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Chemli Y, Tétrault MA, Marin T, Normandin MD, Bloch I, El Fakhri G, Ouyang J, Petibon Y. Super-resolution in brain positron emission tomography using a real-time motion capture system. Neuroimage 2023; 272:120056. [PMID: 36977452 PMCID: PMC10122782 DOI: 10.1016/j.neuroimage.2023.120056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/28/2023] [Accepted: 03/25/2023] [Indexed: 03/29/2023] Open
Abstract
Super-resolution (SR) is a methodology that seeks to improve image resolution by exploiting the increased spatial sampling information obtained from multiple acquisitions of the same target with accurately known sub-resolution shifts. This work aims to develop and evaluate an SR estimation framework for brain positron emission tomography (PET), taking advantage of a high-resolution infra-red tracking camera to measure shifts precisely and continuously. Moving phantoms and non-human primate (NHP) experiments were performed on a GE Discovery MI PET/CT scanner (GE Healthcare) using an NDI Polaris Vega (Northern Digital Inc), an external optical motion tracking device. To enable SR, a robust temporal and spatial calibration of the two devices was developed as well as a list-mode Ordered Subset Expectation Maximization PET reconstruction algorithm, incorporating the high-resolution tracking data from the Polaris Vega to correct motion for measured line of responses on an event-by-event basis. For both phantoms and NHP studies, the SR reconstruction method yielded PET images with visibly increased spatial resolution compared to standard static acquisitions, allowing improved visualization of small structures. Quantitative analysis in terms of SSIM, CNR and line profiles were conducted and validated our observations. The results demonstrate that SR can be achieved in brain PET by measuring target motion in real-time using a high-resolution infrared tracking camera.
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Affiliation(s)
- Yanis Chemli
- Gordon Center for Medical Imaging, Department of Radiology Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; LTCI, Télécom Paris, Institut Polytechnique de Paris, France.
| | - Marc-André Tétrault
- Department of Computer Engineering, Université de Sherbrooke, Sherbrooke, QC, Canada.
| | - Thibault Marin
- Gordon Center for Medical Imaging, Department of Radiology Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
| | - Marc D Normandin
- Gordon Center for Medical Imaging, Department of Radiology Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
| | - Isabelle Bloch
- Sorbonne Université, CNRS, LIP6, Paris, France; LTCI, Télécom Paris, Institut Polytechnique de Paris, France.
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Department of Radiology Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
| | - Jinsong Ouyang
- Gordon Center for Medical Imaging, Department of Radiology Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
| | - Yoann Petibon
- Gordon Center for Medical Imaging, Department of Radiology Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
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Miranda A, Bertoglio D, Stroobants S, Staelens S, Verhaeghe J. Spatiotemporal Kernel Reconstruction for Linear Parametric Neurotransmitter PET Kinetic Modeling in Motion Correction Brain PET of Awake Rats. Front Neurosci 2022; 16:901091. [PMID: 35645721 PMCID: PMC9133502 DOI: 10.3389/fnins.2022.901091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
The linear parametric neurotransmitter positron emission tomography (lp-ntPET) kinetic model can be used to detect transient changes (activation) in endogenous neurotransmitter levels. Preclinical PET scans in awake animals can be performed to investigate neurotransmitter transient changes. Here we use the spatiotemporal kernel reconstruction (Kernel) for noise reduction in dynamic PET, and lp-ntPET kinetic modeling. Kernel is adapted for motion correction reconstruction, applied in awake rat PET scans. We performed 2D rat brain phantom simulation using the ntPET model at 3 different noise levels. Data was reconstructed with independent frame reconstruction (IFR), IFR with HYPR denoising, and Kernel, and lp-ntPET kinetic parameters (k 2a : efflux rate, γ: activation magnitude, t d : activation onset time, and t p : activation peak time) were calculated. Additionally, significant activation magnitude (γ) difference with respect to a region with no activation (rest) was calculated. Finally, [11C]raclopride experiments were performed in anesthetized and awake rats, injecting cold raclopride at 20 min after scan start to simulate endogenous neurotransmitter release. For simulated data at the regional level, IFR coefficient of variation (COV) of k 2a , γ, t d and t p was reduced with HYPR denoising, but Kernel showed the lowest COV (2 fold reduction compared with IFR). At the pixel level the same trend is observed for k 2a , γ, t d and t p COV, but reduction is larger with Kernel compared with IFR (10-14 fold). Bias in γ with respect with noise-free values was additionally reduced using Kernel (difference of 292, 72.4, and -6.92% for IFR, IFR+KYPR, and Kernel, respectively). Significant difference in activation between the rest and active region could be detected at a simulated activation of 160% for IFR and IFR+HYPR, and of 120% for Kernel. In rat experiments, lp-ntPET parameters have better confidence intervals using Kernel. In the γ, and t d parametric maps, the striatum structure can be identified with Kernel but not with IFR. Striatum voxel-wise γ, t d and t p values have lower variability using Kernel compared with IFR and IFR+HYPR. The spatiotemporal kernel reconstruction adapted for motion correction reconstruction allows to improve lp-ntPET kinetic modeling noise in awake rat studies, as well as detection of subtle neurotransmitter activations.
