Multimodal Imaging of Cancer

TOPICAL COLLECTIONS

Guest Editor

Anna Yaroslavsky

University of Massachusetts at Lowell, Department of Physics and Applied Physics, Massachusetts General Hospital, Department of Dermatology

Anna Yaroslavsky graduated summa cum laude from Saratov State University, Russia, in 1990 with BS in Physics. Between 1990 and 1999, she conducted research at Twente University in Enschede (The Netherlands), Heinrich-Heine University in Dusseldorf (Germany), and Louisiana State University Medical Center in Shreveport, LA (USA). During that time, she worked on the development of individualized, image-based methods of laser dosimetry and planning for brain tumor treatment, formulated concept and implemented full inverse Monte Carlo technique for reconstruction of tissue optical properties, and investigated light scattering by complex biological structures. In 1999, she earned PhD degree in Biophysics from Saratov State University. In 2000, she joined Wellman Center for Photomedicine at Harvard Medical School, where she progressed from Instructor to Assistant Professor of Dermatology. Her research at Wellman Center included the development of combined polarization, fluorescence, and elastic scattering imaging methods for diagnostics of cancers. In 2010, Anna accepted position of Associate Professor of Physics at the University of Massachusetts. Currently, her research focuses on the development and integration of experimental and theoretical multi-modal technologies and methods for functional and structural characterization of biological tissues and cells, as well as pathology detection and treatment. She published extensively in top-tier international journals and authored several patents. She serves as a reviewer on various NIH and NSF panels and has been elected fellow member of SPIE.

Collection Overview

With technological advances, a broad range of multimodal devices and sophisticated image acquisition techniques have become available to clinicians and researchers. As growing amount of data are acquired, a plethora of new methods for the image analysis and their fusion are being developed with the premise that additional modalities incorporate complimentary information that will yield accurate and definitive cancer detection at the early stages, help predict its aggressiveness and invasiveness, and enable effective image-guided treatment strategies. Consequently, in recent years multimodal imaging has become an important part of fundamental, translational, and clinical cancer research. In the clinical setting, multimodal approach to image-guided detection and therapy facilitates diagnosis, characterization, and delineation of cancers, as it benefits from examining multiple attributes of tissues and cells simultaneously and on different scales. In the laboratory setting, it contributes significantly to improving our understanding of tumor microenvironment, its development, and metastatic progression.

This collection will include the following topics

Methods and techniques in multimodal image acquisition and analysis,
Radiomics methods based on multimodal imaging of cancer,
Registration and fusion methods for multimodal cancer images,
Image segmentation techniques based on multimodal images of cancer,
Cross-modality synthetic image generation,
Multimodal cancer classification and prediction methods using supervised or unsupervised methods,
Clinical and pre-clinical studies using multimodal imaging.