Respiratory motion blur identification and reduction in ungated thoracic PET imaging

Xu, Quansheng; Yuan, Kehong; Ye, Datian

doi:10.1088/0031-9155/56/14/016

Cited by 31 publications

(23 citation statements)

References 21 publications

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“…It is found that for small iterations (e.g. 2), MCIR outperforms in the aspect of bias and Co V and has similar noise distribution to RTA. However, for large iterations RTA has improved CoY than MCIR.…”

Section: Discussionmentioning

confidence: 89%

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Statistical evaluation of PET motion correction methods using MR derived motion fields

Polycarpou

Tsoumpas

Marsden

2011

2011 IEEE Nuclear Science Symposium Conference Record

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Although there have been various proposed methods for motion correction in PET, there is not sufficient evidence to answer which method is better in terms of image quality and quantification. This study aims to characterize the behavior of the two main motion correction methods in terms of convergence and image properties. During the first method, reconstruct transform-average (RT A), independent reconstructions of each gate are transformed to a reference gate and averaged. In the second method, motion-compensated image reconstruction (MCIR), all data are used with the motion information within a single reconstruction. The two methods are studied based on the OS EM algorithm. In this investigation motion fields were obtained from real dynamic MR acquisitions and concurrent PET data were simulated from the dynamic MR images. The two motion correction approaches were assessed statistically. Results indicate that MCIR is successful in the recovery of the true values of all regions, whereas RT A has high bias due to the non negativity constraints and interpolation errors during transformation. It has been demonstrated that whilst at low iterations (i.e. 46 sub-iterations) the two methods have similar noise characteristics; at high iterations MCIR has increased noise. Finally, for low iterations the two methods have marginally similar coefficient of variation (CoV) whereas for large number of iterations RTA has clearly better CoY than MCIR. This studyindicates that MCIR may provide superior performance overall to RT A providing the noise can be minimized.

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Section: Discussionmentioning

confidence: 89%

“…F OLLOWING the continuous improvements in PET spatial resolution [1], respiratory motion is becoming a limiting factor for PET performance [2]- [4]. Respiratory motion correction is a difficult problem due to the poor statistical quality of PET data and many approaches have been previously investigated [5].…”

Section: Introductionmentioning

confidence: 99%

Statistical evaluation of PET motion correction methods using MR derived motion fields

Polycarpou

Tsoumpas

Marsden

2011

2011 IEEE Nuclear Science Symposium Conference Record

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“…Patient respiratory motion is a source of quantitation errors in positron emission tomography (PET), due to the degradation of the image resolution and potential misalignment with the attenuation map ( μ ‐map) used for attenuation correction (AC), which is derived from computed tomography (CT) or magnetic resonance imaging (MRI) . Both phenomena can compromise accurate detection and quantitation of lesions in the reconstructed PET .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a direct motion estimation/correction method in respiratory-gated PET/MRI with motion-adjusted attenuation

et al. 2017

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Purpose: Respiratory motion compensation in PET/CT and PET/MRI is essential as motion is a source of image degradation (motion blur, attenuation artifacts). In previous work, we developed a direct method for joint image reconstruction/motion estimation (JRM) for attenuation-corrected (AC) respiratory-gated PET, which uses a single attenuation-map (l-map). This approach was successfully implemented for respiratory-gated PET/CT, but since it relied on an accurate l-map for motion estimation, the question of its applicability in PET/MRI is open. The purpose of this work is to investigate the feasibility of JRM in PET/MRI and to assess the robustness of the motion estimation when a degraded l-map is used. Methods: We performed a series of JRM reconstructions from simulated PET data using a range of simulated Dixon MRI sequence derived l-maps with wrong attenuation values in the lungs, from À100% (no attenuation) to +100% (double attenuation), as well as truncated arms. We compared the estimated motions with the one obtained from JRM in ideal conditions (no noise, true l-map as an input). We also applied JRM on 4 patient datasets of the chest, 3 of them containing hot lesions. Patient list-mode data were gated using a principal component analysis method. We compared SUV max values of the JRM reconstructed activity images and non motion-corrected images. We also assessed the estimated motion fields by comparing the deformed JRM-reconstructed activity with individually non-AC reconstructed gates. Results: Experiments on simulated data showed that JRM-motion estimation is robust to l-map degradation in the sense that it produces motion fields similar to the ones obtained when using the true l-map, regardless of the attenuation errors in the lungs (< 0.5% mean absolute difference with the reference motion field). When using a l-map with truncated arms, JRM estimates a motion field that stretches the l-map in order to match the projection data. Results on patient datasets showed that using JRM improves the SUV max values of hot lesions significantly and suppresses motion blur. When the estimated motion fields are applied to the reconstructed activity, the deformed images are geometrically similar to the non-AC individually reconstructed gates. Conclusion: Motion estimation by JRM is robust to variation of the attenuation values in the lungs. JRM successfully compensates for motion when applied to PET/MRI clinical datasets. It provides a potential alternative to existing methods where the motion fields are pre-estimated from separate MRI measurements.

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“…Respiratory organ motion degrades the accuracy of diagnostic imaging and image guided interventional procedures [1] [2] [3]. Respiratory motion modelling attempts to alleviate the detriment in the aforementioned areas [4] [5].…”

Section: Introductionmentioning

confidence: 99%

Dense motion propogation from sparse samples for free breathing respiratory motion modelling

Smith

Dasari

Lindsay

et al. 2014

2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

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The development of models of respiration for motion correction in diagnostic imaging have often utilized dynamic 4D data, on which plausible respiratory motion patterns can be estimated or validated. To date, dynamic 4D MRI has often been used, its attraction lying in its zero radiation burden and large field of view. However, limitations in scanner technology produces poor contrast images when such volumetric data are acquired at high speed (< 1s). Therefore, in this latest work we provide the first demonstration that sparse sampling of dynamic MRI may be used as an alternative approach to successfully build high contrast, high resolution 4D models of free-breathing respiratory motion. This is achieved by constrained articulation of a high contrast/ high spatial resolution single static volume. The articulation is based on estimating, in the eigen domain, complete 4D motion vector fields from sparsely-sampled freebreathing dynamic MRI data, but constrained by an eigenbasis that characterizes a corresponding average 4D single respiratory cycle dataset. We demonstrate that this approach can provide equivalent motion vector fields compared to fully sampled 4D dynamic data, whilst preserving the corresponding high resolution/ high contrast inherent in the corresponding static image volume. Validation is performed on three 4D MRI volunteer datasets using 8 extracted slices from a fast 4D acquisition (0.7sec per volume). The estimated deformation fields from sparse sampling are compared to the fully sampled equivalents, resulting in an rms error of the order of 2mm, validating the approach. We also present exemplar 4D high contrast, high resolution articulated volunteer datasets using this methodology. This approach facilitates greater freedom in the acquisition of free breathing respiratory motion sequences which may be used to inform motion modeling methods in a range of imaging modalities.

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Respiratory motion blur identification and reduction in ungated thoracic PET imaging

Cited by 31 publications

References 21 publications

Statistical evaluation of PET motion correction methods using MR derived motion fields

Statistical evaluation of PET motion correction methods using MR derived motion fields

Evaluation of a direct motion estimation/correction method in respiratory-gated PET/MRI with motion-adjusted attenuation

Dense motion propogation from sparse samples for free breathing respiratory motion modelling

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