Multimodal imaging apparatus and methods include a rotatable gantry system with multiple sources of radiation comprising different energy levels (for example, kV and MV). Fast slip-ring technology and helical scans allow data from multiple sources of radiation to be combined or utilized to generate improved images and workflows, including for IGRT. Features include increasing the precision of spatial registrations between respective image sets to allow more precise radiation treatment delivery, reducing image artifacts (e.g., scatter, metal and beam hardening, image blur, motion, etc.), and utilization of dual energy imaging (e.g., for material separation and quantitative imaging, patient setup, online adaptive IGRT, etc.).

An x-ray imaging apparatus and associated methods are provided to process projection data from an offset detector during a helical scan, including view completion. The detector may be offset in the channel and/or axial direction. Projection data measured from a current view is combined with projection data measured from at least one conjugate view to reconstruct a target image. A two-dimensional aperture weighting scheme is used to address data redundancy.

The disclosure relates to a system and method for image reconstruction. The method may include the steps of: obtaining raw data corresponding to radiation rays within a volume, determining a radiation ray passing a plurality of voxels, grouping the voxels into a plurality of subsets such that at least some subset of voxels are sequentially loaded into a memory, and performing a calculation relating to the sequentially loaded voxels. The radiation ray may be determined based on the raw data. The calculation may be performed by a plurality of processing threads in a parallel hardware architecture. A processing thread may correspond to a subset of voxels.

An imaging data set (22) comprising detected counts along lines of response (LORs) is reconstructed (24) to generate a full-volume image at a standard resolution. A region selection graphical user interface (GUI) (26) is provided via which a user-chosen region of interest (ROI) is defined in the full-volume image, and this is automatically adjusted by identifying an anatomical feature corresponding to the user-chosen ROI and adjusting the user-chosen ROI to improve alignment with that feature. A sub-set (32) of the counts of the imaging data set is selected (30) for reconstructing the ROI, and only the selected sub-set is reconstructed (34) to generate a ROI image (36) representing the ROI at a higher resolution than the standard resolution. A fraction of the sub-set of counts may be reconstructed using different reconstruction algorithms (40) to generate corresponding sample ROI images, and a reconstruction algorithm selection graphical user interface (42) employs these sample ROI images.

A method of modeling lungs of a patient includes acquiring computed tomography data of a patient's lungs, storing a software application within a memory associated with a computer, the computer having a processor configured to execute the software application, executing the software application to differentiate tissue located within the patient's lung using the acquired CT data, generate a 3-D model of the patient's lungs based on the acquired CT data and the differentiated tissue, apply a material property to each tissue of the differentiated tissue within the generated 3-D model, generate a mesh of the 3-D model of the patient's lungs, calculate a displacement of the patient's lungs in a collapsed state based on the material property applied to the differentiated tissue and the generated mesh of the generated 3-D model, and display a collapsed lung model of the patient's lungs based on the calculated displacement of the patient's lungs.

A method may include obtaining a first acquisition time period related to a scan of a first modality performed on an object. The method may also include obtaining one or more second acquisition time periods related to a scan of a second modality performed on the object. The method may also include obtaining, based on the first acquisition time period and the one or more second acquisition time periods, target data of the object acquired in the scan of the first modality. The method may also include generating one or more target images of the object based on the target data.

The disclosure relates to a system and method for image reconstruction. The method may include the steps of: obtaining raw data corresponding to radiation rays within a volume, determining a radiation ray passing a plurality of voxels, grouping the voxels into a plurality of subsets such that at least some subset of voxels are sequentially loaded into a memory, and performing a calculation relating to the sequentially loaded voxels. The radiation ray may be determined based on the raw data. The calculation may be performed by a plurality of processing threads in a parallel hardware architecture. A processing thread may correspond to a subset of voxels.

A photocoagulation apparatus of some embodiment examples is configured to apply both treatment light for subthreshold coagulation and an OCT scan to a retina via a probe inserted in an eye. Upon receiving a user's instruction, the apparatus applies the treatment light to the retina, and applies an OCT scan to the retina at least after the treatment light application. The apparatus compares the first OCT image constructed from OCT data of the retina acquired prior to the treatment light application and the second OCT image constructed from OCT data of the retina acquired after the treatment light application, thereby acquiring change information that represents a tissue change in the retina caused by the treatment light. The apparatus displays a change image based on the change information together with a retinal image.

This disclosure introduces an approach that includes techniques for determining an optimal weighted execution sequence of available reconstruction algorithms using a multi-processor unit. The introduced approach includes executing a series of optimal weighted execution sequence candidates on a representative slice of the image data and comparing their results to select one of the candidates as the optimal weighted execution sequence.

Compositing is provided in which visual elements from different sources, including live action objects and computer graphic (CG) merged in a constant feed. Representative output images are produced during a live action shoot. The compositing system uses supplementary data, such as depth data of the live action objects for integration with CG items and light marker detection data for device calibration and performance capture. Varying capture times (e.g., exposure times) and processing times are tracked to align with corresponding incoming images and data.