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Affiliation(s)
- Alan Miranda
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Daniele Bertoglio
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Sigrid Stroobants
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
- Department of Nuclear Medicine, University Hospital Antwerp, Antwerp, Belgium
| | - Steven Staelens
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Jeroen Verhaeghe
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
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Sun T, Wu Y, Bai Y, Wang Z, Shen C, Wang W, Li C, Hu Z, Liang D, Liu X, Zheng H, Yang Y, Wang M. An iterative image-based inter-frame motion compensation method for dynamic brain PET imaging. Phys Med Biol 2022; 67. [PMID: 35021156 DOI: 10.1088/1361-6560/ac4a8f] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 01/12/2022] [Indexed: 11/11/2022]
Abstract
As a non-invasive imaging tool, positron emission tomography (PET) plays an important role in brain science and disease research. Dynamic acquisition is one way of brain PET imaging. Its wide application in clinical research has often been hindered by practical challenges, such as patient involuntary movement, which could degrade both image quality and the accuracy of the quantification. This is even more obvious in scans of patients with neurodegeneration or mental disorders. Conventional motion compensation methods were either based on images or raw measured data, were shown to be able to reduce the effect of motion on the image quality. As for a dynamic PET scan, motion compensation can be challenging as tracer kinetics and relatively high noise can be present in dynamic frames. In this work, we propose an image-based inter-frame motion compensation approach specifically designed for dynamic brain PET imaging. Our method has an iterative implementation that only requires reconstructed images, based on which the inter-frame subject movement can be estimated and compensated. The method utilized tracer-specific kinetic modelling and can deal with simple and complex movement patterns. The synthesized phantom study showed that the proposed method can compensate for the simulated motion in scans with18F-FDG,18F-Fallypride and18F-AV45. Fifteen dynamic18F-FDG patient scans with motion artifacts were also processed. The quality of the recovered image was superior to the one of the non-corrected images and the corrected images with other image-based methods. The proposed method enables retrospective image quality control for dynamic brain PET imaging, hence facilitating the applications of dynamic PET in clinics and research.
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Affiliation(s)
- Tao Sun
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, People's Republic of China
| | - Yaping Wu
- Henan Provincial People's Hospital and the People's Hospital of Zhengzhou, University of Zhengzhou, People's Republic of China
| | - Yan Bai
- Henan Provincial People's Hospital and the People's Hospital of Zhengzhou, University of Zhengzhou, People's Republic of China
| | - Zhenguo Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, People's Republic of China
| | - Chushu Shen
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, People's Republic of China
| | - Wei Wang
- United Imaging Healthcare, Shanghai, People's Republic of China
| | - Chenwei Li
- United Imaging Healthcare, Shanghai, People's Republic of China
| | - Zhanli Hu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, People's Republic of China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, People's Republic of China
| | - Xin Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, People's Republic of China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, People's Republic of China
| | - Yongfeng Yang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, People's Republic of China
| | - Meiyun Wang
- Henan Provincial People's Hospital and the People's Hospital of Zhengzhou, University of Zhengzhou, People's Republic of China
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Miranda A, Bertoglio D, Stroobants S, Staelens S, Verhaeghe J. Translation of Preclinical PET Imaging Findings: Challenges and Motion Correction to Overcome the Confounding Effect of Anesthetics. Front Med (Lausanne) 2021; 8:753977. [PMID: 34746189 PMCID: PMC8569248 DOI: 10.3389/fmed.2021.753977] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 09/27/2021] [Indexed: 11/13/2022] Open
Abstract
Preclinical brain positron emission tomography (PET) in animals is performed using anesthesia to avoid movement during the PET scan. In contrast, brain PET scans in humans are typically performed in the awake subject. Anesthesia is therefore one of the principal limitations in the translation of preclinical brain PET to the clinic. This review summarizes the available literature supporting the confounding effect of anesthesia on several PET tracers for neuroscience in preclinical small animal scans. In a second part, we present the state-of-the-art methodologies to circumvent this limitation to increase the translational significance of preclinical research, with an emphasis on motion correction methods. Several motion tracking systems compatible with preclinical scanners have been developed, each one with its advantages and limitations. These systems and the novel experimental setups they can bring to preclinical brain PET research are reviewed here. While technical advances have been made in this field, and practical implementations have been demonstrated, the technique should become more readily available to research centers to allow for a wider adoption of the motion correction technique for brain research.
