MRI to X-ray mammography intensity-based registration with simultaneous optimisation of pose and biomechanical transformation parameters

Determining corresponding regions between an MRI and an X-ray mammogram is a clinically useful task that is challenging for radiologists due to the large deformation that the breast undergoes between the two image acquisitions. In this work we propose an intensity-based image registration framework, where the biomechanical transformation model parameters and the rigid-body transformation parameters are optimised simultaneously. Patient-specific biomechanical modelling of the breast derived from diagnostic, prone MRI has been previously used for this task. However, the high computational time associated with breast compression simulation using commercial packages, did not allow the optimisation of both pose and FEM parameters in the same framework. We use a fast explicit Finite Element (FE) solver that runs on a graphics card, enabling the FEM-based transformation model to be fully integrated into the optimisation scheme. The transformation model has seven degrees of freedom, which include parameters for both the initial rigid-body pose of the breast prior to mammographic compression, and those of the biomechanical model. The framework was tested on ten clinical cases and the results were compared against an affine transformation model, previously proposed for the same task. The mean registration error was 11.6±3.8mm for the CC and 11±5.4mm for the MLO view registrations, indicating that this could be a useful clinical tool.

[1]  Reyer Zwiggelaar,et al.  2D-3D correspondence in mammography , 2002 .

[2]  Janet Waters,et al.  MRI for breast cancer screening, diagnosis, and treatment , 2011, The Lancet.

[3]  Torsten Hopp,et al.  Automatic multimodal 2D/3D breast image registration using biomechanical FEM models and intensity-based optimization , 2013, Medical Image Anal..

[4]  A. Gefen,et al.  Mechanics of the normal woman's breast. , 2007, Technology and health care : official journal of the European Society for Engineering and Medicine.

[5]  A. Karellas,et al.  Breast cancer imaging: a perspective for the next decade. , 2008, Medical physics.

[6]  Janet Waters,et al.  Breast Cancer 1 MRI for breast cancer screening, diagnosis, and treatment , 2011 .

[7]  W. Kaiser,et al.  Model-based registration of X-ray mammograms and MR images of the female breast , 2006, IEEE Transactions on Nuclear Science.

[8]  A R Padhani,et al.  Screening with magnetic resonance imaging and mammography of a UK population at high familial risk of breast cancer: a prospective multicentre cohort study (MARIBS) , 2005, The Lancet.

[9]  D. Hawkes,et al.  Large breast compressions: observations and evaluation of simulations. , 2011, Medical physics.

[10]  Ingrid Schreer,et al.  Interdisciplinary consensus on the uses and technique of MR-guided vacuum-assisted breast biopsy (VAB): results of a European consensus meeting. , 2009, European journal of radiology.

[11]  David Atkinson,et al.  On modelling of anisotropic viscoelasticity for soft tissue simulation: Numerical solution and GPU execution , 2009, Medical Image Anal..

[12]  Shandong Wu,et al.  Atlas-Based Probabilistic Fibroglandular Tissue Segmentation in Breast MRI , 2012, MICCAI.

[13]  Nico Karssemeijer,et al.  Intensity-Based MRI to X-ray Mammography Registration with an Integrated Fast Biomechanical Transformation , 2012, Digital Mammography / IWDM.

[14]  Regina J. Hooley,et al.  Breast Cancer Screening and Problem Solving Using Mammography, Ultrasound, and Magnetic Resonance Imaging , 2011, Ultrasound quarterly.

[15]  C. Boetes,et al.  Breast MRI: guidelines from the European Society of Breast Imaging , 2008, European Radiology.

[16]  David Gavaghan,et al.  Predicting Tumour Location by Simulating Large Deformations of the Breast Using a 3D Finite Element Model and Nonlinear Elasticity , 2004, MICCAI.

[17]  Lianghao Han,et al.  Development of patient-specific biomechanical models for predicting large breast deformation , 2012, Physics in medicine and biology.

[18]  Nico Karssemeijer,et al.  MRI to X-ray mammography registration using a volume-preserving affine transformation , 2012, Medical Image Anal..

[19]  Martyn P. Nash,et al.  Modelling Mammographic Compression of the Breast , 2008, MICCAI.

[20]  Torsten Hopp,et al.  2D/3D image fusion of X-ray mammograms with breast MRI: visualizing dynamic contrast enhancement in mammograms , 2012, International Journal of Computer Assisted Radiology and Surgery.

[21]  Michael Brady,et al.  Fusion of contrast-enhanced breast MR and mammographic imaging data , 2003, Medical Image Anal..

[22]  Martin J. Yaffe,et al.  Biomechanical 3-D finite element modeling of the human breast using MRI data , 2001, IEEE Transactions on Medical Imaging.

[23]  Susan M. Astley,et al.  Quantifying Breast Thickness for Density Measurement , 2008, Digital Mammography / IWDM.

[24]  J. de Bresser,et al.  Breast MRI in clinically and mammographically occult breast cancer presenting with an axillary metastasis: a systematic review. , 2010, European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology.

[25]  W. Paul Segars,et al.  An analysis of the mechanical parameters used for finite element compression of a high-resolution 3D breast phantom. , 2011, Medical physics.

[26]  R. D. Wood,et al.  Finite element analysis of air supported membrane structures , 2000 .

[27]  Martyn P. Nash,et al.  Breast lesion co-localisation between X-ray and MR images using finite element modelling , 2013, Medical Image Anal..