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Westlund Gotby LEL, Lobeek D, Roosen J, de Bakker M, Konijnenberg MW, Nijsen JFW. Accuracy of holmium-166 SPECT/CT quantification over a large range of activities. EJNMMI Phys 2024; 11:78. [PMID: 39325204 PMCID: PMC11427639 DOI: 10.1186/s40658-024-00683-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Accepted: 09/04/2024] [Indexed: 09/27/2024] Open
Abstract
BACKGROUND Quantitative imaging is a crucial step for dosimetry in radionuclide therapies. Traditionally, SPECT/CT imaging is quantified based on scanner-specific conversion factors or self-calibration, but recently absolute quantification methods have been introduced in commercial SPECT reconstruction software (Broad Quantification, Siemens Healthineers). In this phantom study we investigate the accuracy of three quantification methods for holmium-166 SPECT/CT imaging, and provide recommendations for clinical dosimetry. METHODS One cylindrical phantom, filled with a homogeneous holmium-166-chloride activity concentration solution, was imaged at one time point to determine a scanner-specific conversion factor, and to characterize the spatial dependency of the activity concentration recovery. One Jaszczak phantom with six fillable spheres, 10:1 sphere-to-background ratio, was imaged over a large range of holmium-166 activities (61-3130 MBq). The images were reconstructed with either an ordered subset expectation maximization (OSEM, Flash3D-reconstruction; scanner-specific quantification or self-calibration quantification) or an ordered subset conjugate gradient (OSCG, xSPECT-reconstruction; Broad Quantification) algorithm. These three quantification methods were compared for the data of the Jaszczak phantom and evaluated based on whole phantom recovered activity, activity concentration recovery coefficients (ACRC), and recovery curves. RESULTS The activity recovery in the Jaszczak phantom was 28-115% for the scanner-specific, and 57-97% for the Broad Quantification quantification methods, respectively. The self-calibration-based activity recovery is inherently always 100%. The ACRC for the largest sphere (Ø60 mm, ~ 113 mL) ranged over (depending on the activity level) 0.22-0.89, 0.76-0.86, 0.39-0.72 for scanner-specific, self-calibration and Broad Quantification, respectively. CONCLUSION Of the three investigated quantification methods, the self-calibration technique produces quantitative SPECT images with the highest accuracy in the investigated holmium-166 activity range.
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Affiliation(s)
| | - Daphne Lobeek
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Joey Roosen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Maarten de Bakker
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mark W Konijnenberg
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - J Frank W Nijsen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
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Chen Y, Pretorius PH, Lindsay C, Yang Y, King MA. Respiratory signal estimation for cardiac perfusion SPECT using deep learning. Med Phys 2024; 51:1217-1231. [PMID: 37523268 PMCID: PMC11380461 DOI: 10.1002/mp.16653] [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: 12/15/2022] [Revised: 07/06/2023] [Accepted: 07/09/2023] [Indexed: 08/02/2023] Open
Abstract
BACKGROUND Respiratory motion induces artifacts in reconstructed cardiac perfusion SPECT images. Correction for respiratory motion often relies on a respiratory signal describing the heart displacements during breathing. However, using external tracking devices to estimate respiratory signals can add cost and operational complications in a clinical setting. PURPOSE We aim to develop a deep learning (DL) approach that uses only SPECT projection data for respiratory signal estimation. METHODS A modified U-Net was implemented that takes temporally finely sampled SPECT sub-projection data (100 ms) as input. These sub-projections are obtained by reframing the 20-s list-mode data, resulting in 200 sub-projections, at each projection angle for each SPECT camera head. The network outputs a 200-time-point motion signal for each projection angle, which was later aggregated over all angles to give a full respiratory signal. The target signal for DL model training was from an external stereo-camera visual tracking system (VTS). In addition to comparing DL and VTS, we also included a data-driven approach based on the center-of-mass (CoM) strategy. This CoM method estimates respiratory signals by monitoring the axial changes of CoM for counts in the heart region of the sub-projections. We utilized 900 subjects with stress cardiac perfusion SPECT studies, with 302 subjects for testing and the remaining 598 subjects for training and validation. RESULTS The Pearson's correlation coefficient between the DL respiratory signal and the reference VTS signal was 0.90, compared to 0.70 between the CoM signal and the reference. For respiratory motion correction on SPECT images, all VTS, DL, and CoM approaches partially de-blured the heart wall, resulting in a thinner wall thickness and increased recovered maximal image intensity within the wall, with VTS reducing blurring the most followed by the DL approach. Uptake quantification for the combined anterior and inferior segments of polar maps showed a mean absolute difference from the reference VTS of 1.7% for the DL method for patients with motion >12 mm, compared to 2.6% for the CoM method and 8.5% for no correction. CONCLUSION We demonstrate the capability of a DL approach to estimate respiratory signal from SPECT projection data for cardiac perfusion imaging. Our results show that the DL based respiratory motion correction reduces artefacts and achieves similar regional quantification to that obtained using the stereo-camera VTS signals. This may enable fully automatic data-driven respiratory motion correction without relying on external motion tracking devices.
