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Michael Anthony Jacobs, Ph.D.

Photo of Dr. Michael Anthony Jacobs, Ph.D.
  • Director, Imaging Radiological Assessment Team (IRAT), Sidney Kimmel Comprehensive Cancer Center
  • Professor of Radiology and Radiological Science

Expertise

Brain Cancer, Brain Metastases, Breast Imaging, Locally Advanced Breast Cancer, Magnetic Resonance Imaging, Metastatic Bone Disease, Metastatic Disease, Muscular Dystrophies, Neurofibromatosis, New Imaging Modalities, Non-invasive Imaging, Oncologic Imaging, Prostate Disease, Radiology, Sarcoidosis, Stroke Imaging, Tumor Imaging ...read more

Research Interests

Image analysis MRI/MRSI; Breast cancer imaging; New analysis tools for MRI and other imaging; Novel multiparametric MRI and PET/CT assessment; High Intensity Focused Ultrasound; Prostate Imaging ...read more

Background

Dr. Michael A. Jacobs is a Professor in the Johns Hopkins Medicine Department of Radiology and Radiological Science. His research focuses on developing radiological methods for detecting, monitoring, and treating breast, brain, prostate and other cancers, with a particular focus on transferring research from the lab to the clinic.  He is Board Certified in Diagnostic Medical Physics by the American Board of Radiology.

Dr. Jacobs received his undergraduate degrees in mathematics and engineering science from the University of Michigan-Flint. He earned his Ph.D. in biomedical physics from Oakland University. He completed his fellowship in radiology at the Johns Hopkins University School of Medicine. Dr. Jacobs joined the Johns Hopkins faculty in 2003.

He serves on the National Board of the American College of Radiology Imaging Network (ACRIN) Radiomics committee. He is the Director of the Imaging Radiological Assessment Team (IRAT) of the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins. He is also one of the principal investigators on the Quantitative Imaging Network (QIN) U01 at Johns Hopkins University for developing novel methods for determining treatment response in clinical trials.

His work has been recognized with numerous awards and honors, including the 2012 Distinguished Investigator Awards from the Academy of Radiology Research, three Certificate of Merit Awards at the Radiological Society of North America, a bronze medal from the American Roentgen Ray Society and a National Research Service Awards Cancer Research Training Grant. He also was awarded multiple patents for his work related to radiological imaging.

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Titles

  • Director, Imaging Radiological Assessment Team (IRAT), Sidney Kimmel Comprehensive Cancer Center
  • Professor of Radiology and Radiological Science

Departments / Divisions

Centers & Institutes

Education

Degrees

  • Ph.D., Oakland University (Michigan) (1999)
  • B.S., University of Michigan (Michigan) (1993)

Additional Training

  • Diagnostic Medical Physics, American Board of Radiology (2016)
Departments of Neurology and Radiology, Henry Ford Hospital, Detroit, MI, 2000, Stroke Research Coordinator; Depts. of Neurology and Radiology. Henry Ford Hospital, Detroit, MI, 1999, Senior Research Assistant; Mathematics Dept. Oakland University, Detroit, MI, 1995, Graduate Research Assistant; United States Air Force: Non Commissioned Officer (NCO), Wilford Hall Medical Center (Level 1 Trauma Center), San Antonio, TX, 1992, Asst. NCOIC Supervisor of Emergency Room Medical Technicians; The Johns Hopkins University School of Medicine, Baltimore, MD, 2002, Radiological Fellow

Research & Publications

Research Summary

Dr. Jacobs is involved in breast, brain, prostate, and metastatic cancer imaging research. His research examines new analysis tools, such as, radiomics with machine and Deep Learning for magnetic resonance imaging (MRI) and PET/CT.  He is particularly focused on multiparametric MRI/PET/CT assessment and image analysis of radiological imaging. He holds a number of patents related to using and manipulating radiological images.

Technology Expertise Keywords

Diffusion; ADC; computer science; cancer; PET; CT; Radiomics

Clinical Trial Keywords

PET/CT, Whole Body Imaging, Dynamic Contrast Imaging, Diffusion Imaging, Magnetic Resonance Imaging, Radiomics, Deep Learning, COVID-19, Computer Science, Medical Physics

Clinical Trials

Pre-Operative Radiation and Veliparib for Breast Cancer

Pulsed Radiofrequency vs. Steroid Injections for Occipital Neuralgia

Selected Publications

View all on Pubmed

Parekh VS, Jacobs MA, Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI. npj Breast Cancer (2017) 3:43 ; doi:10.1038/s41523-017-0045-3

Leung DG, Carrino JA, Wagner KR, Jacobs MA. Whole-body MRI evaluation of facioscapulohumeral muscular dystrophy. Muscle and Nerve. 2015; epub: Jan 16

Parekh V, Jacobs MA. Radiomics: a new application from established techniques. Expert Review of Precision Medicine and Drug Development. 2016;1(2):207-226

Parekh VS, Macura, KJ, Harvey S, Kamel I, EI-Khouli R, Bluemke DA, Jacobs MA. Multiparametric Deep Learning Tissue Signatures for a Radiological Biomarker of Breast Cancer: Preliminary Results. Medical Physics, 2020; 47(1):75-88. doi: 10.1002/mp.13849

Jacobs MA, Umbricht C, Parekh VS, EI-Khouli R, Cope L, Macura KJ, Harvey S, Wolff AC. Advanced machine learning informatics modeling and multiparametric radiomics for characterizing breast tumor characteristics with the OncotypeDX gene array. Cancers. 2020, 12(10), 2772; https://doi.org/10.3390/cancers12102772

Patents

Advanced Treatment Response Prediction Using Clinical Parameters and Advanced Unsupervised Machine Learning: The Contribution Scattergram
Patent # US Patent 10,388,017 B2 | 08/20/2019

This patent provides methods for a method for detection of different ontologies using advanced unsupervised machine learning which will be used to visualize factors not visible to the human observer such as unknown characteristics between imaging datasets and other factors to provide insights into the structure of the data. We have termed this the Contribution Scattergram, For example, using radiological images, we can determine the relationship in dimension, structure, and “distance” between each parameter using advanced graph theoretic measures. This information can be used to determine if the changes within the images have occurred and these changes can be used for treatment response in different types od diseases. 