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Affiliation(s)
- Alan Miranda
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Daniele Bertoglio
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Sigrid Stroobants
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
- University Hospital Antwerp, Antwerp, Belgium
| | - Steven Staelens
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Jeroen Verhaeghe
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
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Kyme AZ, Fulton RR. Motion estimation and correction in SPECT, PET and CT. Phys Med Biol 2021; 66. [PMID: 34102630 DOI: 10.1088/1361-6560/ac093b] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 06/08/2021] [Indexed: 11/11/2022]
Abstract
Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and X-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion, but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art machine learning methods may have a unique role to play in this context.
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Affiliation(s)
- Andre Z Kyme
- School of Biomedical Engineering, The University of Sydney, Sydney, New South Wales, AUSTRALIA
| | - Roger R Fulton
- Sydney School of Health Sciences, The University of Sydney, Sydney, New South Wales, AUSTRALIA
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6
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Miranda A, Staelens S, Stroobants S, Verhaeghe J. Motion Dependent and Spatially Variant Resolution Modeling for PET Rigid Motion Correction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2518-2530. [PMID: 32070945 DOI: 10.1109/tmi.2019.2962237] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Recent advances in positron emission tomography (PET) have allowed to perform brain scans of freely moving animals by using rigid motion correction. One of the current challenges in these scans is that, due to the PET scanner spatially variant point spread function (SVPSF), motion corrected images have a motion dependent blurring since animals can move throughout the entire field of view (FOV). We developed a method to calculate the image-based resolution kernels of the motion dependent and spatially variant PSF (MD-SVPSF) to correct the loss of spatial resolution in motion corrected reconstructions. The resolution kernels are calculated for each voxel by sampling and averaging the SVPSF at all positions in the scanner FOV where the moving object was measured. In resolution phantom scans, the use of the MD-SVPSF resolution model improved the spatial resolution in motion corrected reconstructions and corrected the image deformation caused by the parallax effect consistently for all motion patterns, outperforming the use of a motion independent SVPSF or Gaussian kernels. Compared to motion correction in which the SVPSF is applied independently for every pose, our method performed similarly, but with more than two orders of magnitude faster computation time. Importantly, in scans of freely moving mice, brain regional quantification in motion-free and motion corrected images was better correlated when using the MD-SVPSF in comparison with motion independent SVPSF and a Gaussian kernel. The method developed here allows to obtain consistent spatial resolution and quantification in motion corrected images, independently of the motion pattern of the subject.
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Sun T, Petibon Y, Han PK, Ma C, Kim SJW, Alpert NM, El Fakhri G, Ouyang J. Body motion detection and correction in cardiac PET: Phantom and human studies. Med Phys 2019; 46:4898-4906. [PMID: 31508827 DOI: 10.1002/mp.13815] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 08/28/2019] [Accepted: 08/28/2019] [Indexed: 12/14/2022] Open
Abstract
PURPOSE Patient body motion during a cardiac positron emission tomography (PET) scan can severely degrade image quality. We propose and evaluate a novel method to detect, estimate, and correct body motion in cardiac PET. METHODS Our method consists of three key components: motion detection, motion estimation, and motion-compensated image reconstruction. For motion detection, we first divide PET list-mode data into 1-s bins and compute the center of mass (COM) of the coincidences' distribution in each bin. We then compute the covariance matrix within a 25-s sliding window over the COM signals inside the window. The sum of the eigenvalues of the covariance matrix is used to separate the list-mode data into "static" (i.e., body motion free) and "moving" (i.e. contaminated by body motion) frames. Each moving frame is further divided into a number of evenly spaced sub-frames (referred to as "sub-moving" frames), in which motion is assumed to be negligible. For motion estimation, we first reconstruct the data in each static and sub-moving frame using a rapid back-projection technique. We then select the longest static frame as the reference frame and estimate elastic motion transformations to the reference frame from all other static and sub-moving frames using nonrigid registration. For motion-compensated image reconstruction, we reconstruct all the list-mode data into a single image volume in the reference frame by incorporating the estimated motion transformations in the PET system matrix. We evaluated the performance of our approach in both phantom and human studies. RESULTS Visually, the motion-corrected (MC) PET images obtained using the proposed method have better quality and fewer motion artifacts than the images reconstructed without motion correction (NMC). Quantitative analysis indicates that MC yields higher myocardium to blood pool concentration ratios. MC also yields sharper myocardium than NMC. CONCLUSIONS The proposed body motion correction method improves image quality of cardiac PET.
