A New Validation Method for X-ray Mammogram Registration Algorithms Using a Projection Model of Breast X-ray Compression

Abstract

Abstract-Establishing spatial correspondence between features visible in X-ray mammograms obtained at different times has great potential to aid assessment and quantitation of change in the breast indicative of malignancy. The literature contains numerous nonrigid registration algorithms developed for this purpose, but existing approaches are flawed by the assumption of inappropriate 2-D transformation models and quantitative estimation of registration accuracy is limited. In this paper, we describe a novel validation method which simulates plausible mammographic compressions of the breast using a magnetic resonance imaging (MRI) derived finite element model. By projecting the resulting known 3-D displacements into 2-D and generating pseudo-mammograms from these same compressed magnetic resonance (MR) volumes, we can generate convincing images with known 2-D displacements with which to validate a registration algorithm. We illustrate this approach by computing the accuracy for two conventional nonrigid 2-D registration algorithms applied to mammographic test images generated from three patient MR datasets. We show that the accuracy of these algorithms is close to the best achievable using a 2-D one-to-one correspondence model but that new algorithms incorporating more representative transformation models are required to achieve sufficiently accurate registrations for this application.

Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers

“…A model of even greater complexity and realism was recently proposed by Ma et al (2009), which incorporates the lactiferous duct system, Cooper ligaments, pectoral muscle, fibro-glandular tissues, as well as abnormalities such as microcalcifications, irregular tumours and spiculated tumours. Also noteworthy is the study by Hipwell et al (2007), where breast MRI volumes were used as phantoms to test X-ray mammogram registration algorithms. The MR intensities were converted into linear attenuation coefficients using an approximate histogram-based mapping rule.…”

“…Unfortunately, MR scans image magnetic properties of the tissue, rather than X-ray attenuation values as obtained by CT scans. Importantly, there are no exact, one-to-one relationships between the intensities seen in MR and CT scans (Hipwell et al, 2007). Hence it is debatable whether the tissue features seen in breast MR are entirely representative of those seen in breast CT, and whether breast MR volumes can be reliably converted into X-ray attenuation phantoms.…”

Computational simulation of breast compression based on segmented breast and fibroglandular tissues on magnetic resonance images

“…Other commonly used software packages include Amira® software for generating 3D breast surfaces and ANSYS® software for generating breast volume meshes (Hipwell et al 2007). The simulation of soft tissue deformation using the finite element method can be performed using ABAQUS® and ANSYS® (Tanner et al 2006, Hipwell et al 2007, del Palomar et al 2008, Misra et al 2009).…”

Large breast compressions: Observations and evaluation of simulations

“…We simulated a population of mammograms from MR images for a second cohort 13 and compared results for the ten cases for which we also had digitized mammograms. The images were acquired for the UK MR breast screening study ͑MARIBS͒.…”