Multiparametric Non-linear dimension reduction methods for segmentation and classification of radiological images.
Patent # US Patent 9,256,966 | 02/09/2016

Visualization of anatomical structures is an important tool in medicine to differentiate normal tissue from pathological tissue using radiological imaging methods, which can generate large amounts of data for a radiologist to read.  Integrating these large data sets is difficult and time-consuming.  A new approach has used both supervised and unsupervised advanced machine learning techniques for visualizing and segmenting radiological data.  This study presents the application of a novel machine learning method based upon Non-Linear Dimensionality Reduction (NLDR) methods using an objective (unsupervised) computer algorithm implementing modified versions of Isomap, Local Linear Embedding (LEE), and Diffusion Maps.  We test the utility of these models using magnetic resonance imaging (MRI) data in patients.

Methods and systems for registration of radiological images
Patent # US Patent 9,008,462 | 04/14/2015

We developed a novel 3D registration method for application to radiological images. Typically, global motion seen in imaging is modeled by an affine transformation (rigid), while local motion and slice matching are estimated using some type of non-uniform resampling of the pixel intensities of the target image volume along a chosen reference plane. The reference plane is defined as the plane that the reference image volume was scanned, for example, axial, coronal, or sagittal.  The target plane can be any of anatomical planes obliterating the need for the reference and target scan planes to be the same.  Our method of registration is performed by searching for a reslicing angles and reslicing planes that maximizes similarity between the reference image volume and the resliced target image volume using novel multi-resolution wavelet transform. This wavelet transform provides estimated reslicing angles could lie between 0 and 360 degrees. By angular reslicing of the target image volume, the number of slices of the reference and target volumes will be the same and it deforms the target volume to morph it with the reference image volume.  Such a reslicing-based nonrigid registration of the radiological images according to this aspect of the present invention is a useful tool to match slices location and thickness, and 3D registration of the image or object slices.

Compression device for enhancing normal/abnormal tissue contrast in MRI including devices and methods related thereto
Patent # US patent 8,380,286 | 02/19/2013

Compression device for enhancing normal/abnormal tissue contrast in MRI including devices and methods related thereto.
Patent # US patent 8,380,281 | 02/19/2013

Contact for Research Inquiries

712 Rutland Ave.
Traylor Building, Room 309
Baltimore, MD 21205 map

Activities & Honors

Honors

  • Distinguished Investigator Award, Academy of Radiology Research, 2012
  • Bronze medal winner at the American Roentgen Ray Society, Washington DC, 2008
  • Two Certificates of Merit Awards, Radiological Society of North America, 2010
  • Certificate of Merit Award, Radiological Society of North America, 2009
  • National Research Service Award Cancer Research Training Grant, 2002
  • First Place Award in Clinical Research, JHMI Oncology Center Research Day, 2000

Memberships

  • American Association of Physicists in Medicine
  • American College of Radiology Imaging Network (ACRIN), By Invitation Only
  • Imaging Response Assessment Team (IRAT) Network

Professional Activities

  • American Association of Physicists in Medicine
  • American College of Radiology Imaging Network (ACRIN)
  • Imaging Response Assessment Team (IRAT) Network
  • Chair DWI MRI Imaging. NCI – Quantitative Cancer Imaging, NCI, 2014
  • Chair/Co-chair DCE MRI Imaging. NCI – Quantitative Cancer Imaging March 5-6, NCI, 2011
  • Co-Director, Department of Radiology and Oncology, 2005
  • Co-Director, JHU ICMIC Imaging, Department of Radiology, 2007
  • Co-PI, JHU Quantitative Cancer Imaging Program, 2011
  • Director, IRAT committee, Sidney Kimmel Comprehensive Cancer Center, 2014
  • Reviewer, California Breast Cancer Research Board UCLA, 2004
  • Reviewer, Susan B. Komen Foundation for breast cancer, Dallas, 2004
  • Reviewer, NIH/NCI, 2005
  • Reviewer, University of Maryland, 2005
  • Reviewer, R01-NIH/NCI, 2011
  • Reviewer, DOD for Prostate, 2012
  • Reviewer, UK Wellcome Burroughs, UK, 2006
  • Reviewer, Novitis European Grant Foundation, England, 2010
  • Reviewer, French National Evaluation Committee for Cancer translational research, 2009
  • Reviewer, King Abdullah International Medical Research Center (KAIMRC), Saudi Arabia
  • Reviewer, United States-Israel Binational Science Foundation, Israel, 2018
  • Reviewer, Swiss National Science Foundation (SNSF), Sweden, 2019

Videos & Media

Recent News Articles and Media Coverage

Road to RSNA preview of the RSNA 2015 conference in Chicago, - presentation listed in the Imaging Informatics Preview. One of the innovative informatics talks at the RSNA, Aunt Minnie (November 2, 2015)

Can whole-body MRI be used in oncologic staging? Aunt Minnie (October 23, 2018)

Johns Hopkins Researchers Compare CT Scans of Lung Tissue? New Mexico Decedent Image Database (NMDID) Aids in COVID-19 Research (April 16, 2020)

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