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Affiliation(s)
- Tao Sun
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Yoann Petibon
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Paul K Han
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Chao Ma
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Sally J W Kim
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Nathaniel M Alpert
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Jinsong Ouyang
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
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Slipsager JM, Ellegaard AH, Glimberg SL, Paulsen RR, Tisdall MD, Wighton P, van der Kouwe A, Marner L, Henriksen OM, Law I, Olesen OV. Markerless motion tracking and correction for PET, MRI, and simultaneous PET/MRI. PLoS One 2019; 14:e0215524. [PMID: 31002725 PMCID: PMC6474595 DOI: 10.1371/journal.pone.0215524] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 04/03/2019] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE We demonstrate and evaluate the first markerless motion tracker compatible with PET, MRI, and simultaneous PET/MRI systems for motion correction (MC) of brain imaging. METHODS PET and MRI compatibility is achieved by careful positioning of in-bore vision extenders and by placing all electronic components out-of-bore. The motion tracker is demonstrated in a clinical setup during a pediatric PET/MRI study including 94 pediatric patient scans. PET MC is presented for two of these scans using a customized version of the Multiple Acquisition Frame method. Prospective MC of MRI acquisition of two healthy subjects is demonstrated using a motion-aware MRI sequence. Real-time motion estimates are accompanied with a tracking validity parameter to improve tracking reliability. RESULTS For both modalities, MC shows that motion induced artifacts are noticeably reduced and that motion estimates are sufficiently accurate to capture motion ranging from small respiratory motion to large intentional motion. In the PET/MRI study, a time-activity curve analysis shows image improvements for a patient performing head movements corresponding to a tumor motion of ±5-10 mm with a 19% maximal difference in standardized uptake value before and after MC. CONCLUSION The first markerless motion tracker is successfully demonstrated for prospective MC in MRI and MC in PET with good tracking validity. SIGNIFICANCE As simultaneous PET/MRI systems have become available for clinical use, an increasing demand for accurate motion tracking and MC in PET/MRI scans has emerged. The presented markerless motion tracker facilitate this demand.
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Affiliation(s)
- Jakob M. Slipsager
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- TracInnovations, Ballerup, Denmark
| | - Andreas H. Ellegaard
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | | | - Rasmus R. Paulsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - M. Dylan Tisdall
- Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Paul Wighton
- Athinoula. A. Matinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - André van der Kouwe
- Athinoula. A. Matinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Lisbeth Marner
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Otto M. Henriksen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Oline V. Olesen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- TracInnovations, Ballerup, Denmark
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Estimation of and correction for finite motion sampling errors in small animal PET rigid motion correction. Med Biol Eng Comput 2018; 57:505-518. [PMID: 30242596 PMCID: PMC6347657 DOI: 10.1007/s11517-018-1899-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Accepted: 09/14/2018] [Indexed: 11/23/2022]
Abstract
Motion tracking with finite time sampling causing an associated unknown residual motion between two motion measurements is one of the factors contributing to resolution loss in small animal PET motion correction. The aim of this work is (i) to provide a means to estimate the effect of the finite motion sampling on the spatial resolution of the motion correction reconstructions and (ii) to correct for this residual motion thereby minimizing resolution loss. We calculate a tailored spatially variant deconvolution kernel from the measured motion data which is then used to deconvolve the motion corrected image using a 3D Richardson-Lucy algorithm. A simulation experiment of numerical phantoms as well as a microDerenzo phantom experiment wherein the phantom was manually moved at different speeds was performed to assess the performance of our proposed method. In the motion corrected images of the microDerenzo phantom there was an average rod FWHM differences between the slow and fast motion cases of 9.7%. This difference was reduced to 5.8% after applying the residual motion deconvolution. In awake animal experiments, the proposed method can serve to mitigate the finite sampling factor degrading the spatial resolution as well as the resolution differences between fast-moving and slow-moving animals. Motion correction of positron emission tomography (PET) scans of moving subjects can be performed by measuring the motion of the subject during the PET scan with an optical tracking camera. The motion tracking data obtained from the tracking camera is then used to correct the PET image reconstructions for motion. Due to finite time sampling of the motion data, the motion corrected reconstructions suffer from loss of spatial resolution. In the proposed method, a spatially variant deconvolution kernel is calculated from the motion tracking data, which is then used to correct the motion-corrected PET reconstructions for the blurring effect of the finite motion sampling through a Richardson-Lucy deconvolution. ![]()
