Abstract: A system for registering a luminal network to a 3D model of the luminal network includes a computing device configured to identify potential matches in the 3D model with location data of a location sensor, assigning one of the potential matches a registration score based on a deformation model applied to the 3D model, and displaying the potential match having the highest registration score.

Abstract: Higher quality diffusion metrics and/or diffusion-weighted images are generated from lower quality input diffusion-weighted images using a suitably trained neural network (or other machine learning algorithm). High-fidelity scalar and orientational diffusion metrics can be extracted using a theoretical minimum of a single non-diffusion-weighted image and six diffusion-weighted images, achieved with data-driven supervised deep learning. As an example, a deep convolutional neural network (“CNN”) is used to map the input non-diffusion-weighted image and diffusion-weighted images sampled along six optimized diffusion-encoding directions to the residuals between the input and output high-quality non-diffusion-weighted image and diffusion-weighted images, which enables residual learning to boost the performance of CNN and full tensor fitting to generate any scalar and orientational diffusion metrics.

Abstract: The present disclosure provides a method and an apparatus for semantic segmentation of an image, capable of solving the problem in the related art associated with low speed and inefficiency in semantic segmentation of images. The method includes: receiving the image; performing semantic segmentation on the image to obtain an initial semantic segmentation result; and inputting image information containing the initial semantic segmentation result to a pre-trained convolutional neural network for semantic segmentation post-processing, so as to obtain a final semantic segmentation result. With the solutions of the present disclosure, the initial semantic segmentation result can be post-processed using the convolutional neural network, such that the speed and efficiency of the semantic segmentation of the image can be improved.

Abstract: Introduced here are approaches to assessing whether digital features (or simply “features”) detected in digital images by detection models are representative of artifacts that can obscure actual pathologies. A diagnostic platform may characterize each digital feature detected in a digital image based on its likelihood of being an artifact. For instance, a digital feature could be characterized as being representative of an artifact caused by improper illumination, an artifact caused by a physical element that is adhered to the lens through which light is collected by an imaging device, or a pathological feature indicative of a disease.

Abstract: Methods for segmenting medical images from different modalities include integrating a plurality of types of quantitative image descriptors with a deep 3D convolutional neural network. The descriptors include: (i) a Gibbs energy for a prelearned 7th-order Markov-Gibbs random field (MGRF) model of visual appearance, (ii) an adaptive shape prior model, and (iii) a first-order appearance model of the original volume to be segmented. The neural network fuses the computed descriptors to obtain the final voxel-wise probabilities of the goal regions.

Abstract: A method for correcting projection images in CT image reconstruction is provided. The method may include obtaining a plurality of projection images of a subject. Each of the plurality of projection images may correspond to one of the plurality of gantry angles. The method may further include correcting a first projection image of the plurality of projection images according to a process for generating a corrected projection image. The process may include performing, based on the first projection image and a second projection image of the plurality of projection images, a first correction on the first projection image to generate a preliminary corrected first projection image. The process may also include performing, based on at least part of the preliminary corrected first projection image, a second correction on the preliminary corrected first projection image to generate a corrected first projection image corresponding to the first gantry angle.

Abstract: The present disclosure describes methods for calibrating a spectral X-ray system to perform material decomposition with a single scan of an energy discriminating detector or with a single scan at each used X-ray spectrum. The methods may include material pathlengths exceeding the size of the volume reconstructable by the system. Example embodiments include physical and matching calibration phantoms. The physical calibration phantom is used to measure the attenuation of X-rays passing therethrough with all combinations of pathlengths through the calibration's basis materials. The matching digital calibration phantom is registered with the physical calibration phantom and is used to calculate the pathlength though each material for each measured attenuation value. A created data structure includes the X-ray attenuation for each X-ray spectrum or detector energy bin for all combinations of basis material pathlengths.