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Affiliation(s)
- Yuan Chen
- Department of Radiology, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - P Hendrik Pretorius
- Department of Radiology, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Clifford Lindsay
- Department of Radiology, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Yongyi Yang
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Michael A King
- Department of Radiology, University of Massachusetts Medical School, Worcester, Massachusetts, USA
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Chang H, Kobzarenko V, Mitra D. Inverse radon transform with deep learning: an application in cardiac motion correction. Phys Med Biol 2024; 69:035010. [PMID: 37988757 DOI: 10.1088/1361-6560/ad0eb5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 11/21/2023] [Indexed: 11/23/2023]
Abstract
Objective. This paper addresses performing inverse radon transform (IRT) with artificial neural network (ANN) or deep learning, simultaneously with cardiac motion correction (MC). The suggested application domain is cardiac image reconstruction in emission or transmission tomography where IRT is relevant. Our main contribution is in proposing an ANN architecture that is particularly suitable for this purpose.Approach. We validate our approach with two types of datasets. First, we use an abstract object that looks like a heart to simulate motion-blurred radon transform. With the known ground truth in hand, we then train our proposed ANN architecture and validate its effectiveness in MC. Second, we used human cardiac gated datasets for training and validation of our approach. The gating mechanism bins data over time using the electro-cardiogram (ECG) signals for cardiac motion correction.Main results. We have shown that trained ANNs can perform motion-corrected image reconstruction directly from a motion-corrupted sinogram. We have compared our model against two other known ANN-based approaches.Significance. Our method paves the way for eliminating any need for hardware gating in medical imaging.
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Affiliation(s)
- Haoran Chang
- Department of Electrical Engineering and Computer Science, Florida Institute of Technology, Melbourne, FL 32901, United States of America
| | - Valerie Kobzarenko
- Department of Electrical Engineering and Computer Science, Florida Institute of Technology, Melbourne, FL 32901, United States of America
| | - Debasis Mitra
- Department of Electrical Engineering and Computer Science, Florida Institute of Technology, Melbourne, FL 32901, United States of America
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Nakajima K, Shibutani T, Massanes F, Shimizu T, Yoshida S, Onoguchi M, Kinuya S, Vija AH. Myocardial perfusion imaging with retrospective gating and integrated correction of attenuation, scatter, respiration, motion, and arrhythmia. J Nucl Cardiol 2023; 30:2773-2789. [PMID: 37758961 PMCID: PMC10682219 DOI: 10.1007/s12350-023-03374-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/31/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND Absolute quantitative myocardial perfusion SPECT requires addressing of aleatory and epistemic uncertainties in conjunction with providing image quality sufficient for lesion detection and characterization. Iterative reconstruction methods enable the mitigation of the root causes of image degradation. This study aimed to determine the feasibility of a new SPECT/CT method with integrated corrections attempting to enable absolute quantitative cardiac imaging (xSPECT Cardiac; xSC). METHODS We compared images of prototype xSC and conventional SPECT (Flash3DTM) acquired at rest from 56 patients aged 71 ± 12 y with suspected coronary heart disease. The xSC prototype comprised list-mode acquisitions with continuous rotation and subsequent iterative reconstructions with retrospective electrocardiography (ECG) gating. Besides accurate image formation modeling, patient-specific CT-based attenuation and energy window-based scatter correction, additionally we applied mitigation for patient and organ motion between views (inter-view), and within views (intra-view) for both the gated and ungated reconstruction. We then assessed image quality, semiquantitative regional values, and left ventricular function in the images. RESULTS The quality of all xSC images was acceptable for clinical purposes. A polar map showed more uniform distribution for xSC compared with Flash3D, while lower apical count and higher defect contrast of myocardial infarction (p = 0.0004) were observed on xSC images. Wall motion, 16-gate volume curve, and ejection fraction were at least acceptable, with indication of improvements. The clinical prospectively gated method rejected beats ≥20% in 6 patients, whereas retrospective gating used an average of 98% beats, excluding 2% of beats. We used the list-mode data to create a product equivalent prospectively gated dataset. The dataset showed that the xSC method generated 18% higher count data and images with less noise, with comparable functional variables of volume and LVEF (p = ns). CONCLUSIONS Quantitative myocardial perfusion imaging with the list-mode-based prototype xSPECT Cardiac is feasible, resulting in images of at least acceptable image quality.
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Affiliation(s)
- Kenichi Nakajima
- Functional Imaging and Artificial Intelligence, Kanazawa University, Kanazawa, 920-8640, Japan.
| | - Takayuki Shibutani
- Quantum Medical Technology, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan
| | - Francesc Massanes
- Siemens Medical Solutions USA, Inc. Molecular Imaging, Hoffman Estates, IL, USA
| | - Takeshi Shimizu
- Siemens Medical Solutions USA, Inc. Molecular Imaging, Hoffman Estates, IL, USA
| | - Shohei Yoshida
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Science, Kanazawa, Japan
| | - Masahisa Onoguchi
- Quantum Medical Technology, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan
| | - Seigo Kinuya
- Department of Nuclear Medicine, Kanazawa University, Kanazawa, Japan
| | - A Hans Vija
- Siemens Medical Solutions USA, Inc. Molecular Imaging, Hoffman Estates, IL, USA
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Reymann MP, Vija AH, Maier A. Method for comparison of data driven gating algorithms in emission tomography. Phys Med Biol 2023; 68:185024. [PMID: 37619585 DOI: 10.1088/1361-6560/acf3ce] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 08/24/2023] [Indexed: 08/26/2023]
Abstract
Objective.Multiple algorithms have been proposed for data driven gating (DDG) in single photon emission computed tomography (SPECT) and have successfully been applied to myocardial perfusion imaging (MPI). Application of DDG to acquisition types other than SPECT MPI has not been demonstrated so far, as limitations and pitfalls of current methods are unknown.Approach.We create a comprehensive set of phantoms simulating the influence of different motion artifacts, view angles, moving objects, contrast, and count levels in SPECT. We perform Monte Carlo simulation of the phantoms, allowing the characterization of DDG algorithms using quantitative metrics derived from the data and evaluate the Center of Light (COL) and Laplacian Eigenmaps methods as sample DDG algorithms.Main results.View angle, object size, count rate density, and contrast influence the accuracy of both DDG methods. Moreover, the ability to extract the respiratory motion in the phantom was shown to correlate with the contrast of the moving feature to the background, the signal to noise ratio, and the noise in the data.Significance.We showed that reporting the average correlation to an external physical reference signal per acquisition is not sufficient to characterize DDG methods. Assessing DDG methods on a view-by-view basis using the simulations and metrics from this work could enable the identification of pitfalls of current methods, and extend their application to acquisitions beyond SPECT MPI.
