Abstract: A system for planning and performing a repeat interventional procedure is provided which includes a registration device and an image generation device which map current targets in a reference image for a first interventional procedure to at least one guidance image acquired from a different imaging modality. Biopsy locations are recorded to the guidance images during the first interventional procedure and the biopsy locations are mapped to the reference image to provide a planning image for use in a subsequent interventional procedure on the patient. In a subsequent interventional procedure, the prior planning image (124) may be registered to a current reference image and the prior biopsy locations and prior and current targets are mapped to a guidance image acquired from a different imaging modality. Biopsy locations are then mapped to the guidance image and mapped back to the current reference image.

Abstract: One embodiment provides an apparatus for fluorescence lifetime imaging (FLI). The apparatus includes a deep neural network (DNN). The DNN includes a first convolutional layer, a plurality of intermediate layers and an output layer. The first convolutional layer is configured to receive FLI input data. Each intermediate layer is configured to receive a respective intermediate input corresponding to an output of a respective prior layer. Each intermediate layer is further configured to provide a respective intermediate output related to the received respective intermediate input. The output layer is configured to provide estimated FLI output data corresponding to the received FLI input data. The DNN is trained using synthetic data.

Abstract: The present disclosure describes ultrasound systems and methods configured to determine the elasticity of a target tissue. Systems can include an ultrasound transducer configured to acquire echoes responsive to ultrasound pulses transmitted toward the tissue, which may include a region of increased stiffness. Systems can also include a beamformer configured to control the transducer to transmit a push pulse into the tissue, thereby generating a shear wave in the region of increased stiffness. The beamformer can be configured to control the transducer to emit tracking pulses adjacent to the push pulse. Systems can further include a processor configured to determine a displacement amplitude of the shear wave and based on the amplitude, generate a qualitative tissue elasticity map of the tissue. The processor can combine the qualitative map with a quantitative map of the same tissue, and based on the combination, determine a boundary of the region of increased stiffness.

Abstract: A method of multi-modal image registration is provided. The method includes receiving as input a fixed image from a first imaging device, receiving as input a moving image from a second imaging device, performing feature extraction on the fixed image via a first feature extractor to generate a fixed image feature map, performing feature extraction on the moving image via second feature extractor to generate a moving image feature map, performing cross-modal attention on the fixed image feature map and the moving image feature map to generate cross-modal feature attention data, performing deep registration on the cross-modal feature attention data via a deep registrator, and outputting a multi-modal registered image.

Abstract: An interventional instrument (16) is inserted into a patient (14) to perform an interventional medical procedure guided by a medical imaging device (10). A trigger control (26) is activated. An electronic data processing device (30) is programmed to operate the medical imaging device to: acquire and display video (50) of the interventional instrument (16) inserted into the patient; detect activation of the trigger control; in response to the detecting, process a video segment (54) of the acquired video to identify a trigger image (60) as a frame of the video segment capturing a medical intervention event performed by the interventional instrument and a location (62) of the medical intervention event in the identified trigger image; and display a still image of the identified trigger image with a superimposed marker (102) indicating the identified location of the medical intervention event.

Abstract: In one embodiment, there is provided an apparatus for denoising a medical image. The apparatus includes a denoising artificial neural network (ANN) configured to denoise input image data. The denoising ANN is trained, based at least in part, on at least one loss function. The at least one loss function includes a task-oriented loss.

Abstract: One embodiment provides an apparatus for registering a two dimensional (2D) ultrasound (US) frame and a three dimensional (3D) magnetic resonance (MR) volume. The apparatus includes a first deep neural network (DNN) and an image fusion management circuitry. The first DNN is configured to determine a 2D US pose vector based, at least in part, on 2D US frame data. The image fusion management circuitry is configured to register the 2D US frame data and a 3D MR volume data.

Abstract: In one embodiment, there is provided a method of optimizing image quality and organ dose for computed tomography (CT). The method includes segmenting, by an organ segmentation module, at least one organ based, at least in part, on patient image data. The method further includes determining, by a Monte Carlo dose module, a patient-specific heterogeneous dose based, at least in part, on the patient image data and based, at least in part, on a selected CT scanner data. The method further includes determining, by a patient-specific organ dose module, a patient-specific nominal organ dose for each segmented organ based, at least in part, on the patient-specific heterogeneous dose. The method further includes determining, by an inverse optimization module, at least one CT scanner parameter configured to optimize image quality and a selected patient-specific organ dose of at least one selected organ.

Abstract: A system and method for planning and performing an interventional procedure based on the spatial relationships between identified points. The system includes a storage device (102) having an image (104) which includes a plurality of targets (107). A spatial determination device (114) is configured to determine distances and/or orientation between each of the targets. The system is configured to compare the distances and generate a warning signal if at least one of the distances is less than a minimum threshold (128). An image generation device (116) is configured to generate a graphical representation for display to the user which shows the spatial information between a selected target with respect to the other targets. A planning device (126) is configured to modify or consolidate targets automatically or based on a user's input in order to more effectively plan or execute an interventional procedure.

Abstract: A method of ultrasound image volume reconstruction includes: providing a convolutional neural network (“CNN”); receiving a first dataset comprising at least one pair of consecutive ultrasound images; inputting the first dataset to the CNN; training the CNN with the first dataset; receiving a second dataset comprising an ultrasound video comprising a plurality of consecutive ultrasound images; inputting the second dataset to the CNN; and processing, by the CNN, the second dataset to produce as output a reconstructed 3D ultrasound image volume.

