benign). The sensitivity of the system for detecting the calcifications on at least one image of the two-view mammographic examination (case sensitivity) and on each individual mammographic image (image sensitivity) was determined. Findings were correlated with results from large core needle biopsy or surgical excision in each case. RESULTS: The CAD system detected amorphous calcifications in 43 of 85 cases (case sensitivity, 51%) and in 59 of 146 mammographic images (image sensitivity, 40%). The case sensitivities by histologic outcome were 57% for malignant calcifications, 29% for high-risk calcifications, and 54% for benign calcifications. An average of 2.0 false-positive marks were displayed per case. CONCLUSION: The CAD sensitivity for malignant amorphous calcifications is markedly lower than previously reported for all malignant calcifications. Breast imaging radiologists who use CAD systems should continue to search diligently for these difficult-to-detect lesions.

Adult↗

PubMed2004

High-resolution determination of soft tissue deformations using MRI and first-order texture correlation.

Mechanical factors such as deformation and strain are thought to play important roles in the maintenance, repair, and degeneration of soft tissues. Determination of soft tissue static deformation has traditionally only been possible at a tissue's surface, utilizing external markers or instrumentation. Texture correlation is a displacement field measurement technique which relies on unique image patterns within a pair of digital images to track displacement. The technique has recently been applied to MR images, indicating the possibility of high-resolution displacement and strain field determination within the mid-substance of soft tissues. However, the utility of MR texture correlation analysis may vary amongst tissue types depending on their underlying structure, composition, and contrast mechanism, which give rise to variations in texture with MRI. In this study, we investigate the utility of a texture correlation algorithm with first-order displacement mapping terms for use with MR images, and suggest a novel index of image "roughness" as a way to decrease errors associated with the use of texture correlation for intra-tissue strain measurement with MRI. We find that a first-order algorithm can significantly reduce strain measurement error, and that an image "roughness" index correlates with displacement measurement error for a variety of imaging conditions and tissue types.

Algorithms↗