Joint PET-MR respiratory motion models for clinical PET motion correction

Manber, Richard; Thielemans, Kris; Hutton, Brian F.; Wan, Simon; McClelland, Jamie R.; Barnes, Anna; Arridge, Simon; Ourselin, Sébastien; Atkinson, David

doi:10.1088/0031-9155/61/17/6515

Cited by 28 publications

(31 citation statements)

References 21 publications

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“…In addition, the flexible body surface coils are not accounted for in clinical AC PET images and may lead to a regionally dependent bias [46]. Furthermore, respiratory motion can lead to PET image blurring, artifacts, and tracer uptake quantification errors in general [47], but this affects both PET/MRI and PET/CT data, and motion correction methods are improving [47–50]. A review study by Spick et al [6] summarized 46 studies (including 2340 patients) and found that the PET/MRI and PET/CT provide comparable diagnostic information for most types of cancer despite both technical and operational issues.…”

Section: Discussionmentioning

confidence: 99%

Image quality and detectability in Siemens Biograph PET/MRI and PET/CT systems—a phantom study

et al. 2019

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Background The technology of modern positron emission tomography (PET) systems continuously improving, and with it the possibility to detect smaller lesions. Since first introduced in 2010, the number of hybrid PET/magnetic resonance imaging (MRI) systems worldwide is constantly increasing. It is therefore important to assess and compare the image quality, in terms of detectability, between the PET/MRI and the well-established PET/computed tomography (CT) systems. For this purpose, a PET image quality phantom (Esser) with hot spheres, ranging from 4 to 20 mm in diameter, was prepared with fluorodeoxyglucose and sphere-to-background activity concentrations of 8:1 and 4:1, to mimic clinical conditions. The phantom was scanned on a PET/MRI and a PET/CT system for both concentrations to obtain contrast recovery coefficients (CRCs) and contrast-to-noise ratios (CNRs), for a range of reconstruction settings. The detectability of the spheres was scored by three human observers for both systems and concentrations and all reconstructions. Furthermore, the impact of acquisition time on CNR and observer detectability was investigated. Results Reconstructions applying point-spread-function modeling (and time-of-flight for the PET/CT) yielded the highest CRC and CNR in general, and PET/CT demonstrated slightly higher values than PET/MRI for most sphere sizes. CNR was dependent on reconstruction settings and was maximized for 2 iterations, a pixel size of less than 2 mm and a 4 mm Gaussian filter. Acquisition times of 97 s (PET/MRI) and 150 s (PET/CT) resulted in similar total net true counts. For these acquisition times, the smallest detected spheres by the human observers in the 8:1 activity concentration was the 6-mm sphere with PET/MRI (CNR = 5.6) and the 5-mm sphere with PET/CT (CNR = 5.5). With an acquisition time of 180 s, the 5-mm sphere was also detected with PET/MRI (CNR = 5.8). The 8-mm sphere was the smallest detected sphere in the 4:1 activity concentration for both systems. Conclusion In this experimental study, similar detectability was found for the PET/MRI and the PET/CT, although for an increased acquisition time for the PET/MRI.

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Section: Discussionmentioning

confidence: 99%

Image quality and detectability in Siemens Biograph PET/MRI and PET/CT systems—a phantom study

et al. 2019

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“…Previous works have used PCA to generate surrogate signals to drive respiratory motion models, but these have applied PCA to raw PET data (Manber et al 2016 ), or the thermal noise variance obtained from the raw k-space data (Andreychenko et al 2018 ). To the best of our knowledge, PCA has not been used before to generate surrogate signals from 2D cine-MR images to build and drive motion models, as proposed in this study.…”

Section: Discussionmentioning

confidence: 99%

“…MR-based motion models have also been proposed for a wide range of applications, including PET-MR (Baumgartner et al 2014 , Manber et al 2016 , Küstner et al 2017 ), MR-guided high intensity focused ultrasound and radiotherapy (Baumgartner et al 2017 ). However, none of these models used surrogate signals derived from 2D cine-MR images to drive the motion model.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of MRI-derived surrogate signals to model respiratory motion

