An ultrasound diagnostic apparatus 1 includes an image acquisition unit 8 that transmits an ultrasound beam from an ultrasound probe 18 to a subject to acquire an ultrasound image, an optic nerve recognition unit 9 that performs image analysis on the ultrasound image acquired by the image acquisition unit 8 to recognize an optic nerve of the subject, an optic nerve evaluation unit 10 that evaluates a shape of the optic nerve of the subject recognized by the optic nerve recognition unit 9 on the basis of an anatomical structure, and an operation guide unit 12 that guides a user to operate the ultrasound probe 18 so as to acquire an ultrasound image for measurement of the optic nerve of the subject on the basis of an evaluation result obtained by the optic nerve evaluation unit 10.

The invention provides a fluid analysis apparatus, a method for operating a fluid analysis apparatus, and a fluid analysis program that perform display such that the tendency of a fluid flow in a blood vessel is easily checked. Route position information that is capable of identifying an order along a route of the anatomical structure is assigned to each position in the anatomical structure, using three-dimensional volume data in which each voxel has the information of a three-dimensional flow velocity vector indicating a flow velocity of a fluid in an anatomical structure. The three-dimensional flow velocity vector is selected such that the route position information of a position where the three-dimensional flow velocity vector is present is sequentially arranged from one point in the anatomical structure and a trajectory indicating the flow of the fluid is drawn so as to be visibly recognized.

This invention provides a signal-processing method that makes it possible to acquire, relatively easily and surely, a highly reliable normalized impulse-response signal without relying on the signal-correction processing after normalization. The signal-processing method of this invention includes a low-frequency extraction step, a high-frequency extraction step and a synthesizing step. In the low-frequency extraction step, only the low-frequency component is extracted from the spectrum of the first normalized signal NS1 obtained by normalizing the target signal S.sub.tgt in the time domain. In the high-frequency extraction step, only the high-frequency component is extracted from the spectrum of the second normalized signal NS2 obtained by normalizing the target signal S.sub.tgt in the frequency domain using the reference signal S.sub.ref. In the synthesizing step, the low-frequency component, derived from the first normalized signal NS1, and the high-frequency component, derived from the second normalized signal NS2, are synthesized to obtain a normalized impulse-response signal NS.

A method for image analysis of medical test results, comprising receiving, at a server, information from a mobile device regarding test results from a test performed using a testing device, wherein the testing device includes an alignment target disposed on the testing device and a plurality of immunoassay test strips, receiving at the server an image of the testing device from the mobile device, determining by the server RGB values for a plurality of pixels of the image, normalizing by the server the RGB values into a single value, comparing by the server the single value to a control value stored on the server, and providing by the server a risk indicator, wherein the risk indicator indicates a likelihood of a presence of a medical condition.

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 spirometry system includes an imaging device configured to capture upper body movement images of a subject during inhalation and exhalation of the subject. The system further includes at least one controller configured to receive the captured images from the imaging device and, based upon the received images, determine at least one of an image-based spirometry flow-volume curve for the subject or an image-based spirometry parameter for the subject.

A method for manufacturing a personalized naturally designed mitral valve prosthesis to precisely fit a specific patient for which the valve prosthesis is made for is provided. The method includes measuring size and shape of a mitral valve of the specific patient by using imaging methods, calculating geometry and dimensions of annular ring, leaflets and chords per the specific patient based on validated algorithms, and cutting and connecting the annular ring, leaflets and chords to form a personalized prosthesis mitral valve.

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.

Systems and methods of the present invention provide for: receiving a digital image data; modifying the digital image data to reduce a width of a feature within the digital image data; executing a dimension reduction process on the feature; storing a feature vector comprising: at least one feature for each of the received digital image data, and a correct or incorrect label associated with each feature vector; selecting the feature vector from a data store; training a classification software engine to classify each feature vector according to the label; classifying the image data as correct or incorrect according to a classification software engine; and generating an output labeling a second digital image data as correct or incorrect.