A breast cancer diagnosis system includes a probe that sequentially outputs light of a plurality of wavelengths of a near-infrared area to a target object, receives reflected light, and sequentially processes the reflected light; a central control device that receives optical data of the reflected light sensed by the probe, calculates a concentration of chromophore of the target object for each chromophore, and generates an image of the chromophores indicating a distribution of the concentration value of each chromophore; and a display that outputs the image, in which in the probe, one or more channel signal processing units including one or more light irradiation modules and light collection modules may be disposed, and each of the channel signal processing units sequentially operates so that each of the channel signal processing units may generate the optical data of the reflected light.

Systems, instruments, and methods for surgical navigation with verification feedback are provided. The systems, instruments, and methods may be used to verify a trajectory of a surgical tool during a procedure. The systems, instruments, and methods may receive one or more captured images of an anatomical portion of a patient; execute a surgical plan to insert the surgical tool into the anatomical portion; receive sensor data collected from one or more sensors being inserted into the anatomical portion; determine whether the sensor data corresponds to the surgical plan; and send, in response to determining that the sensor data does not correspond to the surgical plan, an alert indicating that the surgical tool is not being inserted according to the surgical plan. The one or more sensors may be attached to the surgical tool.

A drive unit includes: a scanner unit to which a catheter is connectable, an imaging core that executes tomographic imaging and that is positioned in the catheter; and a pull-back unit configured to support the scanner unit such that the scanner unit is displaceable. The drive unit includes a hold unit configured to control a non-hold state in which scanner unit displacement is not restricted and a hold state in which scanner unit displacement is restricted, a driving unit configured to rotationally drive the catheter imaging core, a switching input unit configured to receive a switching input of the hold state and the non-hold state, and a control unit configured to, when a switching input operation from the hold state to the non-hold state is detected while the imaging core is not rotationally driven, rotationally drive the imaging core and then set the scanner unit to the non-hold state.

The invention describes an iterative reconstructing method allowing a spatial distribution of fluorescence in an object to be obtained. The method comprises acquiring images of fluorescence in various planes at various depths in the object, so as to form a three-dimensional acquired image. It comprises an iterative reconstructing algorithm with, in each iteration, an initial fluorescence distribution or a fluorescence distribution resulting from a preceding iteration being taken into account, and the fluorescence light wave propagating through the object being simulated, so as to obtain a reconstruction of the acquired image. The acquired image, or a differential image corresponding to a comparison between the acquired image and the reconstructed image, is then back-propagated through the object, so as to update the fluorescence distribution. FIG. 5B.

Method and apparatus can be provided according to an exemplary embodiment of the present disclosure. For example, with at least one first section of an optical enclosure, it is possible to provide at least one first electro-magnetic radiation. In addition, with at least one second section provided within the enclosure, it is possible to cause, upon impact by the first radiation, a redirection of the first radiation to become at least one second radiation. Further, with at least one third section of the optical enclosure, it is possible to cause at least one second radiation to be provided to a tissue. For example, the redirection of the first radiation causes, at least approximately, a uniform optical illumination on of a surface of the tissue.

A computer implemented method for assessing an arterio-venous malformation (AVM) may include, for example, receiving a patient-specific model of a portion of an anatomy of a patient; using a computer processor to analyze the patient-specific model for identifying one or more blood vessels associated with the AVM, in the patient-specific model; and estimating a risk of an undesirable outcome caused by the AVM, by performing computer simulations of blood flow through the one or more blood vessels associated with the AVM in the patient-specific model.

An apparatus for oral imaging has a light source energizable to generate a light frequency signal ranging from a minimum to a maximum frequency. An image acquisition apparatus scans the generated light frequency signal to successive positions on a sample surface and to combine a returned signal from each successive position with the generated light frequency signal. The image acquisition apparatus has a detector that obtains a beat frequency signal from the combined returned signal and the generated light frequency signal. A processor that is in signal communication with the detector generates a processed beat signal from the combined signals, wherein the processed beat signal is indicative of the distance from the tunable laser source to the sample surface at the corresponding position. A display is in signal communication with the processor and is energizable to display distance data according to the processed beat signal for each scanned position.

There is provided a computer implemented method for generating a three dimensional (3D) image based of fluorescent illumination, comprising: receiving in parallel by each of at least three imaging sensors positioned at a respective parallax towards an object having a plurality of regions with fluorescent illumination therein, a respective sequence of a plurality of images including fluorescent illumination of the plurality of regions, each of the plurality of images separated by an interval of time; analyzing the respective sequences, to create a volume-dataset indicative of the depth of each respective region of the plurality of regions; and generating a 3D image according to the volume-dataset.

A method and a device for non-contact determination of temporal color and/or intensity variations in objects in a scene. Monoscopic overview images of the scene are detected with first and second overview cameras from two different viewing directions, and calculated to form a stereoscopic overview map. A two-dimensional detail image is detected by a detail camera from a third viewing direction and is projected on the overview map. Measurement surfaces in the scene are selected based on criteria which are predetermined depending on parameters on which conclusions are to be drawn from the color variations and/or intensity variations. Light emitted by the measurement surfaces is detected in a spatially-resolved and wavelength-resolved manner in a continuously-captured series of measurement images in a predetermined spectral range. The measurement surfaces are analyzed in the measurement images with respect to temporal variation of the intensity and/or color of the light, and the results displayed.

A method and apparatus is provided that uses a deep learning (DL) network to reduce noise and artifacts in reconstructed medical images, such as images generated using computed tomography, positron emission tomography, and magnetic resonance imaging. The DL network can operate either on pre-reconstruction data or on a reconstructed image. The DL network can be an artificial neural network or a convolutional neural network (e.g., using a three-channel volumetric kernel architecture). Different neural networks can be trained depending on the noise level, scanning protocol, or the anatomic, diagnostic or clinical objective of the reconstructed image (e.g., by partitioning the training data into noise-level range and training respective DL networks for each range). Further, the DL networks can be trained to mitigate artifacts, such as the cone-beam artifact.