Abstract: A flexible digital radiographic detector assembly includes a flexible sleeve enclosing a photosensor array supported by a flexible substrate. Integrated circuit readout electronics are coupled to the photosensor array. The digital radiographic detector assembly communicates with a local computer system or with a cloud server to transmit image data captured in the photosensor array, to display the image data on a screen, and to analyze defects that may be present in the image.

Abstract: A framework for personalized recommendation. An image content profile for a current case is generated. One or more auxiliary information representations associated with the current case are further generated. Affinity scores for radiology service providers are then determined by applying service profiles of the radiology service providers, the image content profile and the one or more auxiliary information representations to a trained recommendation engine. The current case is then assigned to one of the radiology service providers based on the affinity scores.

Abstract: A framework diagnostic test planning is described herein. In accordance with one aspect, the framework receives data representing one or more sample patients, diagnostic tests administered to the one or more sample patients, diagnostic test results and confirmed medical conditions associated with the administered diagnostic tests. The framework trains one or more classifiers based on the data to identify diagnostic test plans from the diagnostic tests. The one or more classifiers may then be applied to current patient data to generate a diagnostic test plan for a given patient.

Abstract: A framework for patient management based on anatomic measurements is described herein. In accordance with one aspect, patient records are clustered into a set of sub-populations based on first anatomic measurements and characteristics extracted from first patient data associated with a population of patients. A representative sub-population similar to a patient may be determined from the set of sub-populations based on the patient data of the patient. A report that presents the second anatomic measurements associated with the patient in relation to corresponding first anatomic measurements associated with the representative sub-population may then be generated.

Abstract: A framework for patient management based on anatomic measurements is described herein. In accordance with one aspect, patient records are clustered into a set of sub-populations based on first anatomic measurements and characteristics extracted from first patient data associated with a population of patients. A representative sub-population similar to a patient may be determined from the set of sub-populations based on the patient data of the patient. A report that presents the second anatomic measurements associated with the patient in relation to corresponding first anatomic measurements associated with the representative sub-population may then be generated.

Abstract: A framework for selective image reconstruction. In accordance with one aspect, the framework receives at least one first image that is reconstructed based on at least one first reconstruction attribute. At least one region of interest may then be identified in the at least one first image. The framework may selectively reconstruct at least one second image of the region of interest based on at least one second reconstruction attribute, wherein the second reconstruction attribute is different from the first reconstruction attribute. Results may then be generated based on the at least one second image.

Abstract: A framework for automated measurement. In accordance with one aspect, the framework detects a centerline point of a structure of interest in an image. A centerline of the structure of interest may be traced based on the detected centerline point. A trained segmentation learning structure may be used to generate one or more contours of the structure of interest along the centerline. One or more measurements may then be extracted from the one or more contours.

Abstract: A framework for selective image reconstruction. In accordance with one aspect, the framework receives at least one first image that is reconstructed based on at least one first reconstruction attribute. At least one region of interest may then be identified in the at least one first image. The framework may selectively reconstruct at least one second image of the region of interest based on at least one second reconstruction attribute, wherein the second reconstruction attribute is different from the first reconstruction attribute. Results may then be generated based on the at least one second image.

Abstract: A framework for automated measurement. In accordance with one aspect, the framework detects a centerline point of a structure of interest in an image. A centerline of the structure of interest may be traced based on the detected centerline point. A trained segmentation learning structure may be used to generate one or more contours of the structure of interest along the centerline. One or more measurements may then be extracted from the one or more contours.

Abstract: A framework for supporting therapy planning is described herein. In accordance with one aspect, patient-specific characteristics are extracted from medical data associated with a given patient. The framework may then search a database for one or more other patients associated with personal characteristics that are similar to the patient-specific characteristics. Information associated with the one or more other patients may be presented to support therapy planning or diagnosis.

Abstract: A framework diagnostic test planning is described herein. In accordance with one aspect, the framework receives data representing one or more sample patients, diagnostic tests administered to the one or more sample patients, diagnostic test results and confirmed medical conditions associated with the administered diagnostic tests. The framework trains one or more classifiers based on the data to identify diagnostic test plans from the diagnostic tests. The one or more classifiers may then be applied to current patient data to generate a diagnostic test plan for a given patient.

Abstract: A framework for patient management based on anatomic measurements is described herein. In accordance with one aspect, patient records are clustered into a set of sub-populations based on first anatomic measurements and characteristics extracted from first patient data associated with a population of patients. A representative sub-population similar to a patient may be determined from the set of sub-populations based on the patient data of the patient. A report that presents the second anatomic measurements associated with the patient in relation to corresponding first anatomic measurements associated with the representative sub-population may then be generated.

