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Wang J, Bermudez D, Chen W, Durgavarjhula D, Randell C, Uyanik M, McMillan A. Motion-correction strategies for enhancing whole-body PET imaging. FRONTIERS IN NUCLEAR MEDICINE (LAUSANNE, SWITZERLAND) 2024; 4:1257880. [PMID: 39118964 PMCID: PMC11308502 DOI: 10.3389/fnume.2024.1257880] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/10/2024]
Abstract
Positron Emission Tomography (PET) is a powerful medical imaging technique widely used for detection and monitoring of disease. However, PET imaging can be adversely affected by patient motion, leading to degraded image quality and diagnostic capability. Hence, motion gating schemes have been developed to monitor various motion sources including head motion, respiratory motion, and cardiac motion. The approaches for these techniques have commonly come in the form of hardware-driven gating and data-driven gating, where the distinguishing aspect is the use of external hardware to make motion measurements vs. deriving these measures from the data itself. The implementation of these techniques helps correct for motion artifacts and improves tracer uptake measurements. With the great impact that these methods have on the diagnostic and quantitative quality of PET images, much research has been performed in this area, and this paper outlines the various approaches that have been developed as applied to whole-body PET imaging.
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Affiliation(s)
- James Wang
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI, United States
| | - Dalton Bermudez
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI, United States
| | - Weijie Chen
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Electrical and Computer Engineering, University of Wisconsin Madison, Madison, WI, United States
| | - Divya Durgavarjhula
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Computer Science, University of Wisconsin Madison, Madison, WI, United States
| | - Caitlin Randell
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Biomedical Engineering, University of Wisconsin Madison, Madison, WI, United States
| | - Meltem Uyanik
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI, United States
| | - Alan McMillan
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI, United States
- Department of Electrical and Computer Engineering, University of Wisconsin Madison, Madison, WI, United States
- Department of Biomedical Engineering, University of Wisconsin Madison, Madison, WI, United States
- Data Science Institute, University of Wisconsin Madison, Madison, WI, United States
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Revilla EM, Gallezot JD, Naganawa M, Toyonaga T, Fontaine K, Mulnix T, Onofrey JA, Carson RE, Lu Y. Adaptive data-driven motion detection and optimized correction for brain PET. Neuroimage 2022; 252:119031. [PMID: 35257856 PMCID: PMC9206767 DOI: 10.1016/j.neuroimage.2022.119031] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 02/16/2022] [Accepted: 02/21/2022] [Indexed: 12/03/2022] Open
Abstract
Head motion during PET scans causes image quality degradation, decreased concentration in regions with high uptake and incorrect outcome measures from kinetic analysis of dynamic datasets. Previously, we proposed a data-driven method, center of tracer distribution (COD), to detect head motion without an external motion tracking device. There, motion was detected using one dimension of the COD trace with a semiautomatic detection algorithm, requiring multiple user defined parameters and manual intervention. In this study, we developed a new data-driven motion detection algorithm, which is automatic, self-adaptive to noise level, does not require user-defined parameters and uses all three dimensions of the COD trace (3DCOD). 3DCOD was first validated and tested using 30 simulation studies (18F-FDG, N = 15; 11C-raclopride (RAC), N = 15) with large motion. The proposed motion correction method was tested on 22 real human datasets, with 20 acquired from a high resolution research tomograph (HRRT) scanner (18F-FDG, N = 10; 11C-RAC, N = 10) and 2 acquired from the Siemens Biograph mCT scanner. Real-time hardware-based motion tracking information (Vicra) was available for all real studies and was used as the gold standard. 3DCOD was compared to Vicra, no motion correction (NMC), one-direction COD (our previous method called 1DCOD) and two conventional frame-based image registration (FIR) algorithms, i.e., FIR1 (based on predefined frames reconstructed with attenuation correction) and FIR2 (without attenuation correction) for both simulation and real studies. For the simulation studies, 3DCOD yielded -2.3 ± 1.4% (mean ± standard deviation across all subjects and 11 brain regions) error in region of interest (ROI) uptake for 18F-FDG (-3.4 ± 1.7% for 11C-RAC across all subjects and 2 regions) as compared to Vicra (perfect correction) while NMC, FIR1, FIR2 and 1DCOD yielded -25.4 ± 11.1% (-34.5 ± 16.1% for 11C- RAC), -13.4 ± 3.5% (-16.1 ± 4.6%), -5.7 ± 3.6% (-8.0 ± 4.5%) and -2.6 ± 1.5% (-5.1 ± 2.7%), respectively. For real HRRT studies, 3DCOD yielded -0.3 ± 2.8% difference for 18F-FDG (-0.4 ± 3.2% for 11C-RAC) as compared to Vicra while NMC, FIR1, FIR2 and 1DCOD yielded -14.9 ± 9.0% (-24.5 ± 14.6%), -3.6 ± 4.9% (-13.4 ± 14.3%), -0.6 ± 3.4% (-6.7 ± 5.3%) and -1.5 ± 4.2% (-2.2 ± 4.1%), respectively. In summary, the proposed motion correction method yielded comparable performance to the hardware-based motion tracking method for multiple tracers, including very challenging cases with large frequent head motion, in studies performed on a non-TOF scanner.