Abstract: A system for tracking at least one bone in robotized computer-assisted surgery, comprises a processing unit and a non-transitory computer-readable memory communicatively coupled to the processing unit and comprising computer-readable program instructions executable by the processing unit for: obtaining backscatter images of the at least one bone from a tracking device in a coordinate system; generating a three-dimensional geometry of a surface of the at least one bone from the backscatter images, the three-dimensional geometry of the surface being in the coordinate system; determining a position and orientation of the at least one bone in the coordinate system by matching the three-dimensional geometry of the surface of the at least one bone to a three-dimensional model of the bone; controlling an automated robotized variation of at least one of a position and orientation of the tracking device as a function of a processing of the backscatter images; and continuously outputting the position and orientation of

Abstract: A system and a method for detecting image forgery through a convolutional neural network are capable of detecting image manipulation of compressed and/or color images. The system comprises a manipulated feature pre-processing unit applying an input image to a high-pass filter to enhance features due to image forgery; a manipulated feature extraction unit extracting image manipulated feature information from the image with the enhanced features through a pre-trained convolutional neural network; a feature refining unit refining the extracted image manipulated feature information; and a manipulation classifying unit determining the image forgery based on the image manipulated feature information refined by the feature refining unit.

Abstract: Systems and methods for generating a surgical plan for altering an abnormal bone using a generic normal bone model are discussed. For example, a system for planning a surgery on an abnormal bone can include a model receiver module configured to receive a generic normal bone model. The generic normal bone model, such as a parametric model derived from statistical shape data, can include a data set representing a normal bone having an anatomical origin comparable to the abnormal bone. An input interface can be configured to receive an abnormal bone representation including a data set representing the abnormal bone. A surgical planning module can include a registration module configured to register the generic normal bone model to the abnormal bone representation by creating a registered generic model. A surgical plan formation module can be configured to identify one or more abnormal regions of the abnormal bone using the registered generic model.

Abstract: An image registration system (111) for registering a live stream of ultrasound images (112) of a beamforming ultrasound probe (113) with an X-ray image (114) is described. The image registration (111) system identifies, from the X-ray image (114), the position of a medical device (116) represented in the X-ray image (114); and determines, based on ultrasound signals transmitted between the beamforming ultrasound probe (113) and an ultrasound transducer (115) disposed on the medical device (116), a location of the ultrasound transducer (115) respective the beamforming ultrasound probe (113). Each ultrasound image from the live stream (112) is registered with the X-ray (114) image based on the identified position of the medical device (116).

Abstract: Systems and methods for quantifying a shift of an anatomical object of a patient are provided. A 3D medical image of an anatomical object of a patient is received. An initial location of landmarks on the anatomical object in the 3D medical image is determined using a first machine learning network. A 2D slice depicting the initial location of the landmarks is extracted from the 3D medical image. The initial location of the landmarks in the 2D slice is refined using a second machine learning network. A shift of the anatomical object is quantified based on the refined location of the landmarks in the 2D slice. The quantified shift of the anatomical object is output.

Abstract: A force sensed surface scanning system (20) employs a scanning robot (41) and a surface scanning controller (50). The scanning robot (41) includes a surface scanning end-effector (43) for generating force sensing data informative of a contact force applied by the surface scanning end-effector (43) to an anatomical organ. In operation, the surface scanning controller (50) controls a surface scanning of the anatomical organ by the surface scanning end-effector (43) including the surface scanning end-effector (43) generating the force sensing data, and further constructs an intraoperative volume model of the anatomical organ responsive to the force sensing data generated by the surface scanning end-effector (43) indicating a defined surface deformation offset of the anatomical organ.

Abstract: A method for training generative network, a method for generating near-infrared image and device. The method includes: obtaining a training sample set, in which the set includes near-infrared image samples and visible-light image samples; obtaining an adversarial network to be trained, in which the generative network of the adversarial network is configured to generate each near-infrared image according to an input visible-light image, the discrimination network of the adversarial network is configured to determine whether an input image is real or generated; constructing a first objective function according to a first distance between each generated near-infrared image and the corresponding near-infrared image sample in an image space and a second distance between each generated near-infrared image and the corresponding near-infrared image sample in a feature space; performing an adversarial training on the adversarial network with the set based on optimizing a value of the first objective function.

Abstract: An apparatus for evaluating validity of detection of a cancer region may be provided.

Abstract: Disclosed herein is an imaging system including a first x-ray source configured to produce first x-ray photons in a first energy range suitable for imaging, project the first x-ray photons onto an area designated for imaging, a rotatable gantry configured to rotate the first x-ray source such that the first x-ray source traverses an angular path, and a data processor having an analytical portion. The analytical portion is configured to collect first data relating to the transmission of the first x-ray photons through the area designated for imaging at a set of image-collection angles along the angular path, collect background data at a set of background-collection angles along the angular path, wherein the system acquires more than one image of the designated area for imaging between background angles. The analytical portion is also configured to remove errors in the first data using the background data, and generate a corrected image based on the removal of errors in the first data.