Tran

Eiben

Wetscherek

et al. 2020

Biomed. Phys. Eng. Express

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An MR-Linac can provide motion information of tumour and organs-at-risk before, during, and after beam delivery. However, MR imaging cannot provide real-time high-quality volumetric images which capture breath-to-breath variability of respiratory motion. Surrogate-driven motion models relate the motion of the internal anatomy to surrogate signals, thus can estimate the 3D internal motion from these signals. Internal surrogate signals based on patient anatomy can be extracted from 2D cine-MR images, which can be acquired on an MR-Linac during treatment, to build and drive motion models. In this paper we investigate different MRI-derived surrogate signals, including signals generated by applying principal component analysis to the image intensities, or control point displacements derived from deformable registration of the 2D cine-MR images. We assessed the suitability of the signals to build models that can estimate the motion of the internal anatomy, including sliding motion and breath-to-breath variability. We quantitatively evaluated the models by estimating the 2D motion in sagittal and coronal slices of 8 lung cancer patients, and comparing them to motion measurements obtained from image registration. For sagittal slices, using the first and second principal components on the control point displacements as surrogate signals resulted in the highest model accuracy, with a mean error over patients around 0.80 mm which was lower than the in-plane resolution. For coronal slices, all investigated signals except the skin signal produced mean errors over patients around 1 mm. These results demonstrate that surrogate signals derived from 2D cine-MR images, including those generated by applying principal component analysis to the image intensities or control point displacements, can accurately model the motion of the internal anatomy within a single sagittal or coronal slice. This implies the signals should also be suitable for modelling the 3D respiratory motion of the internal anatomy.

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“…To maintain the count statistics in the reconstructed image while compensating for motion, approaches have been proposed to transform each gated image stack to a reference gate using non-rigid image registration followed by averaging [11]. The transformation field can be estimated based on 4D CT, simultaneously acquired MRI [4,12] , joint PET/MR estimation [13], or gated PET images. Alternatively, the transformation field can also be incorporated into the iterative image reconstruction framework, which is often referred to as motion compensated image reconstruction (MCIR) [4][5][6][7][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Non-Rigid Event-by-Event Continuous Respiratory Motion Compensated List-Mode Reconstruction for PET

Chan

Onofrey

Jian

et al. 2018

IEEE Trans. Med. Imaging

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Respiratory motion during positron emission tomography (PET)/computed tomography (CT) imaging can cause significant image blurring and underestimation of tracer concentration for both static and dynamic studies. In this paper, with the aim to eliminate both intra-cycle and inter-cycle motions, and apply to dynamic imaging, we developed a non-rigid event-by-event (NR-EBE) respiratory motion-compensated list-mode reconstruction algorithm. The proposed method consists of two components: the first component estimates a continuous non-rigid motion field of the internal organs using the internal-external motion correlation. This continuous motion field is then incorporated into the second component, non-rigid MOLAR (NR-MOLAR) reconstruction algorithm to deform the system matrix to the reference location where the attenuation CT is acquired. The point spread function (PSF) and time-of-flight (TOF) kernels in NR-MOLAR are incorporated in the system matrix calculation, and therefore are also deformed according to motion. We first validated NR-MOLAR using a XCAT phantom with a simulated respiratory motion. NR-EBE motion-compensated image reconstruction using both the components was then validated on three human studies injected with F-FPDTBZ and one withF-fluorodeoxyglucose (FDG) tracers. The human results were compared with conventional non-rigid motion correction using discrete motion field (NR-discrete, one motion field per gate) and a previously proposed rigid EBE motion-compensated image reconstruction (R-EBE) that was designed to correct for rigid motion on a target lesion/organ. The XCAT results demonstrated that NR-MOLAR incorporating both PSF and TOF kernels effectively corrected for non-rigid motion. The F-FPDTBZ studies showed that NR-EBE out-performed NR-Discrete, and yielded comparable results with R-EBE on target organs while yielding superior image quality in other regions. The FDG study showed that NR-EBE clearly improved the visibility of multiple moving lesions in the liver where some of them could not be discerned in other reconstructions, in addition to improving quantification. These results show that NR-EBE motion-compensated image reconstruction appears to be a promising tool for lesion detection and quantification when imaging thoracic and abdominal regions using PET.

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Joint PET-MR respiratory motion models for clinical PET motion correction

Cited by 28 publications

References 21 publications

Image quality and detectability in Siemens Biograph PET/MRI and PET/CT systems—a phantom study

Image quality and detectability in Siemens Biograph PET/MRI and PET/CT systems—a phantom study

Evaluation of MRI-derived surrogate signals to model respiratory motion

Non-Rigid Event-by-Event Continuous Respiratory Motion Compensated List-Mode Reconstruction for PET

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