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Pan T, Luo D. Data-driven gated positron emission tomography/computed tomography for radiotherapy. Phys Imaging Radiat Oncol 2024; 31:100601. [PMID: 39040434 PMCID: PMC11261283 DOI: 10.1016/j.phro.2024.100601] [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: 04/07/2023] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 07/24/2024] Open
Abstract
Purpose Software-based data-driven gated (DDG) positron emission tomography/computed tomography (PET/CT) has replaced hardware-based 4D PET/CT. The purpose of this article was to review DDG PET/CT, which could improve the accuracy of treatment response assessment, tumor motion evaluation, and target tumor contouring with whole-body (WB) PET/CT for radiotherapy (RT). Material and methods This review covered the topics of 4D PET/CT with hardware gating, advancements in PET instrumentation, DDG PET, DDG CT, and DDG PET/CT based on a systematic literature review. It included a discussion of the large axial field-of-view (AFOV) PET detector and a review of the clinical results of DDG PET and DDG PET/CT. Results DDG PET matched or outperformed 4D PET with hardware gating. DDG CT was more compatible with DDG PET than 4D CT, which required hardware gating. DDG CT could replace 4D CT for RT. DDG PET and DDG CT for DDG PET/CT can be incorporated in a WB PET/CT of less than 15 min scan time on a PET/CT scanner of at least 25 cm AFOV PET detector. Conclusions DDG PET/CT could correct the misregistration and tumor motion artifacts in a WB PET/CT and provide the quantitative PET and tumor motion information of a registered PET/CT for RT.
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Affiliation(s)
- Tinsu Pan
- Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, United States
| | - Dershan Luo
- Department of Radiation Physics, M.D. Anderson Cancer Center, University of Texas, United States
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Feng T, Wang J, Sun Y, Zhu W, Dong Y, Li H. Self-Gating: An Adaptive Center-of-Mass Approach for Respiratory Gating in PET. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:1140-1148. [PMID: 29727277 DOI: 10.1109/tmi.2017.2783739] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The goal is to develop an adaptive center-of-mass (COM)-based approach for device-less respiratory gating of list-mode positron emission tomography (PET) data. Our method contains two steps. The first is to automatically extract an optimized respiratory motion signal from the list-mode data during acquisition. The respiratory motion signal was calculated by tracking the location of COM within a volume of interest (VOI). The signal prominence (SP) was calculated based on Fourier analysis of the signal. The VOI was adaptively optimized to maximize SP. The second step is to automatically correct signal-flipping effects. The sign of the signal was determined based on the assumption that the average patient spends more time during expiration than inspiration. To validate our methods, thirty-one 18F-FDG patient scans were included in this paper. An external device-based signal was used as the gold standard, and the correlation coefficient of the data-driven signal with the device-based signal was measured. Our method successfully extracted respiratory signal from 30 out of 31 datasets. The failure case was due to lack of uptake in the field of view. Moreover, our sign determination method obtained correct results for all scans excluding the failure case. Quantitatively, the proposed signal extraction approach achieved a median correlation of 0.85 with the device-based signal. Gated images using optimized data-driven signal showed improved lesion contrast over static image and were comparable to those using device-based signal. We presented a new data-driven method to automatically extract respiratory motion signal from list-mode PET data by optimizing VOI for COM calculation, as well as determine motion direction from signal asymmetry. Successful application of the proposed method on most clinical datasets and comparison with device-based signal suggests its potential of serving as an alternative to external respiratory monitors.
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Hess M, Buther F, Schafers KP. Data-Driven Methods for the Determination of Anterior-Posterior Motion in PET. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:422-432. [PMID: 27662672 DOI: 10.1109/tmi.2016.2611022] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Physiological motion combined with elongated scanning times in PET leads to image degradation and quantification errors. Correction approaches usually require 1-D signals that can be obtained with hardware-based or data-driven methods. Most of the latter are optimized or limited to capture internal motion along the superior-inferior (S-I) direction. In this work we present methods for also extracting anterior-posterior (A-P) motion from PET data and propose a set of novel weighting mechanisms that can be used to emphasize certain lines-of-response (LORs) for an increased sensitivity and better signal-to-noise ratio (SNR). The proper functioning of the methods was verified in a phantom experiment. Further, their application to clinical [18F]-FDG-PET data of 72 patients revealed that using the weighting mechanisms leads to signals with significantly higher spectral respiratory weights, i.e. signals with higher quality. Information about multi-dimensional motion is contained in PET data and can be derived with data-driven methods. Motion models or correction techniques such as respiratory gating might benefit from the proposed methods as they allow to describe the three-dimensional movements of PET-positive structures more precisely.
