MRI of the breast is gaining widespread clinical acceptance, being used to screen high-risk women and to further investigate questionable ultra-sound or mammography findings. The observation of the local pattern of enhancement in dynamic contrast enhanced (DCE) imaging is the pillar of the MRI breast exams, yielding close to perfect lesion detection sensitivity. Less than ideal specificity, however, is exhibited by the current protocols;depending on the report, up to 4 out of 5 positive MRI exams may result in negative biopsies and distraught patients. One of the methods recently proposed for increasing breast MRI specificity and reducing the number of false positives and negative biopsies is diffusion weighted imaging (DWI), an approach which probes the mobility of water molecules on a scale up to a few microns. Emerging studies, mainly performed at 1.5T, point to the potential of DWI to reduce up to 33% of the biopsies warranted by positive DCE results, without missing any cancers. As 3T scanners become the clinical standard, the extent of the improvement in specificity of breast cancer diagnosis that can be imparted to a DCE protocol by DWI acquisitions is yet to be determined. A number of problems associated with imaging the breast anatomy at high fields, such as image shading, artifacts due to B0 inhomogeneities, improper fat suppression and low channel count breast RF receive coils available commercially, have left the 3T DWI spatial resolution in the 10-40ml range, similar to 1.5T, and significantly below the 1ml resolution typical of 3T DCE MRI protocols. The research we propose represents a multi-faceted, comprehensive approach toward understanding the ultimate potential of 3T DWI to improve the specificity of breast cancer detection. More specifically, we propose to develop technology resulting in the acquisition of reduced artifact, high spatial resolution, and diffusion weighted images in less than 5 minutes. A number of approaches for insuring homogeneous signal excitation, high sensitivity signal reception, and mitigating the impact of main field inhomogeneity on diffusion weighted image quality will be studied. The hardware and software developed will be loaned to our clinical partner, Beth Israel Deaconess Medical Center, where the performance of DWI measurements alone and as an adjuvant to DCE MRI in diagnosing breast cancer will be assessed in a clinical study.
The research proposed represents a multi-faceted, comprehensive approach towards understanding the ultimate potential of diffusion weighted (DW) MRI to improve breast cancer detection. Improvements to hardware and acquisition approaches that impact DW image quality will be performed. The increase in MRI breast cancer detection specificity due to these improvements will be quantified in a small clinical study.
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