Aspects of the disclosure may involve a method of generating resection plane data for use in planning an arthroplasty procedure on a patient bone. The method may include: obtaining patient data associated with at least a portion of the patient bone, the patient data captured using a medical imaging machine; generating a three-dimensional patient bone model from the patient data, the patient bone model including a polygonal surface mesh; identifying a location of a posterior point on the polygonal surface mesh; creating a three-dimensional shape centered at or near the location; identifying a most posterior vertex of all vertices of the polygonal surface mesh that may be enclosed by the three-dimensional shape; using the most posterior vertex as a factor for determining a posterior resection depth; and generating resection data using the posterior resection depth, the resection data configured to be utilized by a navigation system during the arthroplasty procedure.

The present disclosure is related to imaging systems and methods. The method includes obtaining image data of a subject to be scanned by a medical device. The method includes determining a scan range of the subject based on the image data. The scan range includes at least one scan area of the subject. The method includes determining at least one parameter value of at least one scan parameter based on the at least one scan area of the subject.

A cognitive sensor and a method of operating the same. The cognitive sensor includes a sensor unit, which generates electric signals in response to outside stimulations; a signal processing unit, which generates sensed data regarding the outside stimulations by processing electric signals generated by the sensor unit; a cognitive circuit unit, which specifies an area of interest in the sensed data processed by the signal processing unit; and an output unit, which outputs the sensed data generated by the signal processing unit, wherein at least a portion of the sensed data output by the output unit is sensed data regarding the area of interest.

The present disclosure directs to a system and method for automated positioning of a subject. The method may include obtaining a scout image of a subject when the subject is positioned at a first position in an apparatus. The method may also include determining location information of a reference structure of the subject based on the scout image. The method may also include determining an offset between the first position of the subject relative to a characteristic point of the apparatus according to the location information of the reference structure. The method may further include moving the subject to a second position based on the offset. The method may still further include performing, using the apparatus, a procedure relating to a target structure of the subject located at the second position.

A biological tissue inspection apparatus is disclosed. The biological tissue inspection apparatus comprises a stage and a probe. The probe comprises an optical imaging device, an optical interference detector, and a light guide. The probe acquires data regarding optical images and optical interference, through the optical imaging device and the optical interference detector. The stage or the probe moves such that a selected area of the inspection object is positioned in the FOV of the optical imaging device and of the optical interference detector. The light guide is configured such that illumination light from the optical imaging device and measurement light from the optical interference detector are coaxially emitted to the inspection object.

In a magnetic resonance (MR) apparatus and an operating method wherein the MR apparatus has a scanner with a patient table that can be moved into a patient receiving region of the scanner, the patient table is held in an extended loading position, and three-dimensional scanning data describing the surface of a patient positioned in the loading position on the patient table are recorded with a 3D camera, which has a field of view that covers the patient table in the loading position. For the determination of at least one patient-related recording parameter, the scanning data are evaluated for the subsequent recording MR data.

A magnetic resonance imaging apparatus of an embodiment includes: an imaging condition setting section configured to set an imaging condition for a main scan, a preparatory scan performing section configured to repeat a preparatory scan to be performed by the use of the imaging condition for a main scan and a changeably inputted center frequency so as to generate a preparatory image in real time, a display section on which the preparatory image can be displayed in real time, an optimum center frequency setting section configured to change the center frequency so as to change a situation in which a banding artifact appears and configured to set an optimum center frequency according to a user operation based on an observation of the preparatory image, and a main scan performing section configured to reconstruct an image for a diagnosis by using the optimum center frequency having been set.

An image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to generate, from three-dimensional medical image data, a first cross-sectional image and a second cross-sectional image intersecting the first cross-sectional image and is configured to change display locations of the first cross-sectional image and the second cross-sectional image on a display, in conjunction with a change in an intersecting location of the first and the second cross-sectional images.

A system for determining a probability of normal rectal tissue composition within a region of interest of an ultrasound or photoacoustic image of the rectal tissue is disclosed. The system includes a computing device with at least one processor configured to receive at least one of a photoacoustic image and an ultrasound image; select a region of interest within the at least one of a photoacoustic image and an ultrasound image; transform the region of interest into the probability of normal rectal tissue composition using a CNN model; and display the probability of normal rectal tissue composition to an operator of the system.

A prescan is automated to the greatest extent practicable, allowing acquisition of a favorable image irrespective of skills of an operator, as well as minimizing the time related to the prescan. For a plurality of image types in imaging tasks, a comprehensive FOV including all FOVs respectively of a plurality of image types is set, and for the comprehensive FOV, it is determined whether or not an item adjusted by the prescan satisfies an allowable condition for each image type, and the prescan is executed to make an adjustment appropriate for the image type with the strictest condition, on the item not satisfying the allowable condition.