Neural Machine Registration for Motion Correction in Breast DCE-MRI

Cancer is one of the leading causes of death in the western world, with medical imaging playing a key role for early diagnosis. Focusing on breast cancer, one of the emerging imaging methodologies is Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI). The flip side of using DCE-MRI is in its long acquisition times that often causes the patient to move. This results in motion artefacts, namely distortions in the acquired image that can affect DCE-MRI analysis. A possible solution consists in the use of Motion Correction Techniques (MCTs), i.e. procedures intended to re-align the post-contrast image to the corresponding pre-contrast (reference) one. This task is particularly critic in DCE-MRI, due to brightness variations introduced in post-contrast images by the contrast-agent flowing. To face this problem, in this work we introduce a new MCT for breast DCE-MRI leveraging Physiologically Based PharmacoKinetic (PBPK) modelling and Artificial Neural Networks (ANN) to determine the most suitable physiologically-compliant transformation. To this aim, we propose a Neural Registration Network relying on a very task-specific loss function explicitly designed to take into account the contrast agent flowing while enforcing a correct re-alignment. We compared the obtained results against some conventional motion correction techniques, evaluating the performance on a patient-by-patient basis. Results show that the proposed approach results to be the best performing even when compared against other techniques designed to take into account for brightness variations.

[1]  Mario Sansone,et al.  Dynamic contrast-enhanced MRI in breast cancer: A comparison between distributed and compartmental tracer kinetic models , 2012 .

[2]  Glen R Morrell,et al.  Pharmacokinetic mapping for lesion classification in dynamic breast MRI , 2010, Journal of magnetic resonance imaging : JMRI.

[3]  Stefano Marrone,et al.  Evaluating Impacts of Motion Correction on Deep Learning Approaches for Breast DCE-MRI Segmentation and Classification , 2019, CAIP.

[4]  Stefano Marrone,et al.  3TP-CNN: Radiomics and Deep Learning for Lesions Classification in DCE-MRI , 2019, ICIAP.

[5]  H. Kauczor,et al.  Dynamic contrast-enhanced magnetic resonance imaging: fundamentals and application to the evaluation of the peripheral perfusion. , 2014, Cardiovascular diagnosis and therapy.

[6]  Stefano Marrone,et al.  DCE-MRI Breast Lesions Segmentation with a 3TP U-Net Deep Convolutional Neural Network , 2019, 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS).

[7]  G. Faguet,et al.  A brief history of cancer: Age‐old milestones underlying our current knowledge database , 2015, International journal of cancer.

[8]  B J McNeil,et al.  Advances in biomedical imaging. , 2001, JAMA.

[9]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[10]  M. Knopp,et al.  Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbols , 1999, Journal of magnetic resonance imaging : JMRI.

[11]  Georgios D. Evangelidis,et al.  Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[13]  P. Gøtzsche,et al.  Screening for breast cancer with mammography. , 2013, The Cochrane database of systematic reviews.

[14]  Lawrence A. Ray,et al.  2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications , 2005, J. Electronic Imaging.

[15]  A. Miller.  Screening for breast cancer with mammography , 2001, The Lancet.

[16]  Stefano Marrone,et al.  Automatic Lesion Detection in Breast DCE-MRI , 2013, ICIAP.

[17]  C. Gatsonis,et al.  MRI evaluation of the contralateral breast in women with recently diagnosed breast cancer. , 2007, The New England journal of medicine.

[18]  Stefano Marrone,et al.  Data-driven selection of motion correction techniques in breast DCE-MRI , 2015, 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings.

[19]  C. Nodine,et al.  Using eye movements to study visual search and to improve tumor detection. , 1987, Radiographics : a review publication of the Radiological Society of North America, Inc.

[20]  Nicholas Ayache,et al.  The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration , 1998, MICCAI.

[21]  Stefano Marrone,et al.  A Novel Model-Based Measure for Quality Evaluation of Image Registration Techniques in DCE-MRI , 2014, 2014 IEEE 27th International Symposium on Computer-Based Medical Systems.

[22]  Stefano Marrone,et al.  Comprehensive computer-aided diagnosis for breast T1-weighted DCE-MRI through quantitative dynamical features and spatio-temporal local binary patterns , 2018, IET Comput. Vis..

[23]  Stefano Marrone,et al.  Breast segmentation using Fuzzy C-Means and anatomical priors in DCE-MRI , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[24]  Andrew Zisserman,et al.  Spatial Transformer Networks , 2015, NIPS.

[25]  O Henriksen,et al.  In vivo quantification of the unidirectional influx constant for Gd‐DTPA diffusion across the myocardial capillaries with MR imaging , 1994, Journal of magnetic resonance imaging : JMRI.

[26]  P. Tofts.  Modeling tracer kinetics in dynamic Gd‐DTPA MR imaging , 1997, Journal of magnetic resonance imaging : JMRI.