Abstract: In part, the disclosure relates to computer-based methods, devices, and systems suitable for detecting a delivery catheter using intravascular data. In one embodiment, the delivery catheter is used to position the intravascular data collection probe. The probe can collect data suitable for generating one or more representations of a blood vessel with respect to which the delivery catheter can be detected.

Abstract: There is provided a computed implemented method of automatically generating an adapted presentation of at least one candidate anomalous object detected from anatomical imaging data of a target individual, comprising: providing anatomical imaging data of the target individual acquired by an anatomical imaging device, analyzing the anatomical imaging data by a detection classifier for detecting at least one candidate anomalous object of the anatomical imaging data and computed associated location thereof, computing, by a presentation parameter classifier, at least one presentation parameter for adapting a presentation of a sub-set of the anatomical imaging data including the at least one candidate anomalous object according to at least the location of the candidate anomalous object, and generating according to the at least one presentation parameter, an adapted presentation of the sub-set of the anatomical imaging data including the at least one candidate anomalous object.

Abstract: Methods and systems for navigating to a target through a patient's bronchial tree are disclosed including a bronchoscope, a probe insertable into a working channel of the bronchoscope including a location sensor, and a workstation in operative communication with the probe and the bronchoscope the workstation including a user interface that guides a user through a navigation plan and is configured to present a three-dimensional (3D) view for displaying a 3D rendering of the patient's airways and a corresponding navigation plan, a local view for assisting the user in navigating the probe through peripheral airways of the patient's bronchial tree to the target, and a target alignment view for assisting the user in aligning a distal tip of the probe with the target.

Abstract: A computer-implemented method of preoperatively planning a surgical procedure on a knee of a patient including determining femoral condyle vectors and tibial plateau vectors based on image data of the knee, the femoral condyle vectors and the tibial plateau vectors corresponding to motion vectors of the femoral condyles and the tibial plateau as they move relative to each other. The method may also include modifying a bone model representative of at least one of the femur and the tibia into a modified bone model based on the femoral condyle vectors and the tibial plateau vectors. And the method may further include determining coordinate locations for a resection of the modified bone model.

Abstract: A method of reducing radiation dose for radiology imaging modalities and nuclear medicine by using a convolutional network to generate a standard-dose nuclear medicine image from low-dose nuclear medicine image, where the network includes N convolution neural network (CNN) stages, where each stage includes M convolution layers having K×K kernels, where the network further includes an encoder-decoder structure having symmetry concatenate connections between corresponding stages, downsampling using pooling and upsampling using bilinear interpolation between the stages, where the network extracts multi-scale and high-level features from the low-dose image to simulate a high-dose image, and adding concatenate connections to the low-dose image to preserve local information and resolution of the high-dose image, the high-dose image includes a dose reduction factor (DRF) equal to 1 of a radio tracer in a patient, the low-dose PET image includes a DRF of at least 4 of the radio tracer in the patient.

Abstract: A method and system for providing a mask image including receiving medical image data including a temporal dimension and generating a frequency data set including data points with in each case one frequency value by applying a Fourier transform to the image data. The Fourier transform is applied at least along the temporal dimension. The frequency data set is segmented into two sub-areas based on at least a frequency threshold value. The mask image is generated by applying an inverse Fourier transform to the first and/or the second sub-area of the frequency data set. The generated mask image is provided.

Abstract: Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.

Abstract: An X-ray computed tomography apparatus according to an embodiment includes an X-ray detector and a processing circuitry. The X-ray detector includes a plurality of detection elements arranged in a plurality of rows at least in a channel direction. The processing circuitry acquires an estimated count number of X-rays incident on the X-ray detector for each of the plurality of detection elements and determines a bit number for transmission for detection data indicating a count number of each of the plurality of detection elements based on the estimated count number.

Abstract: A system for generating high-resolution video from low-resolution images is configured to access a first video stream and a second video stream capturing an environment. The first video stream is captured by a first video capture device. The second video stream is captured by a second video capture device. Image frames of the first video stream are temporally synchronized with corresponding image frames of the second video stream. The system is also configured to generate a composite video stream with a higher resolution than the first or second video streams. Each composite image frame of the composite video stream is generated using a respective image frame of the first video stream and a temporally synchronized corresponding image frame of the second video stream as input.

