Detection of microcalcifications by characteristic magnetic susceptibility effects using MR phase image cross‐correlation analysis

Abstract

To develop and evaluate a new method for detecting calcium deposits using their characteristic magnetic susceptibility effects on magnetic resonance (MR) images at high fields and demonstrate its potential in practice for detecting breast microcalcifications. Characteristic dipole signatures of calcium deposits were detected in magnetic resonance phase images by computing the cross-correlation between the acquired data and a library of templates containing simulated phase patterns of spherical deposits. The influence of signal-to-noise ratio and various other MR parameters on the results were assessed using simulations and validated experimentally. The method was tested experimentally for detection of calcium fragments within gel phantoms and calcium-like inhomogeneities within chicken tissue at 7 T with optimized MR acquisition parameters. The method was also evaluated for detection of simulated microcalcifications, modeled from biopsy samples of malignant breast cancer, inserted in silico into breast magnetic resonance imaging (MRIs) of healthy subjects at 7 T. For both assessments of calcium fragments in phantoms and biopsy-based simulated microcalcifications in breast MRIs, receiver operator characteristic curve analyses were performed to determine the cross-correlation index cutoff, for achieving optimal sensitivity and specificity, and the area under the curve (AUC), for measuring the method's performance. The method detected calcium fragments with sizes of 0.14-0.79 mm, 1 mm calcium-like deposits, and simulated microcalcifications with sizes of 0.4-1.0 mm in images with voxel sizes between (0.2 mm)(3) and (0.6 mm)(3). In images acquired at 7 T with voxel sizes of (0.2 mm)(3)-(0.4 mm)(3), calcium fragments (size 0.3-0.4 mm) were detected with a sensitivity, specificity, and AUC of 78%-90%, 51%-68%, and 0.77%-0.88%, respectively. In images acquired with a human 7 T scanner, acquisition times below 12 min, and voxel sizes of (0.4 mm)(3)-(0.6 mm)(3), simulated microcalcifications with sizes of 0.6-1.0 mm were detected with a sensitivity, specificity, and AUC of 75%-87%, 54%-87%, and 0.76%-0.90%, respectively. However, different microcalcification shapes were indistinguishable. The new method is promising for detecting relatively large microcalcifications (i.e., 0.6-0.9 mm) within the breast at 7 T in reasonable times. Detection of smaller deposits at high field may be possible with higher spatial resolution, but such images require relatively long scan times. Although mammography can detect and distinguish the shape of smaller microcalcifications with superior sensitivity and specificity, this alternative method does not expose tissue to ionizing radiation, is not affected by breast density, and can be combined with other MRI methods (e.g., dynamic contrast-enhanced MRI and diffusion weighted MRI), to potentially improve diagnostic performance.