Motion correction eliminates discontinuities in parametric PET images of neuroreceptor binding

Herzog, Hans; Tellmann, Lutz; Fulton, Roger; Stangier, Isabelle; Kops, Elena Rota; Bente, Kay; Pietrzyk, U.

doi:10.1038/sj.jcbfm.9591524.0622

Cited by 34 publications

(63 citation statements)

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“…Such motion can occur either between frames or within frames and can cause significant changes in voxel-wise time-activity curves (TACs), especially on the boundaries of regions with significantly different kinetics and high activity gradient. Such blurring across frames could subsequently lead to blurring of the kinetic parameter maps (Herzog et al 2005, Dinelle et al 2011, Keller et al 2012 or even affect the extraction of image-derived input functions from the dynamic emission data (Mourik et al 2011). Furthermore, patient movement will most likely cause an emission/attenuation mismatch in the affected frames, resulting in errors during attenuation correction and scatter estimation (Anton-Rodriguez et al 2010, Häggström et al 2014.…”

Section: Introductionmentioning

confidence: 99%

Impact of time-of-flight on indirect 3D and direct 4D parametric image reconstruction in the presence of inconsistent dynamic PET data

2016

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Kinetic parameter estimation in dynamic PET suffers from reduced accuracy and precision when parametric maps are estimated using kinetic modelling following image reconstruction of the dynamic data. Direct approaches to parameter estimation attempt to directly estimate the kinetic parameters from the measured dynamic data within a unified framework. Such image reconstruction methods have been shown to generate parametric maps of improved precision and accuracy in dynamic PET. However, due to the interleaving between the tomographic and kinetic modelling steps, any tomographic or kinetic modelling errors in certain regions or frames, tend to spatially or temporally propagate. This results in biased kinetic parameters and thus limits the benefits of such direct methods. Kinetic modelling errors originate from the inability to construct a common single kinetic model for the entire field-of-view, and such errors in erroneously modelled regions could spatially propagate. Adaptive models have been used within 4D image reconstruction to mitigate the problem, though they are complex and difficult to optimize. Tomographic errors in dynamic imaging on the other hand, can originate from involuntary patient motion between dynamic frames, as well as from emission/transmission mismatch. Motion correction schemes can be used, however, if residual errors exist or motion correction is not included in the study protocol, errors in the affected dynamic frames could potentially propagate either temporally, to other frames during the kinetic modelling step or spatially, during the tomographic step. In this work, we demonstrate a new strategy to minimize such error propagation in direct 4D image reconstruction, focusing on the tomographic step rather than the kinetic modelling step, by incorporating time-of-flight (TOF) within a direct 4D reconstruction framework. Using ever improving TOF resolutions (580 ps, 440 ps, 300 ps and 160 ps), we demonstrate that direct 4D TOF image reconstruction can substantially prevent kinetic parameter error propagation either from erroneous kinetic modelling, inter-frame motion or emission/ transmission mismatch. Furthermore, we demonstrate the benefits of TOF in parameter estimation when conventional post-reconstruction (3D) methods are used and compare the potential improvements to direct 4D methods. Further improvements could possibly be achieved in the future by combining TOF direct 4D image reconstruction with adaptive kinetic models and interframe motion correction schemes.

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Section: Introductionmentioning

confidence: 99%

Impact of time-of-flight on indirect 3D and direct 4D parametric image reconstruction in the presence of inconsistent dynamic PET data

2016

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“…In prior work, the multiple acquisition frame (MAF) approach refers to the division of PET data to multiple frames based on a motion-threshold and compensation of motion by alignment and summation of reconstructed frames. Unlike MAF approaches, 3,4,15,22,31,32 we derive the motion estimates from the PET data itself using image registration. Similar methods as ours have been proposed previously.…”

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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“…Herzog et al reported noticeable reduction of motion artifacts in kinetic parameter estimates in real studies using the external tracked motion data and frame-based motion correction. 4 When accurate externally tracked motion data are available, in theory, event-by-event motion correction has the potential for the greatest accuracy. We have implemented this approach in MOLAR, the Motion-compensation OSEM Listmode Algorithm for Resolution-recovery reconstruction for the HRRT, 16 and routinely use this algorithm in human brain PET reconstructions.…”

Section: Introductionmentioning

confidence: 99%

“…Montgomery et al reported that intraframe motion resulted in increased variability in regional activity and binding potential estimates in test-retest studies. 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.…”

Section: Introductionmentioning

confidence: 99%

“…[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.…”

Section: Introductionmentioning

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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Motion correction eliminates discontinuities in parametric PET images of neuroreceptor binding

Cited by 34 publications

References 0 publications

Impact of time-of-flight on indirect 3D and direct 4D parametric image reconstruction in the presence of inconsistent dynamic PET data

Impact of time-of-flight on indirect 3D and direct 4D parametric image reconstruction in the presence of inconsistent dynamic PET data

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

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

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