Abstract: Systems and methods are described for segmenting medical images, such as magnetic resonance images, using a deep learning model that has been trained using random dropped inputs, standardized inputs, or both. Medical images can be segmented based on anatomy, physiology, pathology, other properties or characteristics represented in the medical images, or combinations thereof. As one example, multi-contrast magnetic resonance images are input to the trained deep learning model in order to generate multiple segmented medical images, each representing a different segmentation class.

Abstract: Systems and methods are described for segmenting medical images, such as magnetic resonance images, using a deep learning model that has been trained using random dropped inputs, standardized inputs, or both. Medical images can be segmented based on anatomy, physiology, pathology, other properties or characteristics represented in the medical images, or combinations thereof. As one example, multi-contrast magnetic resonance images are input to the trained deep learning model in order to generate multiple segmented medical images, each representing a different segmentation class.

Abstract: Systems and methods are described for segmenting medical images, such as magnetic resonance images, using a deep learning model that has been trained using random dropped inputs, standardized inputs, or both. Medical images can be segmented based on anatomy, physiology, pathology, other properties or characteristics represented in the medical images, or combinations thereof. As one example, multi-contrast magnetic resonance images are input to the trained deep learning model in order to generate multiple segmented medical images, each representing a different segmentation class.

Abstract: A system and method for acquiring MR imaging data from a subject includes administering positively-charged nitroxides or gadolinium chelates for in vivo mitochondrial labeling, acquiring MR imaging data from the subject, and reconstructing an image of the subject having enhanced contrast in areas including metabolic and/or mitotic activity.

Abstract: A system and method for acquiring MR imaging data from a subject includes administering positively-charged nitroxides or gadolinium chelates for in vivo mitochondrial labeling, acquiring MR imaging data from the subject, and reconstructing an image of the subject having enhanced contrast in areas including metabolic and/or mitotic activity.

Abstract: A system and method for acquiring MR imaging data from a subject includes administering positively-charged nitroxides or gadolinium chelates for in vivo mitochondrial labeling, acquiring MR imaging data from the subject, and reconstructing an image of the subject having enhanced contrast in areas including metabolic and/or mitotic activity.