| dc.description.abstract | Medical imaging is critical to non-invasive diagnosis and treatment of a wide spectrum
of medical conditions. However, different modalities of medical imaging employ/apply
di erent contrast mechanisms and, consequently, provide different depictions of bodily
anatomy. As a result, there is a frequent problem where the same pathology can be
detected by one type of medical imaging while being missed by others. This problem brings
forward the importance of the development of image processing tools for integrating the
information provided by different imaging modalities via the process of information fusion.
One particularly important example of clinical application of such tools is in the diagnostic
management of breast cancer, which is a prevailing cause of cancer-related mortality in
women. Currently, the diagnosis of breast cancer relies mainly on X-ray mammography and
Magnetic Resonance Imaging (MRI), which are both important throughout different stages
of detection, localization, and treatment of the disease. The sensitivity of mammography,
however, is known to be limited in the case of relatively dense breasts, while contrast enhanced
MRI tends to yield frequent 'false alarms' due to its high sensitivity. Given this
situation, it is critical to find reliable ways of fusing the mammography and MRI scans in
order to improve the sensitivity of the former while boosting the specificity of the latter.
Unfortunately, fusing the above types of medical images is known to be a difficult computational
problem. Indeed, while MRI scans are usually volumetric (i.e., 3-D), digital
mammograms are always planar (2-D). Moreover, mammograms are invariably acquired
under the force of compression paddles, thus making the breast anatomy undergo sizeable
deformations. In the case of MRI, on the other hand, the breast is rarely constrained and
imaged in a pendulous state. Finally, X-ray mammography and MRI exploit two completely
di erent physical mechanisms, which produce distinct diagnostic contrasts which
are related in a non-trivial way. Under such conditions, the success of information fusion
depends on one's ability to establish spatial correspondences between mammograms
and their related MRI volumes in a cross-modal cross-dimensional (CMCD) setting in the
presence of spatial deformations (+SD). Solving the problem of information fusion in the
CMCD+SD setting is a very challenging analytical/computational problem, still in need
of efficient solutions.
In the literature, there is a lack of a generic and consistent solution to the problem of
fusing mammograms and breast MRIs and using their complementary information. Most
of the existing MRI to mammogram registration techniques are based on a biomechanical
approach which builds a speci c model for each patient to simulate the effect of mammographic
compression. The biomechanical model is not optimal as it ignores the common
characteristics of breast deformation across different cases. Breast deformation is essentially the planarization of a 3-D volume between two paddles, which is common in all
patients. Regardless of the size, shape, or internal con guration of the breast tissue, one
can predict the major part of the deformation only by considering the geometry of the
breast tissue. In contrast with complex standard methods relying on patient-speci c biomechanical
modeling, we developed a new and relatively simple approach to estimate the
deformation and nd the correspondences. We consider the total deformation to consist of
two components: a large-magnitude global deformation due to mammographic compression
and a residual deformation of relatively smaller amplitude. We propose a much simpler
way of predicting the global deformation which compares favorably to FEM in terms of
its accuracy. The residual deformation, on the other hand, is recovered in a variational
framework using an elastic transformation model.
The proposed algorithm provides us with a computational pipeline that takes breast
MRIs and mammograms as inputs and returns the spatial transformation which establishes
the correspondences between them. This spatial transformation can be applied in different
applications, e.g., producing 'MRI-enhanced' mammograms (which is capable of improving
the quality of surgical care) and correlating between different types of mammograms.
We investigate the performance of our proposed pipeline on the application of enhancing
mammograms by means of MRIs and we have shown improvements over the state of the
art. | en |
