An ultrasound probe acquires ultrasound images at different directions, such as by performing beam steering. A processor analyzes the ultrasound images to detect acoustic shadow artifacts. The processor marks the acoustic shadow artifacts detected in one or more regions of one or more of the analyzed ultrasound images. The processor constructs a compound image based on unmarked regions of the analyzed ultrasound images. The processor presents the constructed compound image at a display system. The processor applies an image recognition technique to identify whether a region of an ultrasound image includes a dark area. The processor compares image data of the region corresponding with a same location in each of the ultrasound images and detects the acoustic shadow artifacts in the region having the identified dark area if at least one of the analyzed ultrasound images does not include the dark area at the region corresponding with the same location.

In an embodiment, a method includes acquiring a first image data set of the patient, via an X-ray apparatus, at a first time point during the operative intervention, the first image data set including the reference structure, the anatomical structure and the reference system between the reference structure and the anatomical structure; acquiring a second image data set of the patient at a second time point, the second image data set including at least the reference structure; registering the second image data set to the first image data set. As a result of the registering of the second image data set to the first image data set, a registered second image data set is determined. Finally, an embodiment of the method includes determining the validity of the reference system by a comparison of the registered second image data set with the first image data set.

An imaging unit of an MRI apparatus performs imaging of a positioning image of a subject including a spine; a first imaging that images a cross section including the spine and extending along a longitudinal direction of the spine; and a second imaging that images a cross section in a direction of traversing the spine. An automatic cross-section position setting unit detects a specific tissue of the spine using a scout image or an image including the spine acquired in the first imaging step, performs a matching process between the detected specific tissue of the spine and a spine model, and calculates an imaging cross-section position of the second imaging based upon a specific tissue position of the spine specified by matching, thereby performing automatic setting.

A system and method for automating an appropriate voxel prescription in a uniquely definable region of interest (ROI) in a tissue of a patient is provided, such as for purpose of conducting magnetic resonance spectroscopy (MRS) in the ROI. The dimensions and coordinates of a single three dimensional rectilinear volume (voxel) within a single region of interest (ROI) are automatically identified. This is done, in some embodiments by: (1) applying statistically identified ROI search areas within a field of view (FOV); (2) image processing an MRI image to smooth the background and enhance a particular structure useful to define the ROI; (3) identifying a population of pixels that define the particular structure; (4) performing a statistical analysis of the pixel population to fit a 2D model such as an ellipsoid to the population and subsequently fit a rectilinear shape within the model; (5) repetiting elements (1) through (4) using multiple images that encompass the 3D ROI to create a 3D rectilinear shape; (6) a repetition of elements (1) through (5) for multiple ROIs with a common FOV. A manual interface may also be provided, allowing for override to replace by manual prescription, assistance to identify structures (e.g. clicking on disc levels), or modifying the automated voxel (e.g. modify location, shape, or one or more dimensions).

An assistive apparatus for organ localization, includes storing a 3D representation and CT images of an anatomical portion of the body of a subject. A localization circuitry determines a rib region and a spine region in the CT images and calculates first and second number of voxels within a first and second region of the 3D representation, respectively. The localization circuitry determines the right side of the body in the CT images, based on a comparison result for the first and second number of voxels. The localization circuitry detects a first bottom portion of right lung based on a distribution of intensity values of pixels in a region of right lung. The localization circuitry detects a second bottom portion of the rib region and localizes the liver organ in the CT images, from a reference of the detected first bottom portion and the detected second bottom portion.

A spine image registration method includes: obtaining a CT image and an MRI image corresponding to a spine; inputting the CT image into a first model to identify at least one first vertebral body of the spine in the CT image; inputting the MRI image to a second model to identify at least one second vertebral body of the spine in the MRI image; marking the first vertebral body with at least one first landmark and marking the second vertebral body with at least one second landmark; matching the first landmark with the second landmark to obtain a corresponding relationship; performing a registration on the CT image and the MRI image according to the corresponding relationship, and generating a registered image according to the content of the CT image and the content of the MRI image located in the same coordinate space; and outputting the registered image.

Certain systems, methods, and devices described herein are configured to dynamically model a patient area for surgery and/or other treatment, dynamically identify one or more features and/or characteristics thereon such as the length and/or elasticity of the posterior longitudinal ligament (PLL), dynamically allow modification of the model, dynamically limit and/or assist in modification of the model, and/or dynamically generate guidelines for generation of patient-specific implants and/or treatment kits for a specific patient.

System and method of more efficiently identifying and segmenting anatomical structures from 2-D cone beam CT images, rather than from reconstructed 3-D volume data, is disclosed. An image processing system receives, from a cone beam CT device, at least one 2-D x-ray image, which is part of a set of x-ray images taken from a 360 degree scan of a patient with a cone beam CT imaging device. The x-ray image contains at least one anatomical structure such as vertebral bodies to be segmented. The received x-ray is then analyzed in order to identify and segment the anatomical structure contained in the x-ray image based on a stored model of anatomical structures. Once the 360 degree spin is completed, a 3-D image volume from the x-ray image set is created. The identification and segmentation information derived from the x-ray image is then added to the created 3-D image volume.

A system and method for determining patient-specific anatomical parameters to improve surgical outcomes. Some embodiments include processes for predicting the parameters of occluded anatomy. Some embodiment includes processes for more accurately identifying a center point of a ball and socket joint, such as a center point or center of rotation of a femoral head. Some embodiments include processes for identify a patient-specific spinal curvature, including more precisely determining patient specific spinal inflection points. The various steps can be performed automatically through trained computing devices and graphically presented to a surgeon for review and any necessary modifications.

The present disclosure relates to an image processing method. The method may include: obtaining image data; reconstructing an image based on the image data, the image including one or more first edges; obtaining a model, the model including one or more second edges corresponding to the one or more first edges; matching the model and the image; and adjusting the one or more second edges of the model based on the one or more first edges.