A computer implemented method for generating a 3D printable model of a patient specific anatomic feature from 2D medical images is provided. A 3D image is automatically generated from a set of 2D medical images. A machine learning based image segmentation technique is used to segment the generated 3D image. A 3D printable model of the patient specific anatomic feature is created from the segmented 3D image.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Embodiments of the present disclosure are related to providing a method and device processing a first set of volumetric image data comprising cross-sectional images of a myocardium and displaying a second set of volumetric image data of the myocardium. A curved plane to rectangular plane transformation of cross-sectional images of myocardium of human heart is proposed. After the transformation, a combined and reconstructed set of myocardium images are superimposed with a modified Bull's Eye View (BEV) map and corresponding parameters indicating extent of fibrosis to obtain a second set of volumetric image data of myocardium. In addition to quantifying and displaying the extent of fibrosis, the proposed solution preserves neighborhood and adjacency criteria of abnormal tissues of myocardium walls of human heart.

A computer implemented method and apparatus for determining a location at which a given feature is represented in medical imaging data is disclosed. A first descriptor for a first location in first medical imaging data is obtained. The first location is the location within the first medical imaging data at which the given feature is represented. A second descriptor for each of a plurality of candidate second locations in second medical imaging data is obtained. A similarity metric indicating a degree of similarity with the first descriptor is calculated for each of the plurality of candidate second locations. A candidate second location is selected from among the plurality of candidate second locations based on the calculated similarity metrics. The location at which the given feature is represented in the second medical imaging data is determined based on the selected candidate second location.

Systems and methods of valve quantification are disclosed. In one embodiment, a method of mitral valve quantification is provided. The method includes generating a 3-D heart model, defining a 3-D mitral valve annulus, fitting a plane through the 3-D mitral valve annulus, measuring the distance between at least two papillary muscle heads, defining an average diameter of at least one cross section around the micro valve annulus, and determining a size of an implant to be implanted.

Methods and systems for alignment of a subject for medical imaging are disclosed, and involve providing a reference image of an anatomical region of the subject, the anatomical region comprising a target tissue, processing the reference image to generate an alignment reference image, displaying the alignment reference image concurrently with real-time video of the anatomical region, and aligning the real-time video with the alignment reference image to overlay the real-time video with the alignment reference image. Following such alignment, the subject may be imaged using, for example, fluorescence imaging, wherein the fluorescence imaging may be performed by an image acquisition assembly aligned in accordance with the alignment.

The present disclosure provides a system and method for motion field generation and image correction. The method may include obtaining a plurality of first sets of magnetic resonance (MR) image data of an object generated based on a plurality of first sets of imaging sequences. The method may include obtaining a motion curve of the object. The method may include obtaining position emission tomography (PET) image data of the object generated in a scanning time period. The method may include generating one or more target motion fields corresponding to the scanning time period based on the plurality of first sets of MR image data and the motion curve. The method may include generating one or more corrected PET images by correcting, based on the one or more target motion fields, the PET image data.

A method includes assigning, to first voxels in a model of tissue of a chamber of a heart, respective first values of a parameter at respective locations on the tissue, the first voxels representing the locations, respectively. Some of the locations are on an endocardial surface of the tissue, and others of the locations are on an epicardial surface of the tissue. The method further includes assigning respective second values to second voxels in the model, a subset of which represent a portion of the tissue between the endocardial surface and the epicardial surface, by interpolating the first values. Other embodiments are also described.

A medical image processing apparatus according to an embodiment includes processing circuitry configured to generate an output data set apparently expressing a second data set obtained by transmitting and receiving an ultrasound wave, for each scanning line, as many times as a second number that is larger than a first number, by inputting a first data set to a trained model that generates the output data set on a basis of the first data set obtained by transmitting and receiving an ultrasound wave as many times as the first number for each scanning line.

A contractile tissue-based analysis device is provided, in which a strip of contractile tissue is supported by support structure. The support structure comprises a substantially planar base element, and first and second support pillars extending from said base element. An optical detection device is arranged on the side of the base element opposite to said support pillars, and is arranged to capture image data from at least one of the head portions of the support pillars. The motion of the support pillars induced by the strip of contractile tissue can thus be captured from below, i.e. through the planar base element.