Abstract: Described herein is a technology for enhanced visualization of medical image data. In one implementation, a region of interest is identified in a first set of images along at least one viewing direction. Based on the first set of images, a second set of images is reconstructed to include at least one selectively enhanced rendering of the region of interest. The selectively enhanced rendering may include a three-dimensional rendering or any other type of enhanced rendering to facilitate detection of abnormalities.

Abstract: Systems and methods for supporting a diagnostic workflow from a computer system are disclosed herein. In accordance with one implementation, a set of pre-identified anatomical landmarks associated with one or more structures of interest within one or more medical images are presented to a user. In response to a user input selecting at least one or more regions of interest including one or more of the pre-identified anatomical landmarks, the user is automatically navigated to the selected region of interest. In another implementation, a second user input selecting one or more measurement tools is received. An evaluation may be automatically determined based on one or more of the set of anatomical landmarks in response to the second user input.

Abstract: Locations, such as computer assisted detection marks, are presented in medical imaging diagnosis, such as for colon computed tomography diagnosis. To avoid fly-through and/or to assist in more rapid and thorough review of CAD marks, a summary of a plurality of detected candidates is pre-computed and presented to the user in a single collection. For example, a single display or screen view includes a gallery of images for different candidates. These pre-computed images are displayed on the screen, allowing the user to quickly identify locations of interest and rule out other locations. The summary may be used for navigation outside the CAD context, such as presenting different portions of an organ to be viewed.

Abstract: Locations, such as computer assisted detection marks, are presented in medical imaging diagnosis, such as for colon computed tomography diagnosis. To avoid fly-through and/or to assist in more rapid and thorough review of CAD marks, a summary of a plurality of detected candidates is pre-computed and presented to the user in a single collection. For example, a single display or screen view includes a gallery of images for different candidates. These pre-computed images are displayed on the screen, allowing the user to quickly identify locations of interest and rule out other locations. The summary may be used for navigation outside the CAD context, such as presenting different portions of an organ to be viewed.

Abstract: Systems and methods for supporting a diagnostic workflow from a computer system are disclosed herein. In accordance with one implementation, a set of pre-identified anatomical landmarks associated with one or more structures of interest within one or more medical images are presented to a user. In response to a user input selecting at least one or more regions of interest including one or more of the pre-identified anatomical landmarks, the user is automatically navigated to the selected region of interest. In another implementation, a second user input selecting one or more measurement tools is received. An evaluation may be automatically determined based on one or more of the set of anatomical landmarks in response to the second user input.

Abstract: A method of detecting malacia in airways includes providing a plurality of 3-dimensional (3D) digital lung images acquired over an inhalation/exhalation cycle from a same subject, each said image comprising a set of intensities on a 3-dimensional grid of points, registering a successive pair of images, wherein a registration mapping of the point grid of one image is calculated, locating airways in each of said pair of images, and collecting those points between the airways in each of said pair of images, wherein a volume of said collected points is a measure of an extent of malacia in said airways.

Abstract: A method and system for polyp segmentation in computed tomography colonogrphy (CTC) volumes is disclosed. The polyp segmentation method utilizes a three-staged probabilistic binary classification approach for automatically segmenting polyp voxels from surrounding tissue in CTC volumes. Based on an input initial polyp position, a polyp tip is detected in a CTC volume using a trained 3D point detector. A local polar coordinate system is then fit to the colon surface in the CTC volume with the origin at the detected polyp tip. Polyp interior voxels and polyp exterior voxels are detected along each axis of the local polar coordinate system using a trained 3D box. A boundary voxel is detected on each axis of the local polar coordinate system based on the detected polyp interior voxels and polyp exterior voxels by boosted 1D curve parsing using a trained classifier. This results in a segmented polyp boundary.

Abstract: A method and system for providing a user interface for polyp annotation, segmentation, and measurement in computer tomography colonography (CTC) volumes is disclosed. The interface receives an initial polyp position in a CTC volume, and automatically segments the polyp based on the initial polyp position. In order to segment the polyp, a polyp tip is detected in the CTC volume using a trained 3D point detector. A local polar coordinate system is then fit to the colon surface in the CTC volume with the origin at the detected polyp tip. Polyp interior voxels and polyp exterior voxels are detected along each axis of the local polar coordinate system using a trained 3D box. A boundary voxel is detected on each axis of the local polar coordinate system based on the detected polyp interior voxels and polyp exterior voxels by boosted 1D curve parsing using a trained classifier. This results in a segmented polyp boundary.