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Salavati A, Borofsky S, Boon-Keng TK, Houshmand S, Khiewvan B, Saboury B, Codreanu I, Torigian DA, Zaidi H, Alavi A. Application of partial volume effect correction and 4D PET in the quantification of FDG avid lung lesions. Mol Imaging Biol 2015; 17:140-8. [PMID: 25080325 DOI: 10.1007/s11307-014-0776-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
PURPOSE The aim of this study is to assess a software-based method with semiautomated correction for partial volume effect (PVE) to quantify the metabolic activity of pulmonary malignancies in patients who underwent non-gated and respiratory-gated 2-deoxy-2-[(18)F]fluoro-D-glucose (FDG)-positron emission tomography (PET)/x-ray computed tomography(CT). PROCEDURES The study included 106 lesions of 55 lung cancer patients who underwent respiratory-gated FDG-PET/CT for radiation therapy treatment planning. Volumetric PET/CT parameters were determined by using 4D PET/CT and non-gated PET/CT images. We used a semiautomated program employing an adaptive contrast-oriented thresholding algorithm for lesion delineation as well as a lesion-based partial volume effect correction algorithm. We compared respiratory-gated parameters with non-gated parameters by using pairwise comparison and interclass correlation coefficient assessment. In a multivariable regression analysis, we also examined factors, which can affect quantification accuracy, including the size of lesion and the location of tumor. RESULTS This study showed that quantification of volumetric parameters of 4D PET/CT images using an adaptive contrast-oriented thresholding algorithm and 3D lesion-based partial volume correction is feasible. We observed slight increase in FDG uptake by using PET/CT volumetric parameters in comparison of highest respiratory-gated values with non-gated values. After correction for partial volume effect, the mean standardized uptake value (SUVmean) and total lesion glycolysis (TLG) increased substantially (p value <0.001). However, we did not observe a clinically significant difference between partial volume corrected parameters of respiratory-gated and non-gated PET/CT scans. Regression analysis showed that tumor volume was the main predictor of quantification inaccuracy caused by partial volume effect. CONCLUSIONS Based on this study, assessment of volumetric PET/CT parameters and partial volume effect correction for accurate quantification of lung malignant lesions by using respiratory non-gated PET images are feasible and it is comparable to gated measurements. Partial volume correction increased both the respiratory-gated and non-gated values significantly and appears to be the dominant source of quantification error of lung lesions.
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Affiliation(s)
- Ali Salavati
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, USA
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Kesner AL, Schleyer PJ, Büther F, Walter MA, Schäfers KP, Koo PJ. On transcending the impasse of respiratory motion correction applications in routine clinical imaging - a consideration of a fully automated data driven motion control framework. EJNMMI Phys 2014; 1:8. [PMID: 26501450 PMCID: PMC4673082 DOI: 10.1186/2197-7364-1-8] [Citation(s) in RCA: 54] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Accepted: 05/23/2014] [Indexed: 12/21/2022] Open
Abstract
Positron emission tomography (PET) is increasingly used for the detection, characterization, and follow-up of tumors located in the thorax. However, patient respiratory motion presents a unique limitation that hinders the application of high-resolution PET technology for this type of imaging. Efforts to transcend this limitation have been underway for more than a decade, yet PET remains for practical considerations a modality vulnerable to motion-induced image degradation. Respiratory motion control is not employed in routine clinical operations. In this article, we take an opportunity to highlight some of the recent advancements in data-driven motion control strategies and how they may form an underpinning for what we are presenting as a fully automated data-driven motion control framework. This framework represents an alternative direction for future endeavors in motion control and can conceptually connect individual focused studies with a strategy for addressing big picture challenges and goals.
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Affiliation(s)
- Adam L Kesner
- Division of Nuclear Medicine, Department of Radiology, Anschutz Medical Campus, University of Colorado Denver, 12700 E 19th Ave, Box C-278, Aurora, CO, 80045, USA.
| | - Paul J Schleyer
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, WC2R 2LS, UK.
| | - Florian Büther
- European Institute for Molecular Imaging, University of Münster, Münster, 48149, Germany.
| | - Martin A Walter
- Institute of Nuclear Medicine and Department of Clinical Research, University Hospital Bern, Bern, 3010, Switzerland.
| | - Klaus P Schäfers
- European Institute for Molecular Imaging, University of Münster, Münster, 48149, Germany.
| | - Phillip J Koo
- Division of Nuclear Medicine, Department of Radiology, Anschutz Medical Campus, University of Colorado Denver, 12700 E 19th Ave, Box C-278, Aurora, CO, 80045, USA.
