Koninklijke Philips Corporation N.v.

Koninklijke Philips Electronics N.v.

Zuo Zhao

Lingxiong Shao

Jinghan Ye

Xiyun Song

Angela Da Silva

Horace H Hines

Thomas Blaffert

Kirsten Regina Meetz

Mary K Durbin

Angela J Da Silva

A medical imaging system includes a view transformation component () and a segment combiner (). The transformation component () transforms projection data in each view of a plurality of individual segments, which each includes at least one view. The transformed projection data for substantially similar views across the plurality of individual segments have a common radius of rotation. The segment combiner () combines the transformed projection data to produce a single data set that includes the transformed projection data for each of the views of each of the plurality of individual segments.

Multi-segment reconstruction

A presentation component () presents information from one or more data source(s) () to an assessor for assessment. A receiver operating characteristic (ROC) analyzeruses an ROC analysis technique to evaluate the performance of the assessor. A feedback component () provides feedback as to the assessor's performance. A data manipulator () facilitates manipulation of the presented data.

Receiver operating characteristic-based training

An imaging system () comprises at least one radiation detector () disposed adjacent a subject receiving aperture () to detect radiation from a subject, receive the radiation and generate measured data. An image processor () iteratively reconstructs the detected radiation into image representations, in each reconstruction iteration the image processor () applies noise reduction algorithms to at least a difference between the measured data and a portion of a previous iteration image representation.

Iterative reconstruction with enhanced noise control filtering

A potential region of interest segmentation device segments an image data into regions of potential interest. From the regions of potential interest, an identifying device identifies a region of interest including an object of interest based on a set of rules. A recognizing device differentiates among the identified objects of interest and selects at least one particular object of interest.

Hot spot detection, segmentation and identification in pet and spect images

A medical imaging system () includes at least one radiation detection head () disposed adjacent a subject receiving aperture () to detect radiation from a subject. The detected radiation is reconstructed into at least one initial 2D projection image (&#x3bc;). Resolution in each initial 2D image (&#x3bc;) is restored by using the extended iterative constrained deconvolution algorithm by incorporating different estimates of the system response function which estimates correspond to different distances between the detection head and the origins of the detected radiation. Measured response functions are used to restore a series of images. The optimal image is determined by automatic searching with the figure of merit, by user's observation, or by using blind deconvolution for a concurrent estimating of the system response function and updating the original image.

Restoration of the nuclear medicine 2D planar image by iterative constrained deconvolution

A phantom () is used to calibrate a multi-modality imaging system () that includes a nuclear imaging system () and a CT scanner (). The phantom () includes marker receiving cavities () positioned at fixed locations in the phantom, in which markers () are removably placed. The markers () include CT markers (), which are imageable by the CT scanner (), and radioisotope markers (), which are imageable by the nuclear imaging system (). The radioisotope markers () are disposed into wells () provided at a center of mass of each disk-like CT marker. The markers () have a label () identifying its isotope. The phantom (), rigidly affixed to a couch (), is imaged by the nuclear imaging system () and by the CT scanner (). A transformation processor () calculates a transformation which brings centroids of the CT markers () in a CT image and the radioisotope markers () in a nuclear image into alignment.

Calibration image alignment in a PET-CT system

A method for non-rigid registration and fusion of images with physiological modeled organ motions resulting from respiratory motion and cardiac motion that are mathematically modeled with physiological constraints. A method of combining images comprises the steps of obtaining a first image dataset () of a region of interest of a subject and obtaining a second image dataset () of the region of interest of the subject. Next, a general model of physiological motion for the region of interest is provided (). The general model of physiological motion is adapted with data derived from the first image data set () to provide a subject specific physiological model (). The subject specific physiological model is applied () to the second image dataset () to provide a combined image ().

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Palo Alto, CA, United States of America

Zuo Zhao