An approach for improving determining a significant slice associated with a tumor from a volume of medical images is disclosed. The approach is based on the annotation of tumor range and the slice index in which the tumor appears to have the largest area. The approach infer a tumor growth classifier on sliding window of the volume slices and creates a discrete integral function out of the classifier predictions. The approach applies post processing on the discrete integral function which can include a smoothing function and a bias correction. The approach selects the slice index of maximum value from the post processing step.

A Time-of-flight (TOF) MRI scanning method may include: a TOF MRI scan including a first slice selection gradient applied in the Z direction at the same time as an RF pulse being applied to an imaging target; after applying the RF pulse and first slice selection gradient has ended, applying a slice selection encoding gradient and a phase encoding gradient in the Z direction and Y direction respectively; when application of the slice selection encoding gradient and phase encoding gradient ends, applying a readout gradient in the X direction; when application of the readout gradient ends, applying a tracking saturation pulse to the imaging target, and simultaneously applying a second slice selection gradient in the Z direction; when application of the tracking saturation pulse ends, applying a spoiler gradient in the X, Y and/or Z directions of the magnetic field. The method advantageously reduces the TOF MRI scanning time.

A method is for providing respiratory information. In an embodiment, the method includes receiving imaging data relating to a lung; calculating a perfusion fraction for each respective region of a set of regions of the lung, based on the imaging data; calculating a respective ventilation value for each respective region of the set of regions of the lung based on the imaging data; calculating a weighted average of respective ventilation values across all respective regions of the set of regions of the lung, wherein for each respective region of the set of regions of the lung, the respective ventilation value of the respective region is weighted with the perfusion fraction of the respective region; generating the respiratory information based on the weighted average of the respective ventilation values; and providing the respiratory information.

The present disclosure provides systems and methods for predicting a disease state of a subject using ultrasound imaging and ancillary information to the ultrasound imaging. At least two quantitative measurements of a subject, including at least one measurement taken using ultrasound imaging, as part of quantified information can be identified. One of the quantitative measurements can be compared to a first predetermined standard, included as part of ancillary information to the quantified information, in order to identify a first initial value. Further, another of the quantitative measurements can be compared to a second predetermined standard, included as part of the ancillary information, in order to identify a second initial value. Subsequently, the quantitative information can be correlated with the ancillary information using the first initial value and the second initial value to determine a final value that is predictive of a disease state of the subject.

A method, a system and a computer readable medium for automatic segmentation of a 3D medical image, the 3D medical image comprising an object to be segmented, the method characterized by comprising: carrying out, by using a machine learning model, in at least two of a first, a second and a third orthogonal orientation, 2D segmentations for the object in slices of the 3D medical image to derive 2D segmentation data; determining a location of a bounding box (10) within the 3D medical image based on the 2D segmentation data, the bounding box (10) having predetermined dimensions; and carrying out a 3D segmentation for the object in the part of the 3D medical image corresponding to the bounding box (10).

An image fusion system provides a predicted alignment between images of different modalities and synchronization of the alignment, once acquired. A spatial tracker detects and tracks a position and orientation of an imaging device within an environment. A predicted pose of an anatomical feature can be determined, based on previously acquired image data, with respect to a desired position and orientation of the imaging device. When the imaging device is moved into the desired position and orientation, a relationship is established between the pose of the anatomical feature in the image data and the pose of the anatomical feature imaged by the imaging device. Based on tracking information provided by the spatial tracker, the relationship is maintained even when the imaging device moves to various positions during a procedure.

Presented are concepts for initialising a model for model-based segmentation of an image which use specific landmarks (e.g. detected using other techniques) to initialize the segmentation mesh. Using such an approach, embodiments need not be limited to predefined model transformations, but can initialise a segmentation mesh with arbitrary shape. In this way, embodiments may provide for an image segmentation algorithm that not only delivers a robust surface-based segmentation result but also does so for strongly varying target structure variations (in terms of shape).

Systems and methods for predicting outcome of radiation therapy is described herein. An example method includes receiving respective values for tumor volume of a target patients tumor at first and second time points, and calculating a change in tumor volume between the first and second time points. The method also includes estimating a patient-specific carrying capacity based on a logistic growth model and the change in tumor volume. Additionally, the method includes predicting a volume of the target patient's tumor at a future time point during radiation treatment based, at least in part, on a historical carrying capacity reduction fraction distribution and the patient-specific carrying capacity. The method further includes predicting a patient-specific outcome of radiation therapy for the target patient based, at least in part, on the predicted volume of the target patients tumor at the future time point.

A computer-implemented method is provided for improving live video quality. The method comprises: acquiring, using a medical imaging apparatus, a stream of consecutive image frames of a subject, and the stream of consecutive image frames are acquired with reduced amount of radiation dose; applying a deep learning network model to the stream of consecutive image frames to generate an image frame with improved quality; and displaying the image frame with improved quality in real-time on a display.

A brain stimulation simulation system and method according to a preset guide system using an anonymized data-based external server are provided. According to various embodiments of the present invention, provided is a brain stimulation simulation method according to a preset guide system using an external server, the method performed by a computing device, the method including: a first server generating a global matrix for performing brain stimulation simulation on a plurality of objects by using a plurality of brain models for each of the plurality of objects; and a second server being provided with the generated global matrix from the first server and performing the brain stimulation simulation on the plurality of objects by using the provided global matrix.