Systems and methods are disclosed for correcting for artificial deformations in anatomical modeling. One method includes obtaining an anatomic model; obtaining information indicating a presence of an artificial deformation of the anatomic model; identifying a portion of the anatomic model associated with the artificial deformation; estimating a non-deformed local area corresponding to the portion of the anatomic model; and modifying the portion of the anatomic model associated with the artificial deformation, based on the estimated non-deformed local area.

Disclosed herein are methods and systems for automated detection of shadow artifacts in optical coherence tomography (OCT) and/or OCT angiography (OCTA). The shadow detection includes applying a machine-learning algorithm to the OCT dataset and the OCTA dataset to detect one or more shadow artifacts in the sample. The machine-learning algorithm is trained with first training data from first training samples that include manufactured shadows and no perfusion defects and second training data from second training samples that include perfusion defects and no manufactured shadows. The shadow artifacts in the OCTA dataset and/or OCT dataset may be suppressed to generate a shadow-suppressed OCTA dataset and/or a shadow-suppressed OCT dataset, respectively. Other embodiments may be described and claimed.

The invention relates to a method, an image processor (26) and a medical observation device (1), such as a microscope or endoscope, for observing an object (4) containing a bolus of at least one fluorophore (12). The object (4) is preferably live tissue comprising several types (16, 18, 20) of tissue. According to the method, a set (34) of component signals (36) is provided. Each component signal (36) represents a fluorescence intensity development of the fluorophore (12) over time in a different type of tissue. A time series (8) of input frames (10) is accessed, one input frame (10) after the other. The input frames (10) represent electronically coded still images of the object (4) at subsequent time. Each input frame (10) contains at least one observation area (22) comprising at least one pixel (23). In the observation area (22) of the current input frame (10) of the time series (8), a fluorescent light intensity (I) is determined over at least one fluorescence emission wavelength (15) of the fluorophore (12). This fluorescent light intensity (I.sub.1) is joined with the fluorescence light intensities (I.sub.n) of the observation area (22) of preceding input frames (10) of the time series (8) to generate a time sequence (40) of fluorescent light intensities (I.sub.1, I.sub.n) of the observation area (22). This time sequence (40) is decomposed on in a preferably linear combination (72) of at least some of the component signals (36) of the set (34). A new set (34) of component signals (36) is provided which includes only those component signals (36) which are present in the combination (72). An output frame (46) is generated, in which the observation area (22) is assigned a color from a color space depending on the combination (72) of component signals (36).

Provided is a technique for deriving a mathematical function for defining arterial and venous blood flow in the body by using a four-dimensional medical image. A method of analyzing blood flow by using a medical image according to an embodiment of the present disclosure includes: determining a position of a blood vessel from four-dimensional medical image data that is obtained by combining data of three-dimensional medical images of a patient's body captured at a preset period; deriving a primary function for an arterial input function and a venous output function by using a vascular signal in a head region and a vascular signal in a heart region from among vascular signals included in three-dimensional medical image data for the position of the blood vessel determined in the determining; and deriving a secondary function that is a final function for the arterial input function and the venous output function by using the primary function and a vascular signal in a neck region.

There is provided a medical image processing apparatus which includes a first extraction unit configured to extract coronary arteries depicted in images of a plurality of time phases relating to the heart, and to extract at least one stenosed part depicted in each coronary artery; a calculation unit configured to calculate a pressure gradient of each of the extracted coronary arteries, based on tissue blood flow volumes of the coronary arteries; a second extraction unit configured to extract an ischemic region depicted in the images; and a specifying unit configured to specify a responsible blood vessel of the ischemic region by referring to a dominance map, in which each of the extracted coronary arteries and a dominance territory are associated, for the extracted ischemic region, and to specify a responsible stenosis, based on the pressure gradient corresponding to a stenosed part in the specified responsible blood vessel.

Apparatus and methods are described for use with an endoluminal data-acquisition device configured to be moved through a lumen of a subject's body, and a two-dimensional angiographic image of the lumen. A value of a luminal-flow-related index of the subject is determined non-invasively at a plurality of locations along the lumen, at least partially by performing image processing on the angiographic image. While the endoluminal data-acquisition device is being moved through the lumen, a set of endoluminal data points of the lumen at a plurality of locations within the lumen is acquired, using the endoluminal data-acquisition device. It is determined that respective endoluminal data points correspond to respective locations along the lumen, and, in response thereto, it is determined that respective endoluminal data points correspond to respective values of the luminal flow-related index. Other applications are also described.

A pulse wave analysis apparatus including a memory, and a processor coupled to the memory and the processor configured to execute a process, the process including extracting, from each of a plurality of captured images of a subject, a plurality of image areas corresponding to each of a plurality of parts of the subject respectively, generating pieces of waveform data corresponding to the plurality of parts based on an image analysis for the plurality of image areas, each of the pieces of waveform data indicating a pulse wave of the subject, calculating a first matching degree between the pieces of waveform data, and determining whether a noise is included in the pieces of waveform data based on the first matching degree.

Systems and methods for generating three-dimensional fluid flow simulations from two-dimensional (2D) image data are provided. Data is segmented from 2D images of a sample having a biological structure with fluid flow therethrough. Three-dimensional (3D) geometries are generated from the segmented data, and then a 3D reconstruction of the biological structure is generated from the 3D geometries. This 3D geometric computational analysis tool can be used to evaluate fluid dynamics and hemodynamics in the context of the structure anatomy and geometry.

According to one embodiment, a medical image processing apparatus includes first specifier, second specifier, determiner and display controller. First specifier collates an ischemic region calculated from a blood vessel visualized into a three-dimensional image in a plurality of phases with a dominating region of the blood vessel, and specifies a culprit vessel in the ischemic region. Second specifier specifies a culprit stenosis in the culprit vessel based on a pressure index calculated from the blood vessel. Determiner determines a connection position to connect a bypass vessel that makes a detour around the culprit stenosis. Display controller displays the determined connection position on a display.

Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and/or integration with telemedicine services, programs, or software.