Respiratory Motion Correction in 3-D PET Data With Advanced Optical Flow Algorithms

Dawood, Mohammad; Büther, Florian; Jiang, Xiaoyi; Schäfers, Klaus P.

doi:10.1109/tmi.2008.918321

Cited by 162 publications

(127 citation statements)

References 32 publications

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“…General Electric Healthcare (GEHC) recently introduced a new reconstruction algorithm (Q.Freeze) based on the reconstruct, register and average (RRA) method originally proposed by Dawood et al, 24,25 for the quantification of moving lesions, addressing both movement artefacts and the problem of acquisition time. The principle of this algorithm is to first gate different time phases of the PET series and subsequently merge them using elastic transformation based on the approach of optical flow.…”

Section: Introductionmentioning

confidence: 99%

Clinical respiratory motion correction software (reconstruct, register and averaged—RRA), for 18F-FDG-PET-CT: phantom validation, practical implications and patient evaluation

Bouyeure-Petit¹,

Chastan²,

Edet-Sanson³

et al. 2017

The British Journal of Radiology

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Objective: On fluorine-18 fludeoxyglucose ( 18 F-FDG) positron emission tomography (PET) CT of pulmonary or hepatic lesions, standard uptake value (SUV) is often underestimated due to patient breathing. The aim of this study is to validate, on phantom and patient data, a motion correction algorithm [reconstruct, register and averaged (RRA)] implemented on a PET-CT system. Methods: Three phantoms containing five spheres filled with 18 F-FDG and suspended in a water or Styrofoam® 18 F-FDG-filled tank to create different contrasts and attenuation environment were acquired on a Discovery GE710. The spheres were animated with a 2-cm longitudinal respiratory-based movement. Respiratory-gated (RRA) and ungated PET images were compared with static reference images (without movement). The optimal acquisition time, number of phases and the best phase within the respiratory cycle were investigated. The impact of irregular motion was also investigated. Quantification impact was computed on each sphere. Quantification improvement on 28 lung lesions was also investigated. Results: Phantoms: 4 min was required to obtain a stable quantification with the RRA method. The reference phase and the number of phases used for RRA did not affect the quantification which was similar on static acquisitions but different on ungated images. The results showed that the maximum standard uptake value (SUV max ) restoration is majored for the smallest spheres (#2.1 ml). Patients: SUV max on RRA and ungated acquisitions were statistically different to the SUV max on whole-body images (p 5 0.05) but not different from each other (mean SUV max : 7.0 6 7.8 vs 6.9 6 7.8, p 5 0.23 on RRA and ungated images, respectively). We observed a statistically significant correlation between SUV restoration and lesion displacement, with a real SUV quantitation improvement for lesion with movement .1.2 mm. Conclusion: According to the results obtained using phantoms, RRA method is promising, showing a real impact on the lesion quantification on phantom data. With regard to the patient study, our results showed a trend towards an increase in the SUVs and a decrease in the volume between the ungated and RRA data. We also noticed a statistically significant correlation between the quantitative restoration obtained with RRA compared with ungated data and lesion displacement, indicating that the RRA approach should be reserved to patients with small lesions or nodes moving with a displacement larger than 1.2 cm. Advances in knowledge: This article investigates the performances of motion correction software recently introduced in PET. The conclusion revealed that such respiratory motion correction approach shows a real impact on the lesion quantification but must be reserved to the patient for whom lesion displacement was confirmed and high enough to clearly impact lesion evaluation.

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Section: Introductionmentioning

confidence: 99%

Clinical respiratory motion correction software (reconstruct, register and averaged—RRA), for 18F-FDG-PET-CT: phantom validation, practical implications and patient evaluation

Bouyeure-Petit¹,

Chastan²,

Edet-Sanson³

et al. 2017

The British Journal of Radiology

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“…Methods have been developed that attempt motion estimation from the PET data simultaneously with the reconstruction procedure 21 and as a separate step from reconstructed gated images. [22][23][24] It is not clear yet that such approaches yield accurate motion estimates because of poor statistics within the PET frames, poor spatial resolution of the PET data, and uniform PET signal within organs that move significantly during respiration, e.g., the myocardium and the liver.…”

Section: Introductionmentioning

confidence: 99%

Nonrigid PET motion compensation in the lower abdomen using simultaneous tagged-MRI and PET imaging

et al. 2011

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Purpose: We propose a novel approach for PET respiratory motion correction using tagged-MRI and simultaneous PET-MRI acquisitions. Methods: We use a tagged-MRI acquisition followed by motion tracking in the phase domain to estimate the nonrigid deformation of biological tissues during breathing. In order to accurately estimate motion even in the presence of noise and susceptibility artifacts, we regularize the traditional HARP tracking strategy using a quadratic roughness penalty on neighboring displacement vectors (R-HARP). We then incorporate the motion fields estimated with R-HARP in the system matrix of an MLEM PET reconstruction algorithm formulated both for sinogram and list-mode data representations. This approach allows reconstruction of all detected coincidences in a single image while modeling the effect of motion both in the emission and the attenuation maps. At present, tagged-MRI does not allow estimation of motion in the lungs and our approach is therefore limited to motion correction in soft tissues. Since it is difficult to assess the accuracy of motion correction approaches in vivo, we evaluated the proposed approach in numerical simulations of simultaneous PET-MRI acquisitions using the NCAT phantom. We also assessed its practical feasibility in PET-MRI acquisitions of a small deformable phantom that mimics the complex deformation pattern of a lung that we imaged on a combined PET-MRI brain scanner. Results: Simulations showed that the R-HARP tracking strategy accurately estimated realistic respiratory motion fields for different levels of noise in the tagged-MRI simulation. In simulations of tumors exhibiting increased uptake, contrast estimation was 20% more accurate with motion correction than without. Signal-to-noise ratio (SNR) was more than 100% greater when performing motion-corrected reconstruction which included all counts, compared to when reconstructing only coincidences detected in the first of eight gated frames. These results were confirmed in our proofof-principle PET-MRI acquisitions, indicating that our motion correction strategy is accurate, practically feasible, and is therefore ready to be tested in vivo. Conclusions: This work shows that PET motion correction using motion fields measured with tagged-MRI in simultaneous PET-MRI acquisitions can be made practical for clinical application and that doing so has the potential to remove motion blur in whole-body PET studies of the torso.

