The design and implementation of a motion correction scheme for neurological PET

Abstract

A method is described to monitor the motion of the head during neurological positron emission tomography (PET) acquisitions and to correct the data post acquisition for the recorded motion prior to image reconstruction. The technique uses an optical tracking system, Polaris, to accurately monitor the position of the head during the PET acquisition. The PET data are acquired in list mode where the events are written directly to disk during acquisition. The motion tracking information is aligned to the PET data using a sequence of pseudo-random numbers, which are inserted into the time tags in the list mode event stream through the gating input interface on the tomograph. The position of the head is monitored during the transmission acquisition, and it is assumed that there is minimal head motion during this measurement. Each event, prompt and delayed, in the list mode event stream is corrected for motion and transformed into the transmission space. For a given line of response, normalization, including corrections for detector efficiency, geometry and crystal interference and dead time are applied prior to motion correction and rebinning in the sinogram. A series of phantom experiments were performed to confirm the accuracy of the method: (a) a point source located in three discrete axial positions in the tomograph field of view, 0 mm, 10 mm and 20 mm from a reference point, (b) a multi-line source phantom rotated in both discrete and gradual rotations through +/- 5 degrees and +/- 15 degrees, including a vertical and horizontal movement in the plane. For both phantom experiments images were reconstructed for both the fixed and motion corrected data. Measurements for resolution, full width at half maximum (FWHM) and full width at tenth maximum (FWTM), were calculated from these images and a comparison made between the fixedand motion corrected datasets. From the point source measurements, the FWHM at each axial position was 7.1 mm in the horizontal direction, and increasing from 4.7 mm at the 0 mm position, to 4.8 mm, 20 mm offset, in the vertical direction. The results from the multi-line source phantom with +/- 5 degrees rotations showed a maximum degradation in FWHM, when compared with the stationary phantom, of 0.6 mm, in the horizontal direction, and 0.3 mm in the vertical direction. The corresponding values for the larger rotation, +/- 15 degrees, were 0.7 mm and 1.1 mm, respectively. The performance of the method was confirmed with a Hoffman brain phantom moved continuously, and a clinical acquisition using [11C]raclopride (normal volunteer). A visual comparison of both the motion and non-motion corrected images of the Hoffman brain phantom clearly demonstrated the efficacy of the method. A sample time-activity curve extracted from the clinical study showed irregularities prior to motion correction, which were removed after correction. A method has been developed to accurately monitor the motion of the head during a neurological PET acquisition, and correct for this motion prior to image reconstruction. The...

Improved frame-based estimation of head motion in PET brain imaging

“…1, 5,7,27 The third motion was that of an actual Volunteer. This motion was obtained by using a marker-based visual tracking system 34 tracking the head movement of a human volunteer who was coached to move while lying in the gantry of a SPECT/CT system where the motion tracking system was installed.…”

“…[6][7][8][9][10][11][12] Motion estimation can be broadly grouped into external-tracking based and data-driven methods. External tracking utilizing an electro-mechanical system was used in Green et al; 13 however, by far the most commonly used method to track motion is with infrared stereo cameras by affixing passive reflective markers 1,3,4,6,7,10,11,[14][15][16][17][18][19][20][21] or active markers that emit light 22 to the head of the patient. Recently, researchers have begun investigating the use of structured light cameras, such as the Microsoft Kinect 23 or other devices 5,9,24 which can be used to track the surface of the head without the need for markers.…”

Evaluation of motion correction methods in human brain PET imaging—A simulation study based on human motion data

“…[2][3][4] Current motion compensation methods include softwarebased image-registration [5][6][7][8][9] and hardware motion tracking using an external measurement device. 4,[10][11][12] For softwarebased methods, i.e., frame-based image registration methods, the raw data are divided into temporal frames, each of which is reconstructed without motion correction and registered post hoc to a reference orientation. 9,13,14 In this method, motion that occurs within a frame is not corrected, thus blurring the image and generating bias in kinetic parameter estimates.…”

“…4 Also, misalignment of the attenuation and emission images causes inaccuracy in attenuation correction, since only one static attenuation map is used for each frame. For hardware motion tracking methods, head motion is assumed to be rigid, and the position of the head is monitored during the scan with devices such as the Polaris optical tracking tool 10 or a structured light 3D tracking system. 15 Motion data, which describe the six degree-of-freedom transformation of the head to a reference orientation, are recorded for postprocessing of the scan data.…”

A method to synchronize signals from multiple patient monitoring devices through a single input channel for inclusion in list‐mode acquisitions

“…13) and patient head motion in neuro-PET. 14 Additionally, in some imaging protocols the input of the ECG R-wave pulses allow acquisitions to be gated in order to capture heart motion to better assess cardiac function. 15 In all of those various methods that incorporate motion tracking and/or ECG gating there is a basic need to synchronize external gating/tracking systems with the imaging system.…”