Abstract: Systems and methods are provided for generating and using statistical data which is indicative of a difference in shape of a type of anatomical structure between images acquired by a first imaging modality and images acquired by a second imaging modality. This statistical data may then be used to modify a first segmentation of the anatomical structure which is obtained from an image acquired by the first imaging modality so as to predict the shape of the anatomical structure in the second imaging modality, or in general, to generate a second segmentation of the anatomical structure as it may appear in the second imaging modality based on the statistical data and the first segmentation.

Abstract: Methods and systems for training computer-aided condition detection systems. One method includes receiving a plurality of images for a plurality of patients, some of the images including an annotation associated with a condition; iteratively applying a first deep learning network to each of the images to produce a segmentation map, a feature map, and an image-level probability of the condition for each of the images; iteratively applying a second deep learning network to each feature map produced by the first network to produce a plurality of outputs; training the first network based on the segmentation map produced for each image; and training the second network based on the output produced for each of the patients. The second network includes a plurality of convolution layers and a plurality of convolutional long short-term memory (LSTM) layers. Each of the outputs includes a patient-level probability of the condition for one of the patients.

Abstract: The present disclosure relates to systems and methods for facing a tissue block. In some embodiments, a method is provided for facing a tissue block that includes imaging a tissue block to generate imaging data of the tissue block, the tissue block comprising a tissue sample embedded in an embedding material, estimating, based on the imaging data, a depth profile of the tissue block, wherein the depth profile comprises a thickness of the embedding material to be removed to expose the tissue sample to a pre-determined criteria, and removing the thickness of the embedding material to expose the tissue to the pre-determined criteria.

Abstract: Exemplary embodiments of the present invention provide a CT imaging method of coronary artery and a computer-readable storage medium, the method comprising: generating and outputting a global optimal phase image of a coronary artery; and generating and outputting a local optimal phase image of a particular trunk of the coronary artery based on a trunk selection command.

Abstract: Disclosed is a CT image generation method for attenuation correction of PET images. According to the method, a CT image and a PET image at T1 and a PET image at T2 are acquired and input into a trained deep learning network to obtain a CT image at T2; the CT image can be applied to the attenuation correction of the PET image, thereby obtaining more an accurate PET AC (Attenuation Correction) image. According to the CT image generation method for attenuation correction of PET images, the dosage of X-rays received by a patient in the whole image acquisition stage can be reduced, and physiological and psychological pressure of the patient is relieved. In addition, the later image acquisition only needs a PET imaging device, without the need of PET/CT device, cost of imaging resource distribution can be reduced, and the imaging expense of the whole stage is reduced.

Abstract: A computer-implemented method for correcting artifacts in computed tomography data is provided. The method includes inputting a sinogram into a trained sinogram correction network, wherein the sinogram is missing a pixel value for at least one pixel. The method also includes processing the sinogram via one or more layers of the trained sinogram correction network, wherein processing the sinogram includes deriving complementary information from the sinogram and estimating the pixel value for the at least one pixel based on the complementary information. The method further includes outputting from the trained sinogram correction network a corrected sinogram having the estimated pixel value.

Abstract: A framework for multi-scan image processing. A single real anatomic image of a region of interest is first acquired. One or more emission images of the region of interest are also acquired. One or more synthetic anatomic images may be generated based on the one or more emission images. One or more deformable registrations of the real anatomic image to the one or more synthetic anatomic images are performed to generate one or more registered anatomic images. Attenuation correction may then be performed on the one or more emission images using the one or more registered anatomic images to generate one or more attenuation corrected emission images.

Abstract: An object of the present invention is to provide an analysis method capable of analyzing time-series images by a method simpler than ever.

Abstract: Object specific in-homogeneities in an MRI system are corrected. Prescan information available at the MR imaging system is determined. The prescan information includes at least object specific information of an object located in the MR imaging system from which an MR image is to be generated. The prescan information does not include a B1 map of the MRI system with the object being present in the MR imaging system. The prescan information is applied to a trained machine learning module provided at the MRI system. The trained machine learning module determines and generates shimming information as output. The shimming information is applied to a shimming module of the MR imaging system, wherein the shimming module uses the shimming information to generate a corrected magnetic field B0.

