Author
Martin O. Leach
Other affiliations: Engineering and Physical Sciences Research Council, University of Minnesota, Lincoln's Inn ...read more
Bio: Martin O. Leach is an academic researcher from The Royal Marsden NHS Foundation Trust. The author has contributed to research in topics: Magnetic resonance imaging & Breast cancer. The author has an hindex of 78, co-authored 512 publications receiving 30002 citations. Previous affiliations of Martin O. Leach include Engineering and Physical Sciences Research Council & University of Minnesota.
Papers published on a yearly basis
Papers
More filters
TL;DR: The results clearly indicate that the proposed nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.
Abstract: In this paper the authors present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, the authors have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.
5,490 citations
American Cancer Society1, University of Washington2, University of Arkansas for Medical Sciences3, The Royal Marsden NHS Foundation Trust4, University of Washington Medical Center5, Memorial Sloan Kettering Cancer Center6, University of North Carolina at Chapel Hill7, University of Pennsylvania8, Northwestern University9, University of Toronto10, Sunnybrook Health Sciences Centre11, University of Southern California12
TL;DR: There are several risk subgroups for which the available data are insufficient to recommend for or against screening, including women with a personal history of breast cancer, carcinoma in situ, atypical hyperplasia, and extremely dense breasts on mammography.
Abstract: New evidence on breast Magnetic Resonance Imaging (MRI) screening has become available since the American Cancer Society (ACS) last issued guidelines for the early detection of breast cancer in 2003. A guideline panel has reviewed this evidence and developed new recommendations for women at different defined levels of risk. Screening MRI is recommended for women with an approximately 20-25% or greater lifetime risk of breast cancer, including women with a strong family history of breast or ovarian cancer and women who were treated for Hodgkin disease. There are several risk subgroups for which the available data are insufficient to recommend for or against screening, including women with a personal history of breast cancer, carcinoma in situ, atypical hyperplasia, and extremely dense breasts on mammography. Diagnostic uses of MRI were not considered to be within the scope of this review.
2,332 citations
TL;DR: In this paper, the authors compared contrast enhanced magnetic resonance imaging (CE MRI) with mammography for screening of women genetically predisposed to breast cancer and found that CE MRI is more sensitive than mammography.
1,023 citations
University of Manchester1, Imperial College London2, Central Manchester University Hospitals NHS Foundation Trust3, Harvard University4, Ford Motor Company5, King's College London6, University Medical Center Groningen7, University of Cambridge8, University of Oxford9, The Royal Marsden NHS Foundation Trust10, University of Leeds11, University of Michigan12, European Organisation for Research and Treatment of Cancer13, Institute of Cancer Research14, University College London15, United States Military Academy16, VU University Amsterdam17, University of Wisconsin-Madison18, Maastricht University19, Institut Gustave Roussy20, Robarts Research Institute21, Memorial Sloan Kettering Cancer Center22, Newcastle University23, University of Leicester24, Mount Vernon Hospital25, Johns Hopkins University26, Hofstra University27, University of Birmingham28, University of Antwerp29, Duke University30, Brighton and Sussex Medical School31, University of Sheffield32, University of Texas at Austin33
TL;DR: Experts assembled to review, debate and summarize the challenges of IB validation and qualification produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical validation, biological/clinical validation and assessment of cost-effectiveness.
Abstract: Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.
758 citations
University of California, San Francisco1, University of Cambridge2, Vanderbilt University3, École Polytechnique Fédérale de Lausanne4, Sunnybrook Health Sciences Centre5, University of Texas Southwestern Medical Center6, University of York7, Institute of Cancer Research8, Center for Devices and Radiological Health9, University of Pennsylvania10, Duke University11
TL;DR: The known abnormalities in cancer metabolism, the value and limitations of current imaging methods for metabolism, and the principles of hyperpolarization are summarized.
661 citations
Cited by
More filters
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
8,730 citations
TL;DR: A review of recent as well as classic image registration methods to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas.
6,842 citations
TL;DR: TBSS aims to improve the sensitivity, objectivity and interpretability of analysis of multi-subject diffusion imaging studies by solving the question of how to align FA images from multiple subjects in a way that allows for valid conclusions to be drawn from the subsequent voxelwise analysis.
5,959 citations
TL;DR: The results clearly indicate that the proposed nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.
Abstract: In this paper the authors present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, the authors have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.
5,490 citations