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Affiliation(s)
- Enette Mae Revilla
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA
| | - Jean-Dominique Gallezot
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA
| | - Mika Naganawa
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA
| | - Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA
| | - Kathryn Fontaine
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA
| | - Tim Mulnix
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA
| | - John A Onofrey
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA; Department of Urology, Yale University, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Yihuan Lu
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA.
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Einspänner E, Jochimsen TH, Harries J, Melzer A, Unger M, Brown R, Thielemans K, Sabri O, Sattler B. Evaluating different methods of MR-based motion correction in simultaneous PET/MR using a head phantom moved by a robotic system. EJNMMI Phys 2022; 9:15. [PMID: 35239047 PMCID: PMC8894542 DOI: 10.1186/s40658-022-00442-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 02/10/2022] [Indexed: 11/23/2022] Open
Abstract
Background Due to comparatively long measurement times in simultaneous positron emission tomography and magnetic resonance (PET/MR) imaging, patient movement during the measurement can be challenging. This leads to artifacts which have a negative impact on the visual assessment and quantitative validity of the image data and, in the worst case, can lead to misinterpretations. Simultaneous PET/MR systems allow the MR-based registration of movements and enable correction of the PET data. To assess the effectiveness of motion correction methods, it is necessary to carry out measurements on phantoms that are moved in a reproducible way. This study explores the possibility of using such a phantom-based setup to evaluate motion correction strategies in PET/MR of the human head. Method An MR-compatible robotic system was used to generate rigid movements of a head-like phantom. Different tools, either from the manufacturer or open-source software, were used to estimate and correct for motion based on the PET data itself (SIRF with SPM and NiftyReg) and MR data acquired simultaneously (e.g. MCLFIRT, BrainCompass). Different motion estimates were compared using data acquired during robot-induced motion. The effectiveness of motion correction of PET data was evaluated by determining the segmented volume of an activity-filled flask inside the phantom. In addition, the segmented volume was used to determine the centre-of-mass and the change in maximum activity concentration. Results The results showed a volume increase between 2.7 and 36.3% could be induced by the experimental setup depending on the motion pattern. Both, BrainCompass and MCFLIRT, produced corrected PET images, by reducing the volume increase to 0.7–4.7% (BrainCompass) and to -2.8–0.4% (MCFLIRT). The same was observed for example for the centre-of-mass, where the results show that MCFLIRT (0.2–0.6 mm after motion correction) had a smaller deviation from the reference position than BrainCompass (0.5–1.8 mm) for all displacements. Conclusions The experimental setup is suitable for the reproducible generation of movement patterns. Using open-source software for motion correction is a viable alternative to the vendor-provided motion-correction software. Supplementary Information The online version contains supplementary material available at 10.1186/s40658-022-00442-6.
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Affiliation(s)
- Eric Einspänner
- Clinic of Radiology and Nuclear Medicine, Magdeburg, Germany. .,Department of Nuclear Medicine, Leipzig University Hospital, Leipzig, Germany.
| | - Thies H Jochimsen
- Department of Nuclear Medicine, Leipzig University Hospital, Leipzig, Germany
| | - Johanna Harries
- Department of Radiation Safety and Medical Physics, Medical School Hannover, Hannover, Germany
| | - Andreas Melzer
- Innovation Center Computer Assisted Surgery (ICCAS), Faculty of Medicine, University Leipzig, Leipzig, Germany.,Institute for Medical Science and Technology IMSaT University Dundee, Dundee, UK
| | - Michael Unger
- Innovation Center Computer Assisted Surgery (ICCAS), Faculty of Medicine, University Leipzig, Leipzig, Germany
| | - Richard Brown
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, London, UK
| | - Osama Sabri
- Department of Nuclear Medicine, Leipzig University Hospital, Leipzig, Germany
| | - Bernhard Sattler
- Department of Nuclear Medicine, Leipzig University Hospital, Leipzig, Germany
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Bradley KM, Deller TW, Spangler-Bickell MG, Jansen FP, McGowan DR. A solution to PET brain motion artefact. J Neurol 2021; 268:3476-3477. [PMID: 34091746 DOI: 10.1007/s00415-021-10632-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 05/27/2021] [Accepted: 05/28/2021] [Indexed: 11/27/2022]
Affiliation(s)
- Kevin M Bradley
- Oxford University Hospitals NHS FT, Oxford, UK.,Wales Research and Diagnostic PET Imaging Centre, University Hospital of Wales, Cardiff, UK
| | | | | | | | - Daniel R McGowan
- Oxford University Hospitals NHS FT, Oxford, UK. .,Department of Oncology, University of Oxford, Oxford, UK.
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