Motion correction of PET images using multiple acquisition frames

Picard, Y. J.; Thompson, C.J.; Moreno-Cantú, Jorge J.

doi:10.1109/nssmic.1995.510427

Cited by 70 publications

(79 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…80 neighbors) with a prior weight of 10. For the measured data, motion artifacts were reduced by applying a multiple acquisition frame correction method (23), where the frame duration was fixed to 5 minutes in advance, because no motion tracking had been performed during acquisition. To correct for emissiontransmission misalignment, a 2D filtered backprojection reconstructed attenuation map was registered to a fast non-attenuation corrected MLEM reconstruction of each frame.…”

Section: Implementation Detailsmentioning

confidence: 99%

Voxel-based comparison of state-of-the-art reconstruction algorithms for 18F-FDG PET brain imaging using simulated and clinical data

et al. 2014

View full text Add to dashboard Cite

The resolution of a PET scanner (2.5-4.5 mm for brain imaging) is similar to the thickness of the cortex in the (human) brain (2.5 mm on average), hampering accurate activity distribution reconstruction. Many techniques to compensate for the limited resolution during or postreconstruction have been proposed in the past and have been shown to improve the quantitative accuracy. In this study, state-of-the-art reconstruction techniques are compared on a voxel-basis for quantification accuracy and group analysis using both simulated and measured data of healthy volunteers and patients with epilepsy. Methods: Maximum a posteriori (MAP) reconstructions using either a segmentation-based or a segmentation-less anatomical prior were compared to maximum likelihood expectation maximization (MLEM) reconstruction with resolution recovery. As anatomical information, a spatially aligned 3-D T1-weighted magnetic resonance image was used. Firstly, the algorithms were compared using normal brain images to detect systematic bias with respect to the true activity distribution, as well as systematic differences between two methods. Secondly, it was verified whether the algorithms yielded similar results in a group comparison study. Results: Significant differences were observed between the reconstructed and the true activity, with the largest errors when using (postsmoothed) MLEM. Only 5-10 % underestimation in cortical gray matter voxel activity was found for both MAP reconstructions. Higher errors were observed at GM edges. MAP with the segmentation-based prior also resulted in a significant bias in the subcortical regions due to segmentation inaccuracies, while MAP with the anatomical prior which does not need segmentation did not. Significant differences in reconstructed activity were also found between the algorithms at similar locations (mainly in gray matter edge voxels and in cerebrospinal fluid voxels) in the simulated as well as in the clinical data sets. Nevertheless, when comparing two groups, very similar regions of significant hypometabolism were detected by all algorithms. Conclusion: Including anatomical a priori information during reconstruction in combination with resolution modeling yielded accurate gray matter activity estimates, and a significant improvement in quantification accuracy was found when compared to post-smoothed MLEM reconstruction with resolution modeling. AsymBowsher provided more accurate subcortical GM activity estimates. It is also reassuring that the differences found between the algorithms did not hamper the detection of hypometabolic regions in the gray matter when performing a voxel-based group comparison. Nevertheless, the size of the detected clusters differed. More elaborated and application-specific studies are required to decide which algorithm is best for a group analysis.

show abstract

Section: Implementation Detailsmentioning

confidence: 99%

Voxel-based comparison of state-of-the-art reconstruction algorithms for 18F-FDG PET brain imaging using simulated and clinical data

et al. 2014

View full text Add to dashboard Cite

show abstract

“…[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%

“…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. Data-driven methods which estimate motion from temporal frames of reconstructed PET data using registration to a reference frame 2,22,[25][26][27][28] have also been reported. These methods have the advantage that no external equipment is necessary and motion compensation can be applied retrospectively.…”

Section: Introductionmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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

et al. 2016

View full text Add to dashboard Cite

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

show abstract

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

Section: Introductionmentioning

confidence: 99%

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

et al. 2013

View full text Add to dashboard Cite

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.

show abstract

Motion correction of PET images using multiple acquisition frames

Cited by 70 publications

References 10 publications

Voxel-based comparison of state-of-the-art reconstruction algorithms for 18F-FDG PET brain imaging using simulated and clinical data

Voxel-based comparison of state-of-the-art reconstruction algorithms for 18F-FDG PET brain imaging using simulated and clinical 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

Contact Info

Product

Resources

About