An Accurate Method for Correction of Head Movement in PET

Bühler, Paul; Just, Uwe; Will, Edmund; Kotzerke, Jörg; Hoff, Jörg van den

doi:10.1109/tmi.2004.831214

Cited by 111 publications

(98 citation statements)

References 12 publications

(28 reference statements)

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“…This definition of motion magnitude incorporates both rotational and translational components in the motion measurement, and the magnitude of motion is quantified by one value so that motion can be conveniently categorized based on its magnitude. This definition differs from previous reported methods [27][28][29] in which motion data were reported based on separate translational and rotational components of the motion transformation matrices.…”

Section: A Quantification Of Motionmentioning

confidence: 89%

“…29,38,39 When event-by-event motion correction is performed, the coordinates are transformed by motion data from detector space into a fixed object-based space, which better preserves the accuracy of the location of each LOR than the sinogram rebinning technique, since the motion corrected event may not fall exactly on the center of detector crystals.…”

Section: G Notable Features Of the Molar Algorithmmentioning

confidence: 99%

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

et al. 2013

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Purpose: Motion correction in PET has become more important as system resolution has improved. The purpose of this study was to evaluate the accuracy of event-by-event and frame-based MC methods in human brain PET imaging. Methods: Motion compensated image reconstructions were performed with static and dynamic simulated high resolution research tomograph data with frame-based image reconstructions, using a range of measured human head motion data. Image intensities in high-contrast regions of interest (ROI) and parameter estimates in tracer kinetic models were assessed to evaluate the accuracy of the motion correction methods. Results: Given accurate motion data, event-by-event motion correction can reliably correct for head motions. The average ROI intensities and the kinetic parameter estimates V T and BP ND were comparable to the true values. The frame-based motion correction methods with correctly aligned attenuation map using the average of externally acquired motion data or motion data derived from image registration give comparable quantitative accuracy. For large intraframe (>5 mm) motion, the framebased methods produced ∼9% bias in ROI intensities, ∼5% in V T , and ∼10% in BP ND estimates. In addition, in real studies that lack a ground truth, the normalized weighted residual sum of squared difference is a potential figure-of-merit to evaluate the accuracy of motion correction methods. Conclusions:The authors conclude that frame-based motion correction methods are accurate when the intraframe motion is less than 5 mm and when the attenuation map is accurately aligned. Given accurate motion data, event-by-event motion correction can reliably correct for head motion in human brain PET studies.

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Section: A Quantification Of Motionmentioning

confidence: 89%

Section: G Notable Features Of the Molar Algorithmmentioning

confidence: 99%

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

et al. 2013

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“…[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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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Purpose: Head motion during PET brain imaging can cause significant degradation of image quality. Several authors have proposed ways to compensate for PET brain motion to restore image quality and improve quantitation. Head restraints can reduce movement but are unreliable; thus the need for alternative strategies such as data-driven motion estimation or external motion tracking. Herein, the authors present a data-driven motion estimation method using a preprocessing technique that allows the usage of very short duration frames, thus reducing the intraframe motion problem commonly observed in the multiple frame acquisition method. Methods: The list mode data for PET acquisition is uniformly divided into 5-s frames and images are reconstructed without attenuation correction. Interframe motion is estimated using a 3D multiresolution registration algorithm and subsequently compensated for. For this study, the authors used 8 PET brain studies that used F-18 FDG as the tracer and contained minor or no initial motion. After reconstruction and prior to motion estimation, known motion was introduced to each frame to simulate head motion during a PET acquisition. To investigate the trade-off in motion estimation and compensation with respect to frames of different length, the authors summed 5-s frames accordingly to produce 10 and 60 s frames. Summed images generated from the motion-compensated reconstructed frames were then compared to the original PET image reconstruction without motion compensation. Results: The authors found that our method is able to compensate for both gradual and step-like motions using frame times as short as 5 s with a spatial accuracy of 0.2 mm on average. Complex volunteer motion involving all six degrees of freedom was estimated with lower accuracy (0.3 mm on average) than the other types investigated. Preprocessing of 5-s images was necessary for successful image registration. Since their method utilizes nonattenuation corrected frames, it is not susceptible to motion introduced between CT and PET acquisitions. Conclusions: The authors have shown that they can estimate motion for frames with time intervals as short as 5 s using nonattenuation corrected reconstructed FDG PET brain images. Intraframe motion in 60-s frames causes degradation of accuracy to about 2 mm based on the motion type. C 2016 American Association of Physicists in Medicine. [http://dx

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“…However, the use of external tracking devices providing additional NIH Public Access information independent of SPECT data might be expected to result in more robust correction than using solely emission data. Following the work of others for head motion compensation in SPECT [11] and PET [12,13], our group has been working towards developing a robust method to track and compensate patient body and respiratory motion in cardiac SPECT with a visual tracking system (VTS). Patient motion is estimated by the VTS through the use of stereo-imaging of retro-reflective markers on stretchy bands wrapped about the patient chest and abdomen [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

An Assessment of a Low-Cost Visual Tracking System (VTS) to Detect and Compensate for Patient Motion During SPECT

McNamara

Bruyant

Johnson

et al. 2008

IEEE Trans. Nucl. Sci.

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Patient motion is inevitable in SPECT and PET due to the lengthy period of time patients are imaged and patient motion can degrade diagnostic accuracy. The goal of our studies is to perfect a methodology for tracking and correcting patient motion when it occurs. In this paper we report on enhancements to the calibration, camera stability, accuracy of motion tracking, and temporal synchronization of a low-cost visual tracking system (VTS) we are developing. The purpose of the VTS is to track the motion of retro-reflective markers on stretchy bands wrapped about the chest and abdomen of patients. We have improved the accuracy of 3D spatial calibration by using a MATLAB optical camera calibration package with a planar calibration pattern. This allowed us to determine the intrinsic and extrinsic parameters for stereo-imaging with our CCD cameras. Locations in the VTS coordinate system are transformed to the SPECT coordinate system by a VTS/SPECT mapping using a phantom of 7 retro-reflective spheres each filled with a drop of Tc 99m . We switched from pan, tilt and zoom (PTZ) network cameras to fixed network cameras to reduce the amount of camera drift. The improved stability was verified by tracking the positions of fixed retro-reflective markers on a wall. The ability of our VTS to track movement, on average, with sub-millimeter and sub-degree accuracy was established with the 7-sphere phantom for 1 cm vertical and axial steps as well as for an arbitrary rotation and translation. The difference in the time of optical image acquisition as decoded from the image headers relative to synchronization signals sent to the SPECT system was used to establish temporal synchrony between optical and list-mode SPECT acquisition. Two experiments showed better than 100 ms agreement between VTS and SPECT observed motion for three axial translations. We were able to track 3 reflective markers on an anthropomorphic phantom with a precision that allowed us to correct motion such that no loss in visual quality was noted in motion corrected slices relative to motion free slices.

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An Accurate Method for Correction of Head Movement in PET

Cited by 111 publications

References 12 publications

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

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

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

An Assessment of a Low-Cost Visual Tracking System (VTS) to Detect and Compensate for Patient Motion During SPECT

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