Magnetic Resonance–based Motion Correction for Quantitative PET in Simultaneous PET-MR Imaging

Rakvongthai, Yothin; Fakhri, Georges El

doi:10.1016/j.cpet.2017.02.004

Cited by 15 publications

(13 citation statements)

References 36 publications

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“…In the radiological review regarding noise level, non‐MoCo PET images were rated to have equal or less noise than the MoCo 2000 and MoCo P2P200 PET images. This result is consistent with previous findings that the MCIR method amplifies noise compared to the non‐MoCo 27–31 . However, no significant difference was found between the three PET reconstructions (non‐MoCo, MoCo 2000, and MoCo P2P200 ) in Dunn's post hoc pairwise comparison, perhaps due to the small sample size.…”

Section: Discussionsupporting

confidence: 92%

MR‐assisted PET respiratory motion correction using deep‐learning based short‐scan motion fields

Chen

Fraum

Eldeniz

et al. 2022

Magnetic Resonance in Med

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We evaluated the impact of PET respiratory motion correction (MoCo) in a phantom and patients. Moreover, we proposed and examined a PET MoCo approach using motion vector fields (MVFs) from a deep-learning reconstructed short MRI scan. Methods:The evaluation of PET MoCo was performed in a respiratory motion phantom study with varying lesion sizes and tumor to background ratios (TBRs) using a static scan as the ground truth. MRI-based MVFs were derived from either 2000 spokes (MoCo 2000 , 5-6 min acquisition time) using a Fourier transform reconstruction or 200 spokes (MoCo P2P200 , 30-40 s acquisition time) using a deep-learning Phase2Phase (P2P) reconstruction and then incorporated into PET MoCo reconstruction. For six patients with hepatic lesions, the performance of PET MoCo was evaluated using quantitative metrics (SUV max , SUV peak , SUV mean , lesion volume) and a blinded radiological review on lesion conspicuity.Results: MRI-assisted PET MoCo methods provided similar results to static scans across most lesions with varying TBRs in the phantom. Both MoCo 2000 and MoCo P2P200 PET images had significantly higher SUV max , SUV peak , SUV mean and significantly lower lesion volume than non-motion-corrected (non-MoCo) PET images. There was no statistical difference between MoCo 2000 and MoCo P2P200 PET images for SUV max , SUV peak , SUV mean or lesion volume. Both radiological reviewers found that MoCo 2000 and MoCo P2P200 PET significantly improved lesion conspicuity. Conclusion: An MRI-assisted PET MoCo method was evaluated using the ground truth in a phantom study. In patients with hepatic lesions, PET MoCo images improved quantitative and qualitative metrics based on only 30-40 s of MRI motion modeling data.

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Section: Discussionsupporting

confidence: 92%

MR‐assisted PET respiratory motion correction using deep‐learning based short‐scan motion fields

Chen

Fraum

Eldeniz

et al. 2022

Magnetic Resonance in Med

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“…In PET/MR hybrid scanners, the use of MRI navigators or tagging pulse sequences, interleaved with normal acquisition, can provide the rigid or non-rigid motion fields needed for motion correction of the PET data [117]. These and other MR-based methods are discussed in section 6 of this roadmap article.…”

Section: Line-of-response Rebinningmentioning

confidence: 99%

Quantitative PET in the 2020s: a roadmap

et al. 2021

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Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in health and disease. Over the last 30 years a large amount of the physics and engineering effort in PET has been motivated by the dominant clinical application during that period, oncology. This has led to important developments such as PET/CT, whole-body PET, 3D PET, accelerated statistical image reconstruction, and time-of-flight PET. Despite impressive improvements in image quality as a result of these advances, the emphasis on static, semiquantitative "hot spot" imaging for oncologic applications has meant that the capability of PET for quantifying biologically relevant parameters based on tracer kinetics has not been fully exploited. More recent advances, such as PET/MR and total body PET, have opened up the ability to address a vast range of new research questions from which a future expansion of applications and radiotracers appears highly likely. Many of these new applications and tracers will, at least initially, require quantitative analyses that more fully exploit the exquisite sensitivity of PET and the tracer principle on which it is based. It is also expected that they will require more sophisticated quantitative analysis methods than those that are currently available. At the same time, artificial intelligence is revolutionizing data analysis and impacting the relationship between the statistical quality of the acquired data and the information we can extract from the data. In this roadmap, leaders of the key sub-disciplines of the field identify the challenges and opportunities to be addressed over the next 10 years that will enable PET to realise its full quantitative potential, initially in research laboratories and, ultimately, in clinical practice.

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“…7,8 For larger lesions, respiratory motion may likewise introduce bias in radiomic features that quantify lesion shape and texture. [9][10][11] Additionally, the mismatch between PET and computed tomography (CT) images due to motion may lead to errors in attenuation correction and scatter estimation. 12,13 Hence, there is an extensive ongoing effort to understand and evaluate the effect of respiratory motion in routine clinical PET imaging, particularly with respect to thoracic lesion detectability and quantification.…”

Section: Introductionmentioning

confidence: 99%

Design of an anthropomorphic PET phantom with elastic lungs and respiration modeling

Black¹,

Yazdi²,

Wong³

et al. 2022

Preprint

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Purpose Respiratory motion during positron emission tomography (PET) scans can be a major detriment to image quality in oncological imaging, leading to loss of quantification accuracy and false negative findings. The impact of motion on lesion quantification and detectability can be assessed using anthropomorphic phantoms with realistic anatomy representation and motion modelling. In this work we design and build such a phantom, with careful consideration of system requirements and detailed force analysis.Methods: We start from a previously-developed anatomically-accurate shell of a human torso and add elastic lungs with a highly controllable actuation mechanism which replicates the physics of breathing. The space outside the lungs is filled with a radioactive water solution. To maintain anatomical accuracy in the torso and realistic gamma ray attenuation, all motion mechanisms and actuators are positioned outside of the phantom compartment. The actuation mechanism can produce a plethora of custom respiratory waveforms with breathing rates up to 25 breaths per minute and tidal volumes up to 1200mL.Results: Several tests were performed to validate the performance of the phantom assembly, in which the phantom was filled with water and given respiratory waveforms to execute. All parts demonstrated nominal performance. Force requirements were not exceeded and no leaks were detected, although continued use of the phantom is required to evaluate wear. The respiratory motion was determined to be within a reasonable realistic range. Conclusions: The full mechanical design is described in this paper, as well as a software application with graphical user interface which was developed to plan and visualize respiratory patterns. Both are available open source and linked in this paper. The developed phantom will facilitate future work in evaluating the impact of respiratory motion on lesion quantification and detectability.

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Magnetic Resonance–based Motion Correction for Quantitative PET in Simultaneous PET-MR Imaging

Cited by 15 publications

References 36 publications

MR‐assisted PET respiratory motion correction using deep‐learning based short‐scan motion fields

MR‐assisted PET respiratory motion correction using deep‐learning based short‐scan motion fields

Quantitative PET in the 2020s: a roadmap

Design of an anthropomorphic PET phantom with elastic lungs and respiration modeling

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