MRI-guided breast biopsies can be lengthy procedures with false negative rates that are unacceptably high¹. The time to (re-)locate the lesion can unnecessarily lengthen the procedure. Various factors may affect lesion identification². For example, the pre-biopsy and biopsy MRIs are often acquired in different orientations, with different fields of view and spatial resolutions. More importantly, breast compression (needed in the biopsy exams) may distort the anatomy and can limit perfusion, leading to reduced or inexistent lesion enhancement in the biopsy series. Numerous post-contrast scans, image reformatting, image subtraction and maximum intensity projection map generation may be needed to localize lesions, thus lengthening the procedure and increasing patient discomfort. In this proof-of-concept study, we investigate whether accurate alignment of the pre-biopsy and biopsy images would enable one to project the lesion identified in the pre-biopsy exam to the biopsy series, thus simplifying lesion identification during biopsy. We introduce a registration methodology to align the pre-biopsy exam (showing both uncompressed breasts) with the biopsy exam (depicting only one compressed breast), and quantify its performance in a small in vivo study.Our study includes pre-biopsy and biopsy MRIs from four subjects: 1 normal control (no contrast) and 3 biopsy proven breast cancer subjects. The 1/3 sets of images were acquired on a 3T/1.5T (MR750/MR450) GE MR scanners, respectively. Our approach for aiding lesions re-localization is based on the automated registration of the pre-biopsy MRI to the reference biopsy MRI; its three steps are summarized in Figure 1 and detailed below:
- First, the MRI intensities for the pre-biopsy and biopsy MRIs are standardized, to remove the irregular enhancement caused by the contrast agent or other heterogeneous illumination artifacts³.
- Next, an iterative grid-search is performed to best align the two sets of images based on their mutual information. In this search, five parameters (the X/Z scaling factors and three translations) are optimized using an exhaustive exploration of the parameter space. In the first iteration, a coarse grid is chosen to identify the spatial translation of the (dual-breast axial) pre-biopsy images relative to the (single breast, sagittal) biopsy data set. The following iterations are executed with finer grids and seek to refine the five parameters.
- Finally, a multi-resolution free form deformation⁴ is performed to elastically register the pre-biopsy and biopsy scans. The grid search and deformable registration are performed using the ITK-based package elastix⁵. The registration accuracy was estimated by measuring the root mean squared deviation (RMSD) of anatomic landmarks, e.g. breast edge, blood vessels etc. For each study, 3-4 anatomic landmarks were manually identified, for a total of 14 landmarks for the four cases. The overlay between the actual and predicted lesion location was quantified using Dice similarity coefficients.The three-step process resulted in very good alignment of the pre-biopsy and biopsy scans in all subjects. Figures 2a-b shows an exemplary initial alignment after the iterative multi-grid registration (step 2); Figures 2c-d demonstrates the final, refined result, after deformable registration (step 3). Note the exquisite alignment between the two image sets, in which one’s features extend seamlessly into the second (see arrows). Figure 3 shows an example of the overlap between the actual lesion from the biopsy MRI (red) and of the predicted lesion location, obtained through the registration of the pre-biopsy and biopsy data sets (blue). This result suggests that a process similar to the one employed here may render the administration of the contrast agent in the biopsy exam unnecessary. Our procedure works very well in large deformation conditions; following scaling and translations, compressions/expansions on the X/Z axis were as large as 35%/30%. The RSMD of the 14 landmarks (summarized in Figure 4 as a function of registration step) was 12.3 mm after the iterative grid search, further improved by the multi-resolution deformable registration to a 3.5mm. The average Dice coefficient between the predicted/actual lesion locations was 59%, on account on small lesion size and difficulty in accurately outlining lesion boundaries. We developed an approach for aligning the pre-biopsy and biopsy breast MR images, enabling the easy identification of breast lesions in biopsy exams. Our final landmark RMSD (3.5mm) approaches the slice spacing in our biopsy exams (3mm). It is likely that higher resolution biopsy images will further increase the achievable precision. Our predicted lesion location (situated within a few pixels of the actual enhancing lesion) will enable clinicians to quickly and accurately locate the lesions in the biopsy exams, thus shortening procedure time, reducing cost, and minimizing patient discomfort.