Motion Correction of PET Brain Images Sample Clauses

Motion Correction of PET Brain Images. Deconvolution is also used in medical imaging, where sharper images with more visible details directly translate to better diagnosis. Patient movement during positron emission tomography (PET) scanning introduces motion blur and re- duces the resolution of the image. While some patient motion can be tolerable in low-resolution imaging systems, with new PET scanners, even small amount of motion can degrade image quality. The resolution of latest PET scanners ap- proaches 2 mm, however it is only attainable when the subject is motionless. On the other hand, it is unreasonable to expect patients to keep their heads perfectly still, unless the acquisition time is very small. Thus, one can either prevent or correct for subject motion during the scan. A cooperative patient, with the aid of a head restraint system, can often limit the movement to within 2-4 mm for the du- ration of a PET study. However, even with that restraint system, translations in the range of 5mm and rotations of 1 degree have been observed [18, 50]. Even more movement may be expected when patients suffer from psychiatric or neurologic diseases. However, if it is possible to continuously measure the position of the head, this positional information can be used to correct the measured data. Different meth- ods for head motion tracking and correction have been described in the literature. Position monitoring has been implemented using light-emitting diodes (LEDs) [95], magnetic field [50] and infrared [45, 77] sources and targets to track pa- tient head position. A commercial system able to make measurements such as these is the VICRA stereo camera from NDI (Northern Digital, Waterloo, On- tario, Canada). It provides estimates of the position of markers placed on the head at up to 20 Hz. Given that object positioning information is available, there are different ways to use it to correct patient motion. Motion correction methods that have been reported in the literature fall into three general categories [41]. Sinogram rebinning described by Bloomfield [18], Xxxxxx [22], Xxxxx [77] and Xxxxxx [98] involves using the known subject movement to move counts into the position where they would have been detected had the patient not moved. This method requires list mode reconstructions and careful consideration of scanner normalization. A second approach is the multiple ac- quisition frame (MAF) method described by Xxxxxx and Xxxxxxxx [95] wherein short duration frames are acquired and each is correc...
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