M
Michael A. Jacobs
Researcher at Johns Hopkins University School of Medicine
Publications - 179
Citations - 9087
Michael A. Jacobs is an academic researcher from Johns Hopkins University School of Medicine. The author has contributed to research in topics: Magnetic resonance imaging & Cancer. The author has an hindex of 54, co-authored 168 publications receiving 8106 citations. Previous affiliations of Michael A. Jacobs include University of California & Ford Motor Company.
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Journal ArticleDOI
Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint
TL;DR: The preliminary results suggest that incorporation of the incompressibility regularization term improves intensity-based free-form nonrigid registration of contrast-enhanced MR breast images by greatly reducing the problem of shrinkage of Contrast-enhancing structures while simultaneously allowing motion artifacts to be substantially reduced.
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Re-examining the brain regions crucial for orchestrating speech articulation.
Argye E. Hillis,Melissa Work,Peter B. Barker,Michael A. Jacobs,Elisabeth L. Breese,Kristin Maurer +5 more
TL;DR: In patients with and without insular lesions, apraxia of speech was associated with structural damage or low blood flow in left posterior inferior frontal gyrus, and this results illustrate a potential limitation of lesion overlap studies, and illustrate an alternative method for identifying brain-behaviour relationships.
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Diffusion-weighted imaging improves the diagnostic accuracy of conventional 3.0-T breast MR imaging
Riham H. Ei Khouli,Michael A. Jacobs,Sarah D. Mezban,Peng Huang,Ihab R. Kamel,Katarzyna J. Macura,David A. Bluemke,David A. Bluemke +7 more
TL;DR: DW imaging with glandular tissue-normalized ADC assessment improves the characterization of breast lesions beyond the characterization achieved with conventional 3D T1-weighted and dynamic contrast-enhanced MR imaging at 3.0 T.
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Principles and Applications of Diffusion-weighted Imaging in Cancer Detection, Staging, and Treatment Follow-up
Ashkan A. Malayeri,Riham H. El Khouli,Atif Zaheer,Michael A. Jacobs,Celia P. Corona-Villalobos,Ihab R. Kamel,Katarzyna J. Macura +6 more
TL;DR: Diffusion-weighted imaging has many applications in oncologic imaging and can aid in tumor detection and characterization and in the prediction and assessment of response to therapy.
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Radiomics: a new application from established techniques
TL;DR: Radiomics is defined as the high throughput extraction of quantitative imaging features or texture from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction and can be further used to develop computational models using advanced machine learning algorithms that may serve as a tool for personalized diagnosis and treatment guidance.