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Yang J, Yamamoto T, Mazin SR, Graves EE, Keall PJ. The potential of positron emission tomography for intratreatment dynamic lung tumor tracking: a phantom study. Med Phys 2014; 41:021718. [PMID: 24506609 DOI: 10.1118/1.4861816] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
PURPOSE This study aims to evaluate the potential and feasibility of positron emission tomography for dynamic lung tumor tracking during radiation treatment. The authors propose a center of mass (CoM) tumor tracking algorithm using gated-PET images combined with a respiratory monitor and investigate the geometric accuracy of the proposed algorithm. METHODS The proposed PET dynamic lung tumor tracking algorithm estimated the target position information through the CoM of the segmented target volume on gated PET images reconstructed from accumulated coincidence events. The information was continuously updated throughout a scan based on the assumption that real-time processing was supported (actual processing time at each frame ≈ 10 s). External respiratory motion and list-mode PET data were acquired from a phantom programmed to move with measured respiratory traces (external respiratory motion and internal target motion) from human subjects, for which the ground truth target position was known as a function of time. The phantom was cylindrical with six hollow sphere targets (10, 13, 17, 22, 28, and 37 mm in diameter). The measured respiratory traces consisted of two sets: (1) 1D-measured motion from ten healthy volunteers and (2) 3D-measured motion from four lung cancer patients. The authors evaluated the geometric accuracy of the proposed algorithm by quantifying estimation errors (Euclidean distance) between the actual motion of targets (1D-motion and 3D-motion traces) and CoM trajectories estimated by the proposed algorithm as a function of time. RESULTS The time-averaged error of 1D-motion traces over all trajectories of all targets was 1.6 mm. The error trajectories decreased with time as coincidence events were accumulated. The overall error trajectory of 1D-motion traces converged to within 2 mm in approximately 90 s. As expected, more accurate results were obtained for larger targets. For example, for the 37 mm target, the average error over all 1D-motion traces was 1.1 mm; and for the 10 mm target, the average error over all 1D-motion traces was 2.8 mm. The overall time-averaged error of 3D-motion traces was 1.6 mm, which was comparable to that of the 1D-motion traces. There were small variations in the errors between the 3D-motion traces, although the motion trajectories were very different. The accuracy of the estimates was consistent for all targets except for the smallest. CONCLUSIONS The authors developed an algorithm for dynamic lung tumor tracking using list-mode PET data and a respiratory motion signal, and demonstrated proof-of-principle for PET-guided lung tumor tracking. The overall tracking error in phantom studies is less than 2 mm.
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Affiliation(s)
- Jaewon Yang
- Department of Electrical Engineering, Stanford University, Stanford, California 94305 and Department of Radiation Oncology, Stanford University, Stanford, California 94305
| | - Tokihiro Yamamoto
- Department of Radiation Oncology, University of California Davis, Sacramento, California 95817
| | | | - Edward E Graves
- Department of Radiation Oncology, Stanford University, Stanford, California 94305
| | - Paul J Keall
- Radiation Physics Laboratory, University of Sydney, Sydney, NSW 2006, Australia
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Büther F, Ernst I, Hamill J, Eich HT, Schober O, Schäfers M, Schäfers KP. External radioactive markers for PET data-driven respiratory gating in positron emission tomography. Eur J Nucl Med Mol Imaging 2012; 40:602-14. [PMID: 23238525 DOI: 10.1007/s00259-012-2313-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2012] [Accepted: 11/23/2012] [Indexed: 11/24/2022]
Abstract
PURPOSE Respiratory gating is an established approach to overcoming respiration-induced image artefacts in PET. Of special interest in this respect are raw PET data-driven gating methods which do not require additional hardware to acquire respiratory signals during the scan. However, these methods rely heavily on the quality of the acquired PET data (statistical properties, data contrast, etc.). We therefore combined external radioactive markers with data-driven respiratory gating in PET/CT. The feasibility and accuracy of this approach was studied for [(18)F]FDG PET/CT imaging in patients with malignant liver and lung lesions. METHODS PET data from 30 patients with abdominal or thoracic [(18)F]FDG-positive lesions (primary tumours or metastases) were included in this prospective study. The patients underwent a 10-min list-mode PET scan with a single bed position following a standard clinical whole-body [(18)F]FDG PET/CT scan. During this scan, one to three radioactive point sources (either (22)Na or (18)F, 50-100 kBq) in a dedicated holder were attached the patient's abdomen. The list mode data acquired were retrospectively analysed for respiratory signals using established data-driven gating approaches and additionally by tracking the motion of the point sources in sinogram space. Gated reconstructions were examined qualitatively, in terms of the amount of respiratory displacement and in respect of changes in local image intensity in the gated images. RESULTS The presence of the external markers did not affect whole-body PET/CT image quality. Tracking of the markers led to characteristic respiratory curves in all patients. Applying these curves for gated reconstructions resulted in images in which motion was well resolved. Quantitatively, the performance of the external marker-based approach was similar to that of the best intrinsic data-driven methods. Overall, the gain in measured tumour uptake from the nongated to the gated images indicating successful removal of respiratory motion was correlated with the magnitude of the respiratory displacement of the respective tumour lesion, but not with lesion size. CONCLUSION Respiratory information can be assessed from list-mode PET/CT through PET data-derived tracking of external radioactive markers. This information can be successfully applied to respiratory gating to reduce motion-related image blurring. In contrast to other previously described PET data-driven approaches, the external marker approach is independent of tumour uptake and thereby applicable even in patients with poor uptake and small tumours.