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“…[3][4][5][6][7][8] Typically, myocardial perfusion PET is performed with Rb-82 agent or if cyclotron is present at the site with 13 N-ammonia agent. In addition, a new 18 F based agent for PET myocardial perfusion imaging (flurpiridaz F 18) has been developed recently 9 and showed excellent image quality in phase I clinical trials.…”

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confidence: 99%

Automatic 3D registration of dynamic stress and rest ⁸²Rb and flurpiridaz F 18 myocardial perfusion PET data for patient motion detection and correction

et al. 2011

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Purpose: The authors aimed to develop an image-based registration scheme to detect and correct patient motion in stress and rest cardiac positron emission tomography (PET)/CT images. The patient motion correction was of primary interest and the effects of patient motion with the use of flurpiridaz F 18 and 82 Rb were demonstrated. Methods: The authors evaluated stress/rest PET myocardial perfusion imaging datasets in 30 patients (60 datasets in total, 21 male and 9 female) using a new perfusion agent (flurpiridaz F 18) (n ¼ 16) and 82 Rb (n ¼ 14), acquired on a Siemens Biograph-64 scanner in list mode. Stress and rest images were reconstructed into 4 ( 82 Rb) or 10 (flurpiridaz F 18) dynamic frames (60 s each) using standard reconstruction (2D attenuation weighted ordered subsets expectation maximization). Patient motion correction was achieved by an image-based registration scheme optimizing a cost function using modified normalized cross-correlation that combined global and local features. For comparison, visual scoring of motion was performed on the scale of 0 to 2 (no motion, moderate motion, and large motion) by two experienced observers. Results: The proposed registration technique had a 93% success rate in removing left ventricular motion, as visually assessed. The maximum detected motion extent for stress and rest were 5.2 mm and 4.9 mm for flurpiridaz F 18 perfusion and 3.0 mm and 4.3 mm for 82 Rb perfusion studies, respectively. Motion extent (maximum frame-to-frame displacement) obtained for stress and rest were (2.2 6 1.1, 1.4 6 0.7, 1.9 6 1.3) mm and (2.0 6 1.1, 1.2 60 .9, 1.9 6 0.9) mm for flurpiridaz F 18 perfusion studies and (1.9 6 0.7, 0.7 6 0.6, 1.3 6 0.6) mm and (2.0 6 0.9, 0.6 6 0.4, 1.2 6 1.2) mm for 82 Rb perfusion studies, respectively. A visually detectable patient motion threshold was established to be !2.2 mm, corresponding to visual user scores of 1 and 2. After motion correction, the average increases in contrast-to-noise ratio (CNR) from all frames for larger than the motion threshold were 16.2% in stress flurpiridaz F 18 and 12.2% in rest flurpiridaz F 18 studies. The average increases in CNR were 4.6% in stress 82 Rb studies and 4.3% in rest 82 Rb studies. Conclusions: Fully automatic motion correction of dynamic PET frames can be performed accurately, potentially allowing improved image quantification of cardiac PET data.

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Respiratory Motion Correction in 3-D PET Data With Advanced Optical Flow Algorithms

Cited by 162 publications

References 32 publications

Clinical respiratory motion correction software (reconstruct, register and averaged—RRA), for 18F-FDG-PET-CT: phantom validation, practical implications and patient evaluation

Clinical respiratory motion correction software (reconstruct, register and averaged—RRA), for 18F-FDG-PET-CT: phantom validation, practical implications and patient evaluation

Nonrigid PET motion compensation in the lower abdomen using simultaneous tagged-MRI and PET imaging

Automatic 3D registration of dynamic stress and rest ⁸²Rb and flurpiridaz F 18 myocardial perfusion PET data for patient motion detection and correction

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Respiratory Motion Correction in 3-D PET Data With Advanced Optical Flow Algorithms

Cited by 162 publications

References 32 publications

Clinical respiratory motion correction software (reconstruct, register and averaged—RRA), for 18F-FDG-PET-CT: phantom validation, practical implications and patient evaluation

Clinical respiratory motion correction software (reconstruct, register and averaged—RRA), for 18F-FDG-PET-CT: phantom validation, practical implications and patient evaluation

Nonrigid PET motion compensation in the lower abdomen using simultaneous tagged-MRI and PET imaging

Automatic 3D registration of dynamic stress and rest 82Rb and flurpiridaz F 18 myocardial perfusion PET data for patient motion detection and correction

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Product

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Automatic 3D registration of dynamic stress and rest ⁸²Rb and flurpiridaz F 18 myocardial perfusion PET data for patient motion detection and correction