A medical imaging system comprises an operator terminal configured to obtain at least one image of a patient generated by a medical imaging device, receive one or more notes pertaining to the at least one image from an operator of the medical imaging device, store a clean set of images including the at least one image in at least one server, annotate the at least one image with the one or more notes to generate a set of annotated images; tag the set of annotated images as non-persistent, and store the set of annotated images in the at least one server; wherein the at least one server is configured to provide to a physician terminal both the clean set of images and the annotated set of images stored for the patient and automatically delete the one or more images tagged as non-persistent after review thereof by the physician.

A transport apparatus may include a patient bed unit for supporting the patient; a transferring unit for moving the patient bed unit along a first direction of the MRI device; and an elevating module for moving the patient bed unit along a second direction of the MRI device.

The present disclosure provides a system and method for PET image reconstruction. The method may include processes for obtaining physiological information and/or rigid motion information. The image reconstruction may be performed based on the physiological information and/or rigid motion information.

A method for compensating motion in positron emission tomographic, PET, data comprising coincident lines of response from positron-emitting position markers, includes: detecting a slippage of one or more of the position markers; determining slippage correction parameters based on the detected slippage; and applying motion correction to the PET data by taking into account the slippage correction parameters, thereby obtaining a motion-compensated PET data.

The invention provides for a medical apparatus (100, 300, 400) comprising a subject support (102) configured for moving a subject (106) from a first position (124) to a second position (130) along a linear path (134). The subject support comprises a support surface (108) for receiving the subject. The subject support is further configured for positioning the subject support in at least one intermediate position (128). The subject support is configured for measuring a displacement (132) along the linear path between the first position and the at least one intermediate position. Each of the at least one intermediate position is located between the first position and the second position. The medical apparatus further comprises a camera (110) configured for imaging the support surface in the first position. Execution of machine executable instructions 116 cause the a processor (116) controlling the medical apparatus to: acquire (200) an initial image (142) with the camera when the subject support is in the first position; control (202) the subject support to move the subject support from the first position to the second position; acquire (204) at least one intermediate image (144) with the camera and the displacement for each of the at least one intermediate image as the subject support is moved from the first position to the second position; and calculate (206) a height profile (150, 600, 604) of the subject by comparing the initial image and the at least one intermediate image. The height profile is at least partially calculated using the displacement. The height profile is descriptive of the spatially dependent height of the subject above the support surface.

Systems and methods for reconstructing medical images are disclosed. Measurement data from positron emission tomography (PET) data, and measurement data from an anatomy modality, such as magnetic resonance (MR) data or computed tomography (CT) data, is received from an image scanning system. A PET image is generated based on the PET measurement data, and an anatomy image is generated based on the anatomy measurement data. A trained neural network is applied to the PET image and the anatomy image to generate an attenuation map. The neural network may be trained based on anatomy and PET images. In some examples, the trained neural network generates an initial attenuation map based on the anatomy image, registers the initial attenuation map to the PET image, and generates an enhanced attenuation map based on the registration. Further, a corrected image is reconstructed based on the generated attenuation map and the PET image.

A system is provided for augmenting a three-dimensional (3D) model of a heart to indicate the tissue state. The system accesses a 3D model of a heart, accesses two-dimensional (2D) images of tissue state slices of the heart, and accesses source location information of an arrhythmia. The system augments the 3D model with an indication of a source location based on the source location information. For each of a plurality of the tissue state slices of the heart, the system augments a 3D model slice of the 3D model that corresponds to that tissue state slice with an indication of the tissue state of the heart represented by the tissue state information of that tissue state slice. The system then displays a representation of the 3D model that indicates the source location of the arrhythmia and the tissue state of the heart.

In a multi-session imaging study, information from a previous imaging session is stored in a Binary Large Object (BLOB). Current emission imaging data are reconstructed into a non-attenuation corrected (NAC) current emission image. A spatial transform is generated aligning a previous NAC emission image from the BLOB to the current NAC emission image. A previous computed tomography (CT) image from the BLOB is warped using the spatial transform, and the current emission imaging data are reconstructed with attenuation correction using the warped CT image. Alternatively, low dose current emission imaging data and a current CT image are acquired, a spatial transform is generated aligning the previous CT image to the current CT image, a previous attenuation corrected (AC) emission image from the BLOB is warped using the spatial transform, and the current emission imaging data are reconstructed using the current CT image with the warped AC emission image as prior.

Described herein are systems and methods for positioning a radiation source with respect to one or more regions of interest in a coordinate system. Such systems and methods may be used in emission guided radiation therapy (EGRT) for the localized delivery of radiation to one or more patient tumor regions. These systems comprise a gantry movable about a patient area, where a plurality of positron emission detectors, a radiation source are arranged movably on the gantry, and a controller. The controller is configured to identify a coincident positron annihilation emission path and to position the radiation source to apply a radiation beam along the identified emission path. The systems and methods described herein can be used alone or in conjunction with surgery, chemotherapy, and/or brachytherapy for the treatment of tumors.

An N-M tomography system comprising: a carrier for the subject of an examination procedure; a plurality of detector heads; a carrier for the detector heads; and a detector positioning arrangement operable to position the detector heads during performance of a scan without interference or collision between adjacent detector heads to establish a variable bore size and configuration for the examination. Additionally, collimated detectors providing variable spatial resolution for SPECT imaging and which can also be used for PET imaging, whereby one set of detectors can be selectably used for either modality, or for both simultaneously.