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Affiliation(s)
- Florian Büther
- European Institute for Molecular Imaging, University of Münster, Münster, Germany.
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Yang J, Yamamoto T, Cho B, Seo Y, Keall PJ. The impact of audio-visual biofeedback on 4D PET images: results of a phantom study. Med Phys 2012; 39:1046-57. [PMID: 22320815 DOI: 10.1118/1.3679012] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Irregular breathing causes motion blurring artifacts in 4D PET images. Audiovisual (AV) biofeedback has been demonstrated to improve breathing regularity. To investigate the hypothesis that, compared with free breathing, motion blurring artifacts are reduced with AV biofeedback, the authors performed the first experimental phantom-based quantification of the impact of AV biofeedback on 4D PET image quality. METHODS The authors acquired 4D PET dynamic phantom images with AV biofeedback and free breathing by moving a phantom programmed with AV biofeedback trained and free breathing respiratory traces of ten healthy subjects. The authors also acquired stationary phantom images for reference. The phantom was cylindrical with six hollow sphere targets (10, 13, 17, 22, 28, and 37 mm in diameter). The authors quantified motion blurring using the target diameter, Dice coefficient and recovery coefficient (RC) metrics to estimate the effect of motion. RESULTS The average increase in target diameter for AV biofeedback was 0.6±1.6mm (4.7±13%), which was significantly (p<0.001) smaller than for free breathing 1.3±2.2mm (9.1±19%). The average Dice coefficient for AV biofeedback was 0.90±0.07, which was significantly (p<0.001) larger than for free breathing (0.88±0.10). The RCs for AV biofeedback were consistently higher than those for free breathing and comparable to those for stationary targets. However, for RCs the impact of target sizes was more dominant than that of motion. In addition, the authors observed large variations in the results with respect to target sizes, subject traces and respiratory bins due to partial volume effects and respiratory motion irregularity. CONCLUSIONS The results indicate that AV biofeedback can significantly reduce motion blurring artifacts and may facilitate improved identification and localization of lung tumors in 4D PET images. The results justify proceeding with clinical studies to quantify the impact of AV biofeedback on 4D PET image quality and tumor detectability.
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Affiliation(s)
- Jaewon Yang
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
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Kesner AL, Kuntner C. A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods. Med Phys 2010; 37:5550-9. [PMID: 21089790 DOI: 10.1118/1.3483784] [Citation(s) in RCA: 65] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
PURPOSE Respiratory gating in PET is an approach used to minimize the negative effects of respiratory motion on spatial resolution. It is based on an initial determination of a patient's respiratory movements during a scan, typically using hardware based systems. In recent years, several fully automated databased algorithms have been presented for extracting a respiratory signal directly from PET data, providing a very practical strategy for implementing gating in the clinic. In this work, a new method is presented for extracting a respiratory signal from raw PET sinogram data and compared to previously presented automated techniques. METHODS The acquisition of respiratory signal from PET data in the newly proposed method is based on rebinning the sinogram data into smaller data structures and then analyzing the time activity behavior in the elements of these structures. From this analysis, a 1D respiratory trace is produced, analogous to a hardware derived respiratory trace. To assess the accuracy of this fully automated method, respiratory signal was extracted from a collection of 22 clinical FDG-PET scans using this method, and compared to signal derived from several other software based methods as well as a signal derived from a hardware system. RESULTS The method presented required approximately 9 min of processing time for each 10 min scan (using a single 2.67 GHz processor), which in theory can be accomplished while the scan is being acquired and therefore allowing a real-time respiratory signal acquisition. Using the mean correlation between the software based and hardware based respiratory traces, the optimal parameters were determined for the presented algorithm. The mean/median/range of correlations for the set of scans when using the optimal parameters was found to be 0.58/0.68/0.07-0.86. The speed of this method was within the range of real-time while the accuracy surpassed the most accurate of the previously presented algorithms. CONCLUSIONS PET data inherently contains information about patient motion; information that is not currently being utilized. We have shown that a respiratory signal can be extracted from raw PET data in potentially real-time and in a fully automated manner. This signal correlates well with hardware based signal for a large percentage of scans, and avoids the efforts and complications associated with hardware. The proposed method to extract a respiratory signal can be implemented on existing scanners and, if properly integrated, can be applied without changes to routine clinical procedures.
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