Systems and methods are provided to plan a spinal correction surgery. The method includes measuring parameters of a spine in a two-dimensional (2D) spinal image including a thoracic Cobb angle and a thoracic kyphosis (TK) and transforming the 2D image to a three-dimensional (3D), spinal image representation. The transforming includes performing segmentation of spine elements in the 2D image, and applying a formula based on the thoracic Cobb angle and the TK to the spine elements. The method includes identifying a TK goal having a post-operative TK value to selected spine elements, transforming a gap of the spine elements representative of a difference between the pre-operative TK in 3D spinal image representation and the TK goal to create a 3D post-operative spinal image representation, and determining a first rod design based on the 3D post-operative spinal image representation to achieve the post-operative TK value in the spine elements.

A system and method for analyzing images to optimize orthopedic functionality at a site within a patient, including obtaining at least a first, reference image of the site, or a corresponding contralateral site, the first image including at least a first anatomical region or a corresponding anatomical region. At least a second, intra-operative results image of the site is obtained. At least one point is selected to serve as a reference for both images during analysis including at least one of scaling, calculations, and image comparisons.

Presented herein are systems and methods that provide for improved computer aided display and analysis of nuclear medicine images. In particular, in certain embodiments, the systems and methods described herein provide improvements to several image processing steps used for automated analysis of bone scan images for assessing cancer status of a patient. For example, improved approaches for image segmentation, hotspot detection, automated classification of hotspots as representing metastases, and computation of risk indices such as bone scan index (BSI) values are provided.

An imaging device for obtaining long-film images of an anatomical element of a patient or of another object includes a wheeled base comprising an elongate track; a trolley comprising a base portion slidably connected to the elongate track and an upper portion rotatably connected to the base portion, the trolley slidable along the elongate track a distance of at least 40 cm; a C-shaped arm defining a semi-circle about a C-shaped arm axis, the C-shaped arm rotatably supported by the upper portion of the trolley; a source fixedly secured to the C-shaped arm; and a detector fixedly secured to the C-shaped arm opposite the source.

An X-ray imaging apparatus includes an X-ray generator including a plurality of X-ray sources, an X-ray detector configured to detect X-rays radiated from the plurality of X-ray sources and generate a plurality of pieces of projection data, and a processor configured to apply log projection to each of the plurality of pieces of projection data, to apply weighted projection to the log-projected projection data, to apply a bidirectional ramp filter to the weighted-projected projection data, and to generate a tomographic image reconstructed based on each of the projection data to which the bidirectional ramp filter is applied.

Systems and methods of detecting a presence of opacification or pneumatization in skeletal structures of patients are disclosed. The systems and methods include receiving images, processing the images using a convolutional neural network, and generating, with the convolutional neural network, an opacification score for the image. Systems and methods include training the convolutional neural network to delineate skeletal structure pixels within a computed tomography scan image and to generate an intensity value for each skeletal structure pixel within a computed tomography scan image to determine an opacification score for the computed tomography scan image.

In accordance with one or more embodiments herein, a system for creating a decision support material indicating damage to at least a part of an anatomical joint of a patient, wherein the created decision support material comprises one or more damage images, is provided. The system comprises a storage media and at least one processor, wherein the at least one processor is configured to i) receive a series of radiology images of the at least part of the anatomical joint from the storage media; ii) obtain a three-dimensional image representation of the at least part of the anatomical joint which is based on at least a part of said series of radiology images, by generating said three-dimensional image representation in an image segmentation process based on said series of radiology images, or receiving said three-dimensional image representation from a storage media; iii) identify tissue parts of the anatomical joint in at least one of at least a part of said series of radiology images and/or the three-dimensional image representation using image analysis; iv) determine damage to the identified tissue parts in the anatomical joint by analyzing at least one of at least a part of said series of radiology images and/or the three-dimensional image representation of the at least part of the anatomical joint; v) determine suitable sizes and suitable implanting positions for one or more graft plugs based on the determined damage; vi) mark damage to the anatomical joint and suitable sizes and implanting positions for the one or more graft plugs in the obtained three-dimensional image representation of the anatomical joint; and vii) generate a decision support material, where the determined damage to the at least part of the anatomical joint and the suitable sizes and implanting positions for the one or more graft plugs are marked in at least one of the one or more damage images of the decision support material, and at least one of the one or more damage images is generated based on the obtained three-dimensional image representation of the at least part of the anatomical joint.

A radiological apparatus including: a gantry encapsulated within a cover, a patient platform, and two radiation sources with imaging directions orthogonal to each other, sliding vertically to perform vertical scanning of a patient standing on the platform. The gantry cover top view is L shaped, each radiation source being located outside the gantry cover, inside the angular sector of the L, and is encapsulated within a cover sliding vertically with the radiation source it encapsulates. The radiological apparatus also includes: a first security device stopping the vertical scanning, when it detects a patient body part going outside a first predetermined area, to avoid collision with the vertically sliding radiation sources covers, and a second security device stopping the vertical scanning, when it detects an object or a person external to the radiological apparatus within a second predetermined area, to avoid collision with the vertically sliding radiation sources covers.

A radiological imaging method including: 2 radiation sources with imaging directions orthogonal to each other, performing vertical scanning of a standing patient along a vertical scanning direction, wherein the radiological method includes at least one operating mode in which: a frontal scout view is made so as to identify a specific bone(s) localization within the frontal scout view, both driving current intensity and voltage intensity modulations of the frontal radiation source, depending on patient thickness and on the identified specific bone(s) localization along the vertical scanning direction, are performed simultaneously, preferably synchronously, and automatically, so as to improve a compromise between: lowering the global radiation dose received by a patient during the vertical scanning, and increasing the local image contrasts of the identified specific bone(s) localization at different imaging positions along the vertical scanning direction, for the frontal image.

A computing system generates, based on medical imaging data of bones of a joint of a patient, bony landmark data that characterizes relationships between two or more landmarks on one or more of the bones of the joint of the patient. Additionally, the computing system applies a classifier algorithm that has been trained using training data to select a class associated with the patient from among a plurality of classes. The classifier algorithm takes the bony landmark data of the patient as input.