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Angelis GI, Gillam JE, Kyme AZ, Fulton RR, Meikle SR. Image-based modelling of residual blurring in motion corrected small animal PET imaging using motion dependent point spread functions. Biomed Phys Eng Express 2018. [DOI: 10.1088/2057-1976/aab922] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Miranda A, Staelens S, Stroobants S, Verhaeghe J. Fast and Accurate Rat Head Motion Tracking With Point Sources for Awake Brain PET. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1573-1582. [PMID: 28207390 DOI: 10.1109/tmi.2017.2667889] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
To avoid the confounding effects of anesthesia and immobilization stress in rat brain positron emission tomography (PET), motion tracking-based unrestrained awake rat brain imaging is being developed. In this paper, we propose a fast and accurate rat headmotion tracking method based on small PET point sources. PET point sources (3-4) attached to the rat's head are tracked in image space using 15-32-ms time frames. Our point source tracking (PST) method was validated using a manually moved microDerenzo phantom that was simultaneously tracked with an optical tracker (OT) for comparison. The PST method was further validated in three awake [18F]FDG rat brain scans. Compared with the OT, the PST-based correction at the same frame rate (31.2 Hz) reduced the reconstructed FWHM by 0.39-0.66 mm for the different tested rod sizes of the microDerenzo phantom. The FWHM could be further reduced by another 0.07-0.13 mm when increasing the PST frame rate (66.7 Hz). Regional brain [18F]FDG uptake in the motion corrected scan was strongly correlated ( ) with that of the anesthetized reference scan for all three cases ( ). The proposed PST method allowed excellent and reproducible motion correction in awake in vivo experiments. In addition, there is no need of specialized tracking equipment or additional calibrations to be performed, the point sources are practically imperceptible to the rat, and PST is ideally suitable for small bore scanners, where optical tracking might be challenging.
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12
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Spangler-Bickell MG, de Laat B, Fulton R, Bormans G, Nuyts J. The effect of isoflurane on 18F-FDG uptake in the rat brain: a fully conscious dynamic PET study using motion compensation. EJNMMI Res 2016; 6:86. [PMID: 27888500 PMCID: PMC5124015 DOI: 10.1186/s13550-016-0242-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Accepted: 11/17/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In preclinical positron emission tomography (PET) studies an anaesthetic is used to ensure that the animal does not move during the scan. However, anaesthesia may have confounding effects on the drug or tracer kinetics under study, and the nature of these effects is usually not known. METHOD We have implemented a protocol for tracking the rigid motion of the head of a fully conscious rat during a PET scan and performing a motion compensated list-mode reconstruction of the data. Using this technique we have conducted eight rat studies to investigate the effect of isoflurane on the uptake of 18F-FDG in the brain, by comparing conscious and unconscious scans. RESULTS Our results indicate that isoflurane significantly decreases the whole brain uptake, as well as decreasing the relative regional FDG uptake in the cortex, diencephalon, and inferior colliculi, while increasing it in the vestibular nuclei. No statistically significant changes in FDG uptake were observed in the cerebellum and striata. CONCLUSION The applied event-based motion compensation technique allowed for the investigation of the effect of isoflurane on FDG uptake in the rat brain using fully awake and unrestrained rats, scanned dynamically from the moment of injection. A significant effect of the anaesthesia was observed in various regions of the brain.
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Affiliation(s)
- Matthew G Spangler-Bickell
- Department of Imaging and Pathology, KU Leuven - University of Leuven, Nuclear Medicine & Molecular Imaging, Medical Imaging Research Center (MIRC), Leuven, Belgium.
| | - Bart de Laat
- Department of Imaging and Pathology, KU Leuven - University of Leuven, Nuclear Medicine & Molecular Imaging, Medical Imaging Research Center (MIRC), Leuven, Belgium
| | - Roger Fulton
- Brain & Mind Centre and the Faculty of Health Sciences, University of Sydney, Sydney, Australia.,Department of Nuclear Medicine, Westmead Hospital, Sydney, Australia
| | - Guy Bormans
- Department of Radiopharmacy, KU Leuven, Leuven, Belgium
| | - Johan Nuyts
- Department of Imaging and Pathology, KU Leuven - University of Leuven, Nuclear Medicine & Molecular Imaging, Medical Imaging Research Center (MIRC), Leuven, Belgium
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