Abstract: Described herein are systems and methods for synthetic CT image creation that allow MR-only radiotherapy of cancer patients, e.g., head and neck (H&N) cancer patients, prostate cancer patients, patients with cancer of the pelvis, abdomen cancer patients, patients with cancer of the extremities, brain cancer patients, or thorax cancer patients. The methods and systems described herein feature image processing techniques that improve the similarity between CT and MR images prior to CT-MR image registration, as well as standardization of the MR intensity histograms prior to MR-MR registration. Application of the techniques result in more accurate assignment of the Hounsfield unit to each point in the synthetic CT compared to other atlas-based methods, providing for more accurate dosing in MR-only radiotherapy simulation and planning.

Abstract: Described herein are systems and methods for synthetic CT image creation that allow MR-only radiotherapy of cancer patients, e.g., head and neck (H&N) cancer patients, prostate cancer patients, patients with cancer of the pelvis, abdomen cancer patients, patients with cancer of the extremities, brain cancer patients, or thorax cancer patients. The methods and systems described herein feature image processing techniques that improve the similarity between CT and MR images prior to CT-MR image registration, as well as standardization of the MR intensity histograms prior to MR-MR registration. Application of the techniques result in more accurate assignment of the Hounsfield unit to each point in the synthetic CT compared to other atlas-based methods, providing for more accurate dosing in MR-only radiotherapy simulation and planning.

Abstract: Physiological imaging-defined subvolumes of tissues/disease are identified to yield spatially-defined prognostic and/or predictive indicators and/or focal therapy targets within such tissues, in particular tumors, for evaluation over time, for example, prior to and after a therapy treatment. Medical image data is analyzed to delineate subvolumes of tissue based upon multiple physiological, metabolic, and biological imaging properties, where those subvolumes are extracted and analyzed in a probabilistic manner to associate with one or more abnormal or disease phenotype conditions.

Abstract: Physiological imaging-defined subvolumes of tissues/disease are identified to yield spatially-defined prognostic and/or predictive indicators and/or focal therapy targets within such tissues, in particular tumors, for evaluation over time, for example, prior to and after a therapy treatment. Medical image data is analyzed to delineate subvolumes of tissue based upon multiple physiological, metabolic, and biological imaging properties, where those subvolumes are extracted and analyzed in a probabilistic manner to associate with one or more abnormal or disease phenotype conditions.