Disclosed is a medical image data processing method for determining a clinical target volume for a medical treatment, wherein the method comprises executing, on at least one processor (3) of at least one computer (2), steps of: a) acquiring (S1) first image data describing at least one image of an anatomical structure of a patient; b) acquiring (S2) second image data describing an indicator for a preferred spreading direction or probability distribution of at least one target cell; c) determining (S3) registration data describing a registration of the first image data to the second image data by performing a co-registration between the first image data and the second image data using a registration algorithm; d) determining (S4) gross target region data describing a target region in the at least one image of the anatomical structure based on the first image data; e) determining (S5) margin region data describing a margin around the target region based on the gross target region data; f) determining (S6) clinical target volume data describing a volume in the anatomical structure for the medical treatment based on the registration data, the gross target region data and the margin region data.

Devices, systems, methods for validating ablation results in a patient's brain are provided. In some embodiments, the method for validating ablation result in a patient's brain includes obtaining magnetic resonance (MR) data of the patient's brain, by use of a magnetic resonance imaging (MRI) device; obtaining first imaging data of the patient's brain, by use of the MRI device; extracting, by use of computing device in communication with the MRI device, first fiber tracts passing through an anatomy in the patient's brain based on the first imaging data; obtaining, by use of the MRI device, second imaging data of the patient's brain after ablation of the anatomy in the patient's brain has started; extracting second fiber tracts passing through the anatomy in the patient's brain based on the second imaging data; and outputting a graphical representation of a comparison between the first fiber tracts and the second fiber tracts.

A method of analyzing a multidimensional image tensor containing a plurality of images comprises: performing imaging scans of a subject imaging data; generating the multidimensional image tensor from the imaging data; determining a spatial basis tensor containing basis images based on the multidimensional image tensor; determining a temporal basis tensor containing basis functions for a temporal dimension based on the multidimensional image tensor; determining a core tensor that relates the spatial basis tensor to the temporal basis tensor; pre-multiplying the core tensor and the temporal basis tensor to produce a modified temporal basis tensor; storing the spatial basis tensor and the modified temporal basis tensor; and generating an image by multiplying at least (i) at least a portion of the spatial basis tensor and (ii) at least a portion of the modified temporal basis tensor.

A magnetic resonance diffusion tensor imaging method and corresponding device. The method includes acquiring omnidirectionally sampled diffusion weighted images of a plurality of training samples; performing diffusion tensor model fitting and undersampling for the omnidirectionally sampled diffusion weighted images of each training sample to obtain an omnidirectionally sampled diffusion tensor image and an undersampled diffusion weighted image; training a deep learning network, with the omnidirectionally sampled diffusion tensor images of the plurality of training samples as training targets and the undersampled diffusion weighted images as training data; acquiring undersampled diffusion weighted images of a target object; and inputting the undersampled diffusion weighted images of target objects into the trained deep learning network to obtain the predicted omnidirectionally sampled diffusion tensor images of the target objects. Also, a fiber tracking method and corresponding device.

A method for registering multiple data types of diverse modalities for a target volume includes acquiring at least at least two datasets associated with the target volume where the at least two datasets having different modalities. Using information field theory and entropy spectrum pathways theory, a local connectivity matrix is constructed for one or both of spatial connectivity and temporal connectivity for each of the datasets. The local connectivity matrices for the datasets are fused into a common coupling matrix and the datasets are merged to generate a registered image displaying the spatial and temporal features within the target volume.

New and innovative systems and methods for registration degradation correction for surgical procedures are disclosed. An example system includes a surgical marking pen including a first trackable target and a registration plate including a second trackable target. The system also includes a navigation camera and a processor configured to perform a pen registration that determines a transformation between a tip of the surgical marking pen and the first trackable target when the tip of the surgical marking pen is placed on the registration plate. The pen registration enables the processor to record virtual marks at locations of the pen tip that correspond to physical marks drawn by the pen. Locations of the virtual marks are later compared to images of the physical marks to correct any registration degradation by moving a surgical camera or robotic arm connected to the surgical camera.

A pre-event connectome of a subject brain is accessed, the pre-event connectome defining i) first functional nodes in the subject brain and ii) first edges that represent connections between the first functional nodes before the subject has undergone an event. A post-event connectome of the subject brain is accessed, the post-event connectome defining i) second functional nodes in the subject brain and ii) second edges that represent connections between the second functional nodes after the subject has undergone the event. A connectome-difference map data is generated that records the difference between the pre-event connectome and the post-event connectome. An action is taken based on the connectome-difference map data.

The present disclosure provides a method and system for processing multi-modality images. The method may include obtaining multi-modality images; registering the multi-modality images; fusing the multi-modality images; generating a reconstructed image based on a fusion result of the multi-modality images; and determining a removal range with respect to a focus based on the reconstructed image. The multi-modality images may include at least three modalities. The multi-modality images may include a focus.

A method of generating a quantitative characterization of injury presence and status of spinal cord tissue using an adaptive CNN system for use in diagnostic assessment, surgical planning, and therapeutic strategy comprises preprocessing for artifact correction of diffusion based, spinal cord MRI data, training an adaptive CNN system with healthy and abnormal (injured/pathologic) spinal cord images obtained by imaging a population of healthy, typically developed spinal cord subjects and subjects with spinal cord injury, evaluating a novel, diffusion-based MRI image for injury biomarkers using the adaptive CNN system, generating a three-dimensional predictive axonal damage map for quantitative characterization and visualization of the novel, diffusion-based MRI image, and transmitting the sets of healthy and injured spinal cord images back to a central database for continued improvement of the adaptive CNN system training. A system for defining a predictive spinal axonal damage map is also described.

Aspects of the disclosure provide a method for denoising an image. The method can include receiving an acquired image from an image acquisition system, and processing the acquired image with a nonlinear diffusion coefficient based filter having a diffusion coefficient that is calculated using gradient vector orientation information in the acquired image.