Abstract: A controller for qualifying image registration includes a memory that stores instructions; and a processor that executes the instructions. When executed by the processor, the instructions cause the controller to execute a process that includes receiving first imagery of a first modality and receiving second imagery of a second modality. The process executed by the controller also includes registering the first imagery of the first modality to the second imagery of the second modality to obtain an image registration. The image registration is subjected to an automated analysis as a qualifying process to qualify the image registration. The image registration is variably qualified when the image registration passes the qualifying process, and is not qualified when the image registration does not pass the qualifying process.

Abstract: A system for boundary identification includes a memory (42) to store shear wave displacements through a medium as a displacement field including a spatial component and a temporal component. A directional filter (206, 208) filters the displacement field to provide a directional displacement field. A signal processing device (26) is coupled to the memory to execute a boundary estimator (214) to estimate a tissue boundary in a displayed image based upon a history of the directional displacement field accumulated over time.

Abstract: A system for planning and performing a repeat interventional procedure is provided which includes a registration device and an image generation device which map current targets in a reference image for a first interventional procedure to at least one guidance image acquired from a different imaging modality. Biopsy locations are recorded to the guidance images during the first interventional procedure and the biopsy locations are mapped to the reference image to provide a planning image for use in a subsequent interventional procedure on the patient. In a subsequent interventional procedure, the prior planning image (124) may be registered to a current reference image and the prior biopsy locations and prior and current targets are mapped to a guidance image acquired from a different imaging modality. Biopsy locations are then mapped to the guidance image and mapped back to the current reference image.

Abstract: One embodiment provides an apparatus for fluorescence lifetime imaging (FLI). The apparatus includes a deep neural network (DNN). The DNN includes a first convolutional layer, a plurality of intermediate layers and an output layer. The first convolutional layer is configured to receive FLI input data. Each intermediate layer is configured to receive a respective intermediate input corresponding to an output of a respective prior layer. Each intermediate layer is further configured to provide a respective intermediate output related to the received respective intermediate input. The output layer is configured to provide estimated FLI output data corresponding to the received FLI input data. The DNN is trained using synthetic data.

Abstract: A system for planning and performing a repeat interventional procedure includes a registration device (116) and an image generation device (120) which map current targets in a reference image (104) for a first interventional procedure to at least one guidance image (112) acquired from a different imaging modality. Biopsy locations are recorded to the guidance images during the first interventional procedure and the biopsy locations are mapped to the reference image to provide a planning image for use in a subsequent interventional procedure on the patient. In a subsequent interventional procedure, the prior planning image (124) may be registered to a current reference image (126) and the prior biopsy locations and prior and current targets are mapped to a guidance image acquired from a different imaging modality. Biopsy locations are then mapped to the guidance image (115) and mapped back to the current reference image.

Abstract: The present disclosure describes ultrasound systems and methods configured to determine the elasticity of a target tissue. Systems can include an ultrasound transducer configured to acquire echoes responsive to ultrasound pulses transmitted toward the tissue, which may include a region of increased stiffness. Systems can also include a beamformer configured to control the transducer to transmit a push pulse into the tissue, thereby generating a shear wave in the region of increased stiffness. The beamformer can be configured to control the transducer to emit tracking pulses adjacent to the push pulse. Systems can further include a processor configured to determine a displacement amplitude of the shear wave and based on the amplitude, generate a qualitative tissue elasticity map of the tissue. The processor can combine the qualitative map with a quantitative map of the same tissue, and based on the combination, determine a boundary of the region of increased stiffness.

Abstract: A controller for qualifying image registration includes a memory that stores instructions; and a processor that executes the instructions. When executed by the processor, the instructions cause the controller to execute a process that includes receiving first imagery of a first modality and receiving second imagery of a second modality. The process executed by the controller also includes registering the first imagery of the first modality to the second imagery of the second modality to obtain an image registration. The image registration is subjected to an automated analysis as a qualifying process to qualify the image registration. The image registration is variably qualified when the image registration passes the qualifying process, and is not qualified when the image registration does not pass the qualifying process.

Abstract: A system for planning and performing a repeat interventional procedure is provided which includes a registration device (116) and an image generation device (120) which map current targets in a reference image (104) for a first interventional procedure to at least one guidance image (112) acquired from a different imaging modality. Biopsy locations are recorded to the guidance images during the first interventional procedure and the biopsy locations are mapped to the reference image to provide a planning image for use in a subsequent interventional procedure on the patient. In a subsequent interventional procedure, the prior planning image (124) may be registered to a current reference image (126) and the prior biopsy locations and prior and current targets are mapped to a guidance image acquired from a different imaging modality. Biopsy locations are then mapped to the guidance image (115) and mapped back to the current reference image.

Abstract: The following relates generally to image segmentation. In one aspect, an image is received and preprocessed. The image may then be classified as segmentable if it is ready for segmentation; if not, it may be classified as not segmentable. Multiple, parallel segmentation processes may be performed on the image. The result of each segmentation process may be marked as a potential success (PS) or a potential failure (PF). The results of the individual segmentation processes may be evaluated in stages. An overall failure may be declared if a percentage of the segmentation processes marked as PF reaches a predetermined threshold.

Abstract: The following relates generally to image segmentation. In one aspect, an image is received and preprocessed. The image may then be classified as segmentable if it is ready for segmentation; if not, it may be classified as not segmentable. Multiple, parallel segmentation processes may be performed on the image. The result of each segmentation process may be marked as a potential success (PS) or a potential failure (PF). The results of the individual segmentation processes may be evaluated in stages. An overall failure may be declared if a percentage of the segmentation processes marked as PF reaches a predetermined threshold.