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Affiliation(s)
- M P Reymann
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Siemens Healthcare GmbH, Forchheim, Germany
- Clinic for Nuclear Medicine, University Hospital Erlangen, Germany
| | - A H Vija
- Siemens Medical Solutions USA, Inc., Molecular Imaging, Hoffman Estates, IL, United States of America
| | - A Maier
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Vergnaud L, Robert A, Baudier T, Parisse-Di Martino S, Boissard P, Rit S, Badel JN, Sarrut D. Dosimetric impact of 3D motion-compensated SPECT reconstruction for SIRT planning. EJNMMI Phys 2023; 10:8. [PMID: 36749446 PMCID: PMC9905464 DOI: 10.1186/s40658-023-00525-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 01/11/2023] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND In selective internal radiation therapy, 99mTc SPECT images are used to optimize patient treatment planning, but they are affected by respiratory motion. In this study, we evaluated on patient data the dosimetric impact of motion-compensated SPECT reconstruction on several volumes of interest (VOI), on the tumor-to-normal liver (TN) ratio and on the activity to be injected. METHODS Twenty-nine patients with liver cancer or hepatic metastases treated by radioembolization were included in this study. The biodistribution of 90Y is assumed to be the same as that of 99mTc when predictive dosimetry is implemented. A total of 31 99mTc SPECT images were acquired and reconstructed with two methods: conventional OSEM (3D) and motion-compensated OSEM (3Dcomp). Seven VOI (liver, lungs, tumors, perfused liver, hepatic reserve, healthy perfused liver and healthy liver) were delineated on the CT or obtained by thresholding SPECT images followed by Boolean operations. Absorbed doses were calculated for each reconstruction using Monte Carlo simulations. Percentages of dose difference (PDD) between 3Dcomp and 3D reconstructions were estimated as well as the relative differences for TN ratio and activities to be injected. The amplitude of movement was determined with local rigid registration of the liver between the 3Dcomp reconstructions of the extreme phases of breathing. RESULTS The mean amplitude of the liver was 9.5 ± 2.7 mm. Medians of PDD were closed to zero for all VOI except for lungs (6.4%) which means that the motion compensation overestimates the absorbed dose to the lungs compared to the 3D reconstruction. The smallest lesions had higher PDD than the largest ones. Between 3D and 3Dcomp reconstructions, means of differences in lung dose and TN ratio were not statistically significant, but in some cases these differences exceed 1 Gy (4/31) and 8% (2/31). The absolute differences in activity were on average 3.1% ± 5.1% and can reach 22.8%. CONCLUSION The correction of respiratory motion mainly impacts the lung and tumor doses but only for some patients. The largest dose differences are observed for the smallest lesions.
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Affiliation(s)
- Laure Vergnaud
- CREATIS; CNRS UMR 5220; INSERM U 1044; Université de Lyon; INSA-Lyon, Université Lyon 1, Lyon, France. .,Centre de Lutte Contre Le Cancer Léon Bérard, Lyon, France.
| | - Antoine Robert
- grid.7849.20000 0001 2150 7757CREATIS; CNRS UMR 5220; INSERM U 1044; Université de Lyon; INSA-Lyon, Université Lyon 1, Lyon, France
| | - Thomas Baudier
- grid.7849.20000 0001 2150 7757CREATIS; CNRS UMR 5220; INSERM U 1044; Université de Lyon; INSA-Lyon, Université Lyon 1, Lyon, France ,grid.418116.b0000 0001 0200 3174Centre de Lutte Contre Le Cancer Léon Bérard, Lyon, France
| | | | - Philippe Boissard
- grid.418116.b0000 0001 0200 3174Centre de Lutte Contre Le Cancer Léon Bérard, Lyon, France
| | - Simon Rit
- grid.7849.20000 0001 2150 7757CREATIS; CNRS UMR 5220; INSERM U 1044; Université de Lyon; INSA-Lyon, Université Lyon 1, Lyon, France
| | - Jean-Noël Badel
- grid.418116.b0000 0001 0200 3174Centre de Lutte Contre Le Cancer Léon Bérard, Lyon, France
| | - David Sarrut
- grid.7849.20000 0001 2150 7757CREATIS; CNRS UMR 5220; INSERM U 1044; Université de Lyon; INSA-Lyon, Université Lyon 1, Lyon, France ,grid.418116.b0000 0001 0200 3174Centre de Lutte Contre Le Cancer Léon Bérard, Lyon, France
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7
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Robert A, Rit S, Baudier T, Jomier J, Sarrut D. Data-Driven Respiration-Gated SPECT for Liver Radioembolization. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022. [DOI: 10.1109/trpms.2021.3137990] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Antoine Robert
- Univ.Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, Lyon, France
| | - Simon Rit
- Univ.Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, Lyon, France
| | | | | | - David Sarrut
- Univ.Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, Lyon, France
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Santoro M, Della Gala G, Paolani G, Zagni F, Strolin S, Civollani S, Calderoni L, Cappelli A, Mosconi C, Lodi Rizzini E, Tabacchi E, Morganti AG, Fanti S, Golfieri R, Strigari L. A novel tool for motion-related dose inaccuracies reduction in 99mTc-MAA SPECT/CT images for SIRT planning. Phys Med 2022; 98:98-112. [PMID: 35526374 DOI: 10.1016/j.ejmp.2022.04.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 04/05/2022] [Accepted: 04/27/2022] [Indexed: 12/19/2022] Open
Abstract