Abstract: A method, an apparatus, a device, and a storage medium for extracting a cardiovascular vessel from a CTA image, the method including the steps of: performing erosion operation and dilation operation on image data successively via a preset structural element to obtain a structure mask; performing a slice-by-slice transformation on the plane of section images of the structural mask to acquire the first ascending aortic structure in the structural mask, and acquiring an aortic center position and an aortic radius in the last slice of the plane of section image of the said structural mask; establishing a binarized sphere structure according to the aortic center position and the aortic radius, and synthesizing a second ascending aorta structure by combining the first ascending aorta structure with the structure mask and the binarized sphere structure.

Abstract: Systems and methods to estimate 3D TOF scatter include acquisition of 3D TOF data, determination of 2D TOF data from the first TOF data, determination of first estimated scatter based on the second TOF data, reconstruction of a first estimated image based on the first estimated scatter and the second TOF data, determination of attenuated unscattered true coincidences based on the first estimated image, determination of second estimated scatter based on the first TOF data and the attenuated unscattered true coincidences, and reconstruction of an image of the object based on the first TOF data and the second estimated scatter.

Abstract: A method and system for reconstructing a magnetic particle distribution model based on time-frequency spectrum enhancement are provided. The method includes: scanning, by a magnetic particle imaging (MPI) device, a scan target to acquire a one-dimensional time-domain signal of the scan target; performing short-time Fourier transform to acquire a time-frequency spectrum; acquiring, by a deep neural network (DNN) fused with a self-attention mechanism, a denoised time-frequency spectrum; acquiring a high-quality magnetic particle time-domain signal; and reconstructing a magnetic particle distribution model. The method learns global and local information in the time-frequency spectrum through the DNN fused with the self-attention mechanism, thereby learning a relationship between different harmonics to distinguish between a particle signal and a noise signal.

Abstract: A computer-implemented system and method for generating a medical image is provided. In some embodiments, the medical image is generated by determining a location and an alignment for a first tracking detector with respect to a particle beam system. The direction of a beam generated from the particle beam system is determined. A first position of a first particle from a detected particle hit on the first tracking detector is also determined. A determination is made as to a first residual range of the first particle from a detected particle hit on a residual range detector. The system reconstructs a path for the first particle based on the location, the alignment, the first position, and the first residual range of the first particle. The resulting medical image that is generated by the system is based on the reconstructed path for the first particle.

Abstract: The disclosure describes artificial reality systems and techniques for saving and exporting artificial reality data. For example, an artificial reality system includes an application engine configured to generate artificial reality content based on a pose of a user participating in an artificial reality environment and a head-mounted display (HMD) configured to output the artificial reality content to the user. The HMD includes a buffer configured to hold data representative of the artificial reality environment during a time window on a rolling basis and a capture engine configured to, in response to user input, capture the data representative of the artificial reality environment held in the buffer at a point in time at which the user input was received.

Abstract: Devices, systems, and methods obtain scan data that were generated by scanning a scanned region, wherein the scan data include groups of scan data that were captured at respective angles; generate partial reconstructions of at least a part of the scanned region, wherein each partial reconstruction of the partial reconstructions is generated based on a respective one or more groups of the groups of scan data, and wherein a collective scanning range of the respective one or more groups is less than the angular scanning range; input the partial reconstructions into a machine-learning model, which generates one or more motion-compensated reconstructions of the at least part of the scanned region based on the partial reconstructions; calculate a respective edge entropy of each of the one or more motion-compensated reconstructions of the at least part of the scanned region; and adjust the machine-learning model based on the respective edge entropies.

Abstract: An X-ray analyzer has a configuration including an X-ray source, an X-ray detector configured to detect an X-ray irradiated from the X-ray source, a rotary stage (stage) disposed between the X-ray source and the X-ray detector, and configured to hold an imaging target, and a light irradiation mechanism configured to irradiate light coaxially with an X-ray optical axis of the X-ray irradiated from the X-ray source to project a shadow of the imaging target onto a position of the X-ray detector.

Abstract: Certain examples provide an image data processing system including an anatomy detector to detect an anatomy in an image and to remove items not included in the anatomy from the image. The example system includes a bounding box generator to generate a bounding box around a region of interest in the anatomy. The example system includes a voxel-level segmenter to classify image data within the bounding box at the voxel level to identify an object in the region of interest. The example system includes an output imager to output an indication of the object identified in the region of interest segmented in the image.

