Koninklijke Philips Corporation N.v.

Henrik Botterweck

Herfried Karl Wieczorek

Rolf Dieter Bippus

Andreas Goedicke

Lothar Spies

When calibrating a nuclear camera, such as a SPECT camera, point spread functions (PSF) are simulated for all possible photon energies and detection distances that the camera may experience. During manufacturer-side calibration, a point source () is imaged by the nuclear camera and a PSF therefor is measured. The measured PSF is compared to one or more simulated PSFs and a difference therebetween is determined. To calibrate the measured PSF, simulated PSF data is interpolated into the measured PSF to minimize the difference between the measured and simulated PSFs. The calibrated PSF is stored to memory and accessed during user-side calibration. A user then images a tracer sample during a routine camera calibration, and a PSF for the tracer sample is compared to the calibrated PSF to determine a level of contamination in the tracer sample. The tracer sample PSF is then calibrated for use in reconstructing nuclear images.

Practical SPECT calibration method for quantification of nuclides with high-energy contributions

An imaging system () includes at least one radiation detector unit () disposed adjacent a field of view () to detect and measure radiation from the field of view (). The detector unit () includes multiple detection modules () which each detects radiation from a prespecified region of the field of view (), each region being a fraction of the field of view. One or more pinholes () are associated with the detector unit (). Each pinhole () receives radiation from the prespecified region of the field of view () and transmits radiation to one or more associated detection modules ().

Pinhole SPECT camera with plural detector heads

In order to reduce an x-ray dose applied to a patient, it is necessary to know the dose absorbed by the patient. According to the present invention, there is provided a method of determining a local patient dose applied to a patient where after the reconstruction of the scan data into a diagnostic image, the scan data are backprojected into the patient volume, using the attenuation information of the diagnostic image to form a spatially varying photon fluence map. In parallel, the diagnostic image is segmented into anatomical structures to which dose-weighting factors are assigned. The locally absorbed dose is then calculated on the basis of the fluence map and the corresponding dose weights.

Reconstruction of local patient doses in computed tomography

A speech recognition method is described in which a basic set of models is adapted to a current speaker on account of the speaker's already noticed speech data. The basic set of models comprises models for different acoustic units. The models are described each by a plurality of model parameters. The basic set of models is then represented by a supervector in a high-dimensional vector space (model space), the supervector being formed by a concatenation of the plurality of the model parameters of the models of the basic set of models. The adaptation of this basic set of models to the speaker is effected in the model space by means of a MAP method in which an asymmetric distribution in the model space is selected as an a priori distribution for the MAP method.

Speech recognition method

Described here is a method of determining an eigenspace for representing a plurality of training speakers, in which first speaker-dependent sets of models are formed for the individual training speakers while training speech data of the individual training speakers are used and the models (SD) of a set of models are described each by a plurality of model parameters. For each speaker a combined model is then displayed in a high-dimensional model space by concatenation of the model parameters of the models of the individual training speakers to a respective coherent super vector. Subsequently, a transformation is carried out while the model space dimension is reduced to obtain eigenspace basis vectors (), which transformation utilizes a reduction criterion based on the variability of the vectors to be transformed. Then the high-dimensional model space is first in a first step reduced to a speaker subspace by a change of basis, in which speaker subspace all the training speakers are represented. In a second step, inside this speaker subspace, the transformation is applied to the vectors representing the training speakers to obtain the eigenspace basis vectors ().

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Aachen, Germany

Henrik Botterweck