INTRODUCTION In Selective Internal Radiation Therapy (SIRT), 90Y is administered to primary/secondary hepatic lesions. An accurate pre-treatment planning using 99mTc-MAA SPECT/CT allows the assessment of its feasibility and of the activity to be injected. Unfortunately, SPECT/CT suffers from patient-specific respiratory motion which causes artifacts and absorbed dose inaccuracies. In this study, a data-driven solution was developed to correct the respiratory motion. METHODS The tool realigns the barycenter of SPECT projection images and shifts them to obtain a fine registration with the attenuation map. The tool was validated using a modified dynamic phantom with several breathing patterns. We compared the absorbed dose distributions derived from uncorrected(Dm)/corrected(Dc) images with static ones(Ds) in terms of γ-passing rates, 210 Gy isodose volumes, dose-volume histograms and percentage differences of mean doses (i.e., ΔD¯m and ΔD¯c, respectively). The tool was applied to twelve SIRT patients and the Bland-Altman analysis was performed on mean doses. RESULTS In the phantom study, the agreement between Dc and Ds was higher (γ-passing rates generally > 90%) than Dm and Ds. The isodose volumes in Dc were closer than Dm to Ds, with differences up to 10% and 30% respectively. A reduction from a median ΔD¯m = -19.3% to ΔD¯c = -0.9%, from ΔD¯m = -42.8% to ΔD¯c = -7.0% and from ΔD¯m = 1586% to ΔD¯c = 47.2% was observed in liver-, tumor- and lungs-like structures. The Bland-Altman analysis on patients showed variations (±50 Gy) and (±4 Gy) between D¯c and D¯m of tumor and lungs, respectively. CONCLUSION The proposed tool allowed the correction of 99mTc-MAA SPECT/CT images, improving the accuracy of the absorbed dose distribution.
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Affiliation(s)
- Miriam Santoro
- Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Giuseppe Della Gala
- Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Giulia Paolani
- Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Federico Zagni
- Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Silvia Strolin
- Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Simona Civollani
- Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Letizia Calderoni
- Nuclear Medicine Unit, IRCCS Azienda Ospedaliero, University of Bologna, 40138 Bologna, Italy
| | - Alberta Cappelli
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Cristina Mosconi
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Elisa Lodi Rizzini
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Elena Tabacchi
- Nuclear Medicine Unit, IRCCS Azienda Ospedaliero, University of Bologna, 40138 Bologna, Italy
| | | | - Stefano Fanti
- Nuclear Medicine Unit, IRCCS Azienda Ospedaliero, University of Bologna, 40138 Bologna, Italy
| | - Rita Golfieri
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Lidia Strigari
- Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy.
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Lu Z, Chen G, Lyu Y, Chen Y, Mok GSP. Technical Note: Respiratory impacts on static and respiratory gated 99m Tc-MAA SPECT/CT for liver radioembolization- A simulation study. Med Phys 2022; 49:5330-5339. [PMID: 35446448 DOI: 10.1002/mp.15682] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 03/25/2022] [Accepted: 04/12/2022] [Indexed: 11/06/2022] Open
Abstract
PURPOSE We aimed to evaluate respiratory impacts on static and respiratory gated (RG) 99m Tc-MAA SPECT in terms of respiratory motion (RM) blur, attenuation correction (AC) and volume-of-interest (VOI) segmentation on lung shunt faction (LSF) and tumor-to-normal liver ratio (TNR) estimation for liver radioembolization therapy planning. METHODS The XCAT phantom was used to simulate a population of 300 phantoms, modelling various anatomical variations, tumor characteristics, respiratory motion amplitudes, LSFs and TNRs. One hundred and twenty noisy projections of average activity maps near end-expiration (End-EX) and whole respiratory cycle were simulated analytically, modeling attenuation and geometric collimator-detector-response (GCDR). The OS-EM algorithm was employed for reconstruction, modeling AC and GCDR. RM effect was evaluated for static SPECT, while AC and VOI mismatch effects were investigated independently and together for static and RG SPECT utilizing one gate, i.e., End-EX. LSF and TNR errors were measured based on the ground truth. Lesions with different characteristics were also investigated for static and RG SPECT. RESULTS RM overestimates LSF and underestimates TNR. The VOI mismatch caused the largest errors in both RG and static SPECT for LSF and TNR estimation, reaching 160% and -52% correspondingly with extremely mismatched VOIs for RG SPECT, even larger than those for static SPECT. With matched AC and VOIs, RG SPECT has better performance than static SPECT. Larger TNR errors are associated with tumors of smaller sizes and higher TNR for static SPECT. CONCLUSIONS The VOI segmentation mismatch has a stronger impact, followed by RM and AC in static 99m Tc-MAA SPECT/CT. This effect is more pronounced for RG SPECT. When VOI masks are derived from a matched CT, RG SPECT is generally superior to static SPECT for LSF and TNR estimation. The performance of RG SPECT could be worse than static SPECT when a mismatched CT is used for segmentation. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Zhonglin Lu
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
| | - Gefei Chen
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
| | - Yingqing Lyu
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
| | - Yue Chen
- Department of Nuclear Medicine, The Affiliated Hospital of Southwest Medical University, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, Sichuan, China
| | - Greta S P Mok
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China.,Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Taipa, Macau SAR, China.,Ministry of Education Frontiers Science Center for Precision Oncology, Faculty of Health Science, University of Macau, Taipa, Macau SAR, China
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10
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Chen C, Chandrasekaran P, Liu Y, Simonetti OP, Tong M, Ahmad R. Ensuring respiratory phase consistency to improve cardiac function quantification in real-time CMR. Magn Reson Med 2022; 87:1595-1604. [PMID: 34719067 PMCID: PMC8776600 DOI: 10.1002/mrm.29064] [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: 06/17/2021] [Revised: 09/16/2021] [Accepted: 10/13/2021] [Indexed: 11/09/2022]
Abstract