Abstract: A method for generating a machine learning model for characterizing a plurality of Regions Of Interest ROIs based on a plurality of 3D medical images and an associated method for characterizing a Region Of Interest ROI based on at least one 3D medical image. The methods proposed here aim to provide complementary strategies to enable a classification of ROIs from 3D medical images which could take profit of the advantageous and complementarity of both 2D and 3D CNNs to improve the accuracy of the prediction. More precisely, the present disclosure proposes a 2D model that complements the 3D model so that the sensitivity/specificity of the diagnosis is improved by taking advantage of complementary notions.

Abstract: Respective longitudinal insertion paths for two tools are planned and associated with 3D image data of a skeletal portion. While respective portions of the tools are disposed at first respective locations along their respective longitudinal insertion paths, first and second x-rays of the respective portions of the tools and the skeletal portion are acquired from respective first and second views. A computer processor matches between a tool in the first x-ray and the same tool in the second x-ray by: (A) identifying respective tool elements of each tool within the first and second x-rays, (B) registering the first and second x-rays to the 3D image data, and (C) based upon the identified respective tool elements and the registration, identifying for at least one tool element a correspondence between the tool element and the respective planned longitudinal insertion path for that tool. Other applications are also described.

Abstract: In a system and method for performing MR image reconstruction based on acquired MR measurement data of an organ structure of a patient, the MR measurement data of the organ structure is received, a-priori information about the organ structure from which the MR measurement data have been acquired is received, MR image reconstruction is performed based on the MR measurement data and taking into account the a-priori information, and the reconstructed MR image data is provided.

Abstract: A method and apparatus for anonymizing a three-dimensional medical image are provided. The apparatus determines a skin region of a three-dimensional medical image, generates a human mask based on a human tissue region of the three-dimensional medical image, the human tissue region including various organs, generates a skin expansion region in which the skin region of the three-dimensional medical image is expanded, generates an anonymization region obtained by removing a region corresponding to the human mask from the skin expansion region, and changes brightness values of voxels corresponding to the anonymization region in the three-dimensional medical image to a predefined value or an arbitrary value.

Abstract: A method for gating in tomographic imaging system includes steps of: (a) performing a tomographic imaging on an object for acquiring a plurality of projection images at different projection angles, wherein a target of the object moves periodically; (b) obtaining a projected position of the target on each of the projection images, wherein the projected position is a center of a target zone on each of the projection images; (c) calculating a parameter value of pixel values in the target zone on each of the projection images, and obtaining a curve of a moving cycle of the target according to the parameter values of the projection images; and (d) selecting the projection images under the same state in the moving cycle for image reconstruction according to the curve of the moving cycle of the target.

Abstract: A method for imaging may include directing a PET scanner to perform scans of a subject injected with a tracer during a first time period. The method may also include generating PET images by reconstructing the PET data that are generated by the PET scanner based on the scans of the subject. The method may also include determining a first portion of a blood input function of the tracer in the subject based on the PET images. The method may also include determining an integral of a second portion of the blood input function based on a kinetic model, the PET data, and the first portion of the blood input function. The method may also include determining kinetic parameters of the kinetic model based on the PET data, the first portion of the blood input function, and the integral of the second portion of the blood input function.

Abstract: The invention relates to a method of determining the sagittal rotation of a patient's pelvis based on a standard anterior posterior X-ray-image with known image parameters and a calibration of the image, for example by using at least one King-Mark calibration object. The angle of the pelvic rotation is determined between a pelvic plane which is orthogonal to the midsagittal plane of the pelvis, and the image plane of the X-ray-image. Assuming the patient's position shown on the X-ray-image represents a standard neutral position, the X-ray-image plane can be used as a functional reference plane for further calculations, for example during hip-replacement surgery. The present invention further relates to a corresponding computer program and system.

Abstract: Disclosed are systems and methods for rapid generation of simulations of a patient's spinal morphology that enable pre-operative viewing of a patient's condition and to assist surgeons in determining the best corrective procedure and with any of the selection, augmentation or manufacture of spinal devices based on the patient specific simulated condition. The simulation is generated by morphing a generic spine model with a three-dimensional curve representation of the patient's particular spinal morphology derived from existing images of the patient's condition. Other anatomical structures in the patient's skeletal system are likewise simulated by morphing a generic normal skeletal model, as applicable, particularly those skeletal entities that are connected directly or indirectly to the spinal column.