Koninklijke Philips Corporation N.v.

Zhongmin Steve Lin

Shalabh Chandra

Scott Kenneth Pohlman

David Nicolay

Hung Y Wong

In a dose modulation method, transmission tomographic imaging data of an associated imaging subject are acquired using a radiation source () revolving around the associated imaging subject. During the tomographic imaging, an estimated attenuation of radiation is determined for an upcoming position or angular bin () of the revolving radiation source based on attenuations determined at previously acquired positions or angular bins () of the radiation source. Prior to acquiring tomographic imaging data at the upcoming position or angular bin, a level of radiation produced by the radiation source is adjusted based on the estimated attenuation of radiation.

Dynamic dose control for computed tomography

An adaptive median filter ( ) provides dynamic detection and correction of digital image defects which are caused by defective or malfunctioning elements of a radiation detector array ( ). The adaptive median filter receives ( ) lines of pixel values of a digital image that may have defects and a user-defined defect threshold. The lines of pixel values are scanned on a pixel-by-pixel basis using a kernel of n&times;n pixels, where the kernel contains the candidate pixel being examined ( ). Each kernel is numerically reordered ( ) and a median value is calculated ( ). A defect threshold value is calculated by multiplying the user-defined defect threshold criteria and the candidate pixel value ( ). A reference value is calculated by subtracting the candidate pixel value and the median value ( ). The reference value is compared to the defect threshold value ( ). The candidate pixel value is replaced by the median value ( ) if it differs from the median value by more than the predefined defect threshold, e.g., 20%. The adaptive median filter is particularly effective in detecting and correcting double line and double column defects in digital images. In addition, the filter minimizes image blurring and maintains image resolution by filtering only defective pixels.

Method and apparatus for digital image defect correction and noise filtering

A CT scanner ( ) for obtaining a medical diagnostic image of a subject includes a stationary gantry ( ), and a rotating gantry ( ). The detected radiation is reconstructed and divided into sub-portions, which sub-portions are aligned by a registration processor ( ). The registered images are stored in a high resolution memory ( ) and a maximum artery enhancement value is calculated from the high resolution images. A resolution reducer ( ) reduces the resolution of the high resolution images. Time-density curves are found for the voxels of the images, which time-density curves are truncated to eliminate unwanted data, and analyzed to determine characteristic values. A perfusion calculator ( ) calculates perfusion by using the maximum artery enhancement value and the characteristic values. A diagnostician can view any one of a low resolution image, a high resolution image, and a perfusion image on a video monitor ( ).

Measurements with CT perfusion

A The medical diagnostic imaging apparatus includes a source ( ) for generating x-rays, an image receptor ( ) for receiving the x-rays and generating image data, and an image processing subsystem ( ) for generating corrected image data from the image data acquired by the image receptor. The image processing subsystem includes a processor ( ) that is programmed to generate noise image data ( ) by high-pass filtering ( ) uncorrected diagnostic image data acquired by the image receptor, to determine statistical data ( ) from a first subset ( ) of the noise image data, and to correct a subset ( ) of the uncorrected diagnostic image data based on the statistical data, the subset of the uncorrected diagnostic image data corresponding to the subset of the noise image data.

Method and apparatus for reducing noise artifacts in a diagnostic image

A CT scanner ( ) for obtaining a medical diagnostic image of a subject includes a stationary gantry ( ), and a rotating gantry ( ) rotatably supported on the stationary gantry ( ) for rotation about the subject. In a perfusion study time-density curves of voxels of an imaging region are computed. In a low signal identification step ( ), all voxels with low signal are identified. In a clustering step ( ), low signal voxels are clustered together. In a representative determination step ( ) representative time-density curves are computed. In a functional measurement step ( ), measurements are calculated from the combined and uncombined time-density values. In an assigning step ( ), each low signal voxel is assigned the values determined for its group. In a combining step ( ) the results of the low and normal signal voxels are combined to produce a single functional perfusion image.

Low signal correction for perfusion measurements

A CT scanner ( ) for obtaining a medical diagnostic image of a subject includes a stationary gantry ( ), and a rotating gantry ( ) rotatably supported on the stationary gantry ( ) for rotation about the subject. A plurality of temporally displaced volume images are gathered, divided into slices, and stored in slice memories ( ). A slice comparitor ( ) compares each slice to a selected reference slice. The slices are transformed by a slice transformer ( ) to align the slices thereby correcting for movement of the subject over the scan period.

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Solon, OH, United States of America

Zhongmin Steve Lin