Abstract: The invention describes a method for automatically localizing organ segments in a three-dimensional image comprising the following steps: providing a three-dimensional image showing at least one organ and at least one tubular network comprising a plurality of tubular structures, the organ comprising organ segments; performing automatic separation of the organ from other parts of the image; performing automatic tracing of the tubular network to obtain a branch map; performing automatic analysis of the branch map to identify specific tubular structures; performing automatically assigning regions of the organ to the specific tubular structures to segment the organ into localized organ segments; and outputting the localized organ segments and the traced and analyzed tubular network as image data. The invention further describes a localization arrangement and a medical imaging system.

Abstract: Systems and methods for automatically staging non-small cell lung cancer are provided. Patient data relating to a cancer of a patient is received. The patient data comprises one or more medical images and one or more biopsy reports. A T-stage of the cancer is determined based on a location and a size of one or more tumors of the cancer determined using the patient data. An N-stage of the cancer is determined by combining a metastasis evaluation of the cancer to regional lymph nodes determined from the one or more medical images and a metastasis evaluation of the cancer in the regional lymph nodes determined from the one or more biopsy reports. An M-stage of the cancer is determined based on a metastasis evaluation of the cancer to anatomical structures based on the patient data. The T-stage, the N-stage, and the M-stage are output.

Abstract: A framework for multi-scan image processing. A single real anatomic image of a region of interest is first acquired. One or more emission images of the region of interest are also acquired. One or more synthetic anatomic images may be generated based on the one or more emission images. One or more deformable registrations of the real anatomic image to the one or more synthetic anatomic images are performed to generate one or more registered anatomic images. Attenuation correction may then be performed on the one or more emission images using the one or more registered anatomic images to generate one or more attenuation corrected emission images.

Abstract: Systems and methods of PET attenuation correction using low-field MR image data includes receiving a first set of image data and a set of low-field magnetic resonance (MR) image data. An attenuation correction map is generated from the low-field MR image data using a first trained neural network. At least one attenuation correction process is applied to the first set of image data based on the attenuation correction map to generate at least one clinical attenuation-corrected image.

Abstract: A framework for multi-scan image processing. A single real anatomic image of a region of interest is first acquired. One or more emission images of the region of interest are also acquired. One or more synthetic anatomic images may be generated based on the one or more emission images. One or more deformable registrations of the real anatomic image to the one or more synthetic anatomic images are performed to generate one or more registered anatomic images. Attenuation correction may then be performed on the one or more emission images using the one or more registered anatomic images to generate one or more attenuation corrected emission images.

Abstract: The invention describes a method for automatically localizing organ segments in a three-dimensional image comprising the following steps: providing a three-dimensional image showing at least one organ and at least one tubular network comprising a plurality of tubular structures, the organ comprising organ segments; performing automatic separation of the organ from other parts of the image; performing automatic tracing of the tubular network to obtain a branch map; performing automatic analysis of the branch map to identify specific tubular structures; performing automatically assigning regions of the organ to the specific tubular structures to segment the organ into localized organ segments; and outputting the localized organ segments and the traced and analyzed tubular network as image data. The invention further describes a localization arrangement and a medical imaging system.

Abstract: A method for parametric image reconstruction and motion correction using whole-body motion fields includes receiving a nuclear imaging data set including a set of dynamic frames and generating at least one of a whole-body forward motion field and/or a whole-body inverse motion field for at least one frame in the set of frames. An iterative loop is applied to update at least one parameter used in a direct parametric reconstruction and at least one parametric image is generated based on the at least one parameter updated by the iterative loop. The iterative loop includes calculating a frame emission image for the at least one frame, generating a motion-corrected frame emission image based on the at least one whole-body forward motion field or a whole-body inverse motion field, and updating at least one parameter by applying a fit to the motion-corrected frame emission image.

Abstract: A method for parametric image reconstruction and motion correction using whole-body motion fields includes receiving a nuclear imaging data set including a set of dynamic frames and generating at least one of a whole-body forward motion field and/or a whole-body inverse motion field for at least one frame in the set of frames. An iterative loop is applied to update at least one parameter used in a direct parametric reconstruction and at least one parametric image is generated based on the at least one parameter updated by the iterative loop. The iterative loop includes calculating a frame emission image for the at least one frame, generating a motion-corrected frame emission image based on the at least one whole-body forward motion field or a whole-body inverse motion field, and updating at least one parameter by applying a fit to the motion-corrected frame emission image.

Abstract: Myocardial tissue tracking techniques are used to project or guide a single manually-defined set of myocardial contours through time. Displacement encoding with stimulated echoes (DENSE), harmonic phase (HARP) and speckle tracking is used to encode tissue displacement into the phase of complex MRI images, providing a time series of these images, and facilitating the non-invasive study of myocardial kinematics. Epicardial and endocardial contours need to be defined at each frame on cine DENSE images for the quantification of regional displacement and strain as a function of time. The disclosed method presents a novel and effective two dimensional semi-automated segmentation technique that uses the encoded motion to project a manually defined region of interest through time. Contours can then easily be extracted for each cardiac phase.