PURPOSE To develop an automatic method for selecting heartbeats with consistent respiratory phase to improve accuracy of cardiac function quantification in real-time (RT) cardiac MRI. METHODS The respiratory signal is extracted by a principal component analysis method from RT cine images. Then, a two-step procedure is used to determine the directionality (sign) of the respiratory signal. With the motion in a manually selected region-of-interest as a reference, the quality of the extracted respiratory signal is assessed using multislice RT cine data from 11 volunteers and 10 patients. In addition, the impact of selecting heartbeats with consistent respiratory phase on the cardiac function quantification is evaluated. RESULTS The extracted respiratory signal using the proposed method exhibits a high, positive correlation with the reference in all cases and is more robust compared to a recently proposed method. Also, for right ventricular function quantification, selecting heartbeats at expiratory position improves agreement between RT cine and breath-held reference. CONCLUSION The proposed method enables fully automatic extraction and directionality determinations of respiratory signal from RT cardiac cine images, allowing accurate cardiac function quantification.
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Affiliation(s)
- Chong Chen
- Biomedical Engineering, The Ohio State University, Columbus OH, USA
| | | | - Yingmin Liu
- Davis Heart & Lung Research Institute, The Ohio State University, Columbus OH, USA
| | - Orlando P. Simonetti
- Davis Heart & Lung Research Institute, The Ohio State University, Columbus OH, USA
- Internal Medicine, The Ohio State University, Columbus OH, USA
- Radiology, The Ohio State University, Columbus OH, USA
| | - Matthew Tong
- Internal Medicine, The Ohio State University, Columbus OH, USA
| | - Rizwan Ahmad
- Biomedical Engineering, The Ohio State University, Columbus OH, USA
- Davis Heart & Lung Research Institute, The Ohio State University, Columbus OH, USA
- Electrical and Computer Engineering, The Ohio State University, Columbus OH, USA
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Pretorius PH, King MA. Data-driven respiratory signal estimation from temporally finely sampled projection data in conventional cardiac perfusion SPECT imaging. Med Phys 2022; 49:282-294. [PMID: 34859456 PMCID: PMC9348806 DOI: 10.1002/mp.15391] [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: 05/19/2021] [Revised: 10/28/2021] [Accepted: 11/19/2021] [Indexed: 01/03/2023] Open
Abstract
PURPOSE The aim of this work was to revisit the data-driven approach of axial center-of-mass (COM) measurements to recover a surrogate respiratory signal from finely sampled (100 ms) single photon emission computed tomography (SPECT) projection data derived from list-mode acquisitions. METHODS For our initial evaluation, we acquired list-mode projection data from an anthropomorphic cardiac phantom mounted on a Quasar respiratory motion platform simulating 15 mm amplitude respiratory motion. We also selected 302 consecutive patients (138 males, 164 females) with list-mode acquisitions, external respiratory motion tracking, and written consent to evaluate the clinical efficacy of our data-driven approach. Linear regression, Pearson's correlation coefficient (r), and standard error of the estimates (SEE) between the respiratory signals obtained with a visual tracking system (VTS) and COM measurements were calculated for individual projection data sets and for the patient group as a whole. Both the VTS- and COM-derived respiratory signals were used to estimate and correct respiratory motion. The reconstruction for six-degree of freedom rigid-body motion estimation was done in two ways: (1) using three iterations of ordered-subsets expectation-maximization (OSEM) with four subsets (16 projection angles per subset), or 12 iterations of maximum-likelihood expectation-maximization (MLEM). Respiratory motion compensation was done employing either OSEM with 16 subsets (four projection angles per subset) and five iterations or MLEM and 80 iterations, using the two respiratory estimates, respectively. Polar map quantification was also performed, calculating the percentage count difference (%Diff) between polar maps without and with respiratory motion included. Average % Diff was calculated in 17 segments (defined according to ASNC Guidelines). Paired t-tests were used to determine significance (p-values). RESULTS The r-value calculated when comparing the VTS and COM respiratory signals varied widely between -0.01 and 0.96 with an average of 0.70, while the SEE varied between 0.80 and 6.48 mm with an average of 2.05 mm for our patient set, while the same values for the one anthropomorphic phantom acquisition are 0.91 and 1.11 mm, respectively. A comparison between the respiratory motion estimates for VTS and COM in the S-I direction yielded an r = 0.90 (0.94), and an SEE of 1.56 mm (1.20 mm) for OSEM (MLEM), respectively. Bland-Altman plots and calculated intraclass correlation coefficients also showed excellent agreement between the VTS and COM respiratory motion estimates. Average S-I respiratory estimates for the VTS (COM) were 9.04 (9.2 mm) and 9.01 mm (9.14 mm) for the OSEM and MLEM, respectively. The paired t-test approached significance when comparing VTS and COM estimated respiratory signals with p-values of 0.069 and 0.051 for OSEM and MLEM. The respiratory estimates from the anthropomorphic cardiac phantom experiment using the VTS (COM) were 12.62 (14.10 mm) and 12.55 mm (14.29 mm) for OSEM and MLEM, respectively. Polar map quantification yielded average % Diff consistently better when employing VTS-derived respiratory estimates to correct for respiration compared to the COM-derived estimates. CONCLUSIONS The results indicate that our COM method has the potential to provide an automated data-driven correction of cardiac respiratory motion without the drawbacks of our VTS methodology. However, it is not generally equivalent to the VTS method in extent of correction.
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Affiliation(s)
- P Hendrik Pretorius
- Department of Radiology, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Michael A King
- Department of Radiology, University of Massachusetts Medical School, Worcester, Massachusetts, USA
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Hoppe E, Wetzl J, Yoon SS, Bacher M, Roser P, Stimpel B, Preuhs A, Maier A. Deep Learning-Based ECG-Free Cardiac Navigation for Multi-Dimensional and Motion-Resolved Continuous Magnetic Resonance Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2105-2117. [PMID: 33848244 DOI: 10.1109/tmi.2021.3073091] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
For the clinical assessment of cardiac vitality, time-continuous tomographic imaging of the heart is used. To further detect e.g., pathological tissue, multiple imaging contrasts enable a thorough diagnosis using magnetic resonance imaging (MRI). For this purpose, time-continous and multi-contrast imaging protocols were proposed. The acquired signals are binned using navigation approaches for a motion-resolved reconstruction. Mostly, external sensors such as electrocardiograms (ECG) are used for navigation, leading to additional workflow efforts. Recent sensor-free approaches are based on pipelines requiring prior knowledge, e.g., typical heart rates. We present a sensor-free, deep learning-based navigation that diminishes the need for manual feature engineering or the necessity of prior knowledge compared to previous works. A classifier is trained to estimate the R-wave timepoints in the scan directly from the imaging data. Our approach is evaluated on 3-D protocols for continuous cardiac MRI, acquired in-vivo and free-breathing with single or multiple imaging contrasts. We achieve an accuracy of > 98% on previously unseen subjects, and a well comparable image quality with the state-of-the-art ECG-based reconstruction. Our method enables an ECG-free workflow for continuous cardiac scans with simultaneous anatomic and functional imaging with multiple contrasts. It can be potentially integrated without adapting the sampling scheme to other continuous sequences by using the imaging data for navigation and reconstruction.
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13
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Dietze MMA, Kunnen B, Lam MGEH, de Jong HWAM. Interventional respiratory motion compensation by simultaneous fluoroscopic and nuclear imaging: a phantom study. Phys Med Biol 2021; 66:065001. [PMID: 33571969 DOI: 10.1088/1361-6560/abe556] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE A compact and mobile hybrid c-arm scanner, capable of simultaneously acquiring nuclear and fluoroscopic projections and SPECT/CBCT, was developed to aid fluoroscopy-guided interventional procedures involving the administration of radionuclides (e.g. hepatic radioembolization). However, as in conventional SPECT/CT, the acquired nuclear images may be deteriorated by patient respiratory motion. We propose to perform compensation for respiratory motion by extracting the motion signal from fluoroscopic projections so that the nuclear counts can be gated into motion bins. The purpose of this study is to quantify the performance of this motion compensation technique with phantom experiments. METHODS Anthropomorphic phantom configurations that are representative of distributions obtained during the pre-treatment procedure of hepatic radioembolization were placed on a stage that translated with three different motion patterns. Fluoroscopic projections and nuclear counts were simultaneously acquired under planar and SPECT/CBCT imaging. The planar projections were visually assessed. The SPECT reconstructions were visually assessed and quantitatively assessed by calculating the activity recovery of the spherical inserts in the phantom. RESULTS The planar nuclear projections of the translating anthropomorphic phantom were blurry when no motion compensation was applied. With motion compensation, the nuclear projections became representative of the stationary phantom nuclear projection. Similar behavior was observed for the visual quality of SPECT reconstructions. The mean error of the activity recovery in the uncompensated SPECT reconstructions was 15.8% ± 0.9% for stable motion, 11.9% ± 0.9% for small variations, and 11.0% ± 0.9% for large variations. When applying motion compensation, the mean error decreased to 1.8% ± 1.6% for stable motion, 2.2% ± 1.5% for small variations, and 5.2% ± 2.5% for large variations. CONCLUSION A compact and mobile hybrid c-arm scanner, capable of simultaneously acquiring nuclear and fluoroscopic projections, can perform compensation for respiratory motion. Such motion compensation results in sharper planar nuclear projections and increases the quantitative accuracy of the SPECT reconstructions.
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Affiliation(s)
- Martijn M A Dietze
- Radiology and Nuclear Medicine, Utrecht University and University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht, The Netherlands. Image Sciences Institute, Utrecht University and University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht, The Netherlands
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Artificial Intelligence and the Medical Physicist: Welcome to the Machine. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041691] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.
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15
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Fang H, Li H, Song S, Pang K, Ai D, Fan J, Song H, Yu Y, Yang J. Motion-flow-guided recurrent network for respiratory signal estimation of x-ray angiographic image sequences. Phys Med Biol 2020; 65:245020. [PMID: 32590382 DOI: 10.1088/1361-6560/aba087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Motion compensation can eliminate inconsistencies of respiratory movement during image acquisitions for precise vascular reconstruction in the clinical diagnosis of vascular disease from x-ray angiographic image sequences. In x-ray-based vascular interventional therapy, motion modeling can simulate the process of organ deformation driven by motion signals to display a dynamic organ on angiograms without contrast agent injection. Automatic respiratory signal estimation from x-ray angiographic image sequences is essential for motion compensation and modeling. The effects of respiratory motion, cardiac impulses, and tremors on structures in the chest and abdomen bring difficulty in extracting accurate respiratory signals individually. In this study, an end-to-end deep learning framework based on a motion-flow-guided recurrent network is proposed to address the aforementioned problem. The proposed method utilizes a convolutional neural network to learn the spatial features of every single frame, and a recurrent neural network to learn the temporal features of the entire sequence. The combination of the two networks can effectively analyze the image sequence to realize respiratory signal estimation. In addition, the motion-flow between consecutive frames is introduced to provide a dynamic constraint of spatial features, which enables the recurrent network to learn better temporal features from dynamic spatial features than from static spatial features. We demonstrate the advantages of our approach on designed datasets which contain coronary and hepatic angiographic sequences with diaphragm structures, and coronary angiographic sequences without diaphragm structures. Our method improves over state-of-the-art manifold-learning-based methods by 85.7%, 81.5% and 75.3% in respiratory signal accuracy metric on these datasets. The results demonstrate that the proposed method can effectively estimate respiratory signals from multiple motion patterns.
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Affiliation(s)
- Huihui Fang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
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Zhang D, Pretorius PH, Ghaly M, Zhang Q, King MA, Mok GSP. Evaluation of different respiratory gating schemes for cardiac SPECT. J Nucl Cardiol 2020; 27:634-647. [PMID: 30088195 PMCID: PMC11409049 DOI: 10.1007/s12350-018-1392-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 07/17/2018] [Indexed: 11/26/2022]
Abstract
BACKGROUND Respiratory gating reduces motion blurring in cardiac SPECT. Here we aim to evaluate the performance of three respiratory gating strategies using a population of digital phantoms with known truth and clinical data. METHODS We analytically simulated 60 projections for 10 XCAT phantoms with 99mTc-sestamibi distributions using three gating schemes: equal amplitude gating (AG), equal count gating (CG), and equal time gating (TG). Clinical list-mode data for 10 patients who underwent 99mTc-sestamibi scans were also processed using the 3 gating schemes. Reconstructed images in each gate were registered to a reference gate, averaged and reoriented to generate the polar plots. For simulations, image noise, relative difference (RD) of averaged count for each of the 17 segment, and relative defect size difference (RSD) were analyzed. For clinical data, image intensity profile and FWHM were measured across the left ventricle wall. RESULTS For simulations, AG and CG methods showed significantly lower RD and RSD compared to TG, while noise variation was more non-uniform through different gates for AG. In the clinical study, AG and CG had smaller FWHM than TG. CONCLUSIONS AG and CG methods show better performance for motion reduction and are recommended for clinical respiratory gating SPECT implementation.
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Affiliation(s)
- Duo Zhang
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
- Department of Radiology, University of Massachusetts Medical School, Worcester, USA
| | - P Hendrik Pretorius
- Department of Radiology, University of Massachusetts Medical School, Worcester, USA
| | - Michael Ghaly
- The Russell H Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA
- Radiopharmaceutical Imaging and Dosimetry (RAPID), LLC, Baltimore, MD, USA
| | - Qi Zhang
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
| | - Michael A King
- Department of Radiology, University of Massachusetts Medical School, Worcester, USA
| | - Greta S P Mok
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China.
- Department of Radiology, University of Massachusetts Medical School, Worcester, USA.
- Faculty of Health Sciences, University of Macau, Taipa, Macau SAR, China.
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Zhang D, Ghaly M, Mok GSP. InterpolatedCTfor attenuation correction on respiratory gating cardiacSPECT/CT— A simulation study. Med Phys 2019; 46:2621-2628. [DOI: 10.1002/mp.13513] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 03/07/2019] [Accepted: 03/18/2019] [Indexed: 11/12/2022] Open
Affiliation(s)
- Duo Zhang
- Biomedical Imaging Laboratory (BIG) Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau SAR China
| | - Michael Ghaly
- Russell H Morgan Department of Radiology and Radiological Science Johns Hopkins University Baltimore MD USA
- Radiopharmaceutical Imaging and Dosimetry (RAPID), LLC Baltimore MD USA
| | - Greta S. P. Mok
- Biomedical Imaging Laboratory (BIG) Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau SAR China
- Faculty of Health Sciences University of Macau Macau SAR China
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Kesner A, Laforest R, Otazo R, Jennifer K, Pan T. Medical imaging data in the digital innovation age. Med Phys 2018; 45:e40-e52. [PMID: 29405298 DOI: 10.1002/mp.12794] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 11/21/2017] [Accepted: 01/08/2018] [Indexed: 12/17/2022] Open
Abstract
As we reflect on decades of exponential advancements in electronic innovation, we can see the field of medical imaging eclipsed by a new digital landscape - one that is inexpensive, fast, and powerful. This new paradigm presents new opportunities to innovate in both research and clinical settings. In this article, we review the current role of data: the common perceptions around its valuation and the infrastructure currently in place for data-driven innovation. Looking forward, we consider what has already been achieved using modern data capacities, the opportunities we have for further expansion in this area, and the obstacles we will need to transcend.
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Affiliation(s)
- Adam Kesner
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard Laforest
- Division of Radiological Sciences, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ricardo Otazo
- Department of Radiology, New York University Lagone Health, New York, NY, USA
| | - Kwak Jennifer
- Department of Radiology, University of Colorado, Denver, CO, USA
| | - Tinsu Pan
- Department of Imaging Physics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
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Morland D, Guendouzen S, Rust E, Papathanassiou D, Passat N, Hubelé F. Data-driven respiratory gating for ventilation/perfusion lung scan. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2018; 63:394-398. [PMID: 29409314 DOI: 10.23736/s1824-4785.18.03002-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND Ventilation/perfusion lung scan is subject to blur due to respiratory motion whether with planar acquisition or single photon emission computed tomography (SPECT). We propose a data-driven gating method for extracting different respiratory phases from lung scan list-mode or dynamic data. METHODS The algorithm derives a surrogate respiratory signal from an automatically detected diaphragmatic region of interest. The time activity curve generated is then filtered using a Savitzky-Golay filter. We tested this method on an oscillating phantom in order to evaluate motion blur decrease and on one lung SPECT. RESULTS Our algorithm reduced motion blur on phantom acquisition: mean full width at half maximum 8.1 pixels on non-gated acquisition versus 5.3 pixels on gated acquisition and 4.1 pixels on reference image. Automated detection of the diaphragmatic region and time-activity curves generation were successful on patient acquisition. CONCLUSIONS This algorithm is compatible with a clinical use considering its runtime. Further studies will be needed in order to validate this method.
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Affiliation(s)
- David Morland
- Unit of Nuclear Medicine, Jean Godinot Institute, Reims, France - .,Laboratory of Biophysics, Research Unit of Medicine, University of Reims Champagne-Ardenne, Reims, France - .,EA 3804, Science and Information Technology Research Center (CReSTIC), University of Reims Champagne-Ardenne, Reims, France -
| | | | - Edmond Rust
- Service of Nuclear Medicine, Diaconate Clinic, Mulhouse, France
| | - Dimitri Papathanassiou
- Unit of Nuclear Medicine, Jean Godinot Institute, Reims, France.,Laboratory of Biophysics, Research Unit of Medicine, University of Reims Champagne-Ardenne, Reims, France.,EA 3804, Science and Information Technology Research Center (CReSTIC), University of Reims Champagne-Ardenne, Reims, France
| | - Nicolas Passat
- EA 3804, Science and Information Technology Research Center (CReSTIC), University of Reims Champagne-Ardenne, Reims, France
| | - Fabrice Hubelé
- Service of Biophysics et Nuclear Medicine, Strasbourg University Hospitals, Strasbourg, France
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Fischer P, Faranesh A, Pohl T, Maier A, Rogers T, Ratnayaka K, Lederman R, Hornegger J. An MR-Based Model for Cardio-Respiratory Motion Compensation of Overlays in X-Ray Fluoroscopy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:47-60. [PMID: 28692969 PMCID: PMC5750091 DOI: 10.1109/tmi.2017.2723545] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In X-ray fluoroscopy, static overlays are used to visualize soft tissue. We propose a system for cardiac and respiratory motion compensation of these overlays. It consists of a 3-D motion model created from real-time magnetic resonance (MR) imaging. Multiple sagittal slices are acquired and retrospectively stacked to consistent 3-D volumes. Slice stacking considers cardiac information derived from the ECG and respiratory information extracted from the images. Additionally, temporal smoothness of the stacking is enhanced. Motion is estimated from the MR volumes using deformable 3-D/3-D registration. The motion model itself is a linear direct correspondence model using the same surrogate signals as slice stacking. In X-ray fluoroscopy, only the surrogate signals need to be extracted to apply the motion model and animate the overlay in real time. For evaluation, points are manually annotated in oblique MR slices and in contrast-enhanced X-ray images. The 2-D Euclidean distance of these points is reduced from 3.85 to 2.75 mm in MR and from 3.0 to 1.8 mm in X-ray compared with the static baseline. Furthermore, the motion-compensated overlays are shown qualitatively as images and videos.
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Fischer P, Pohl T, Faranesh A, Maier A, Hornegger J. Unsupervised Learning for Robust Respiratory Signal Estimation From X-Ray Fluoroscopy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:865-877. [PMID: 27654320 PMCID: PMC5489115 DOI: 10.1109/tmi.2016.2609888] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Respiratory signals are required for image gating and motion compensation in minimally invasive interventions. In X-ray fluoroscopy, extraction of a respiratory signal can be challenging due to characteristics of interventional imaging, in particular injection of contrast agent and automatic exposure control. We present a novel method for respiratory signal extraction based on dimensionality reduction that can tolerate these events. Images are divided into patches of multiple sizes. Low-dimensional embeddings are generated for each patch using illumination-invariant kernel PCA. Patches with respiratory information are selected automatically by agglomerative clustering. The signals from this respiratory cluster are combined robustly to a single respiratory signal. In the experiments, we evaluate our method on a variety of scenarios. If the diaphragm is visible, we track its superior-inferior motion as ground truth. Our method has a correlation coefficient of more than 91% with the ground truth irrespective of whether or not contrast agent injection or automatic exposure control occur. Additionally, we show that very similar signals are estimated from biplane sequences and from sequences without visible diaphragm. Since all these cases are handled automatically, the method is robust enough to be considered for use in a clinical setting.
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Dasari PKR, Könik A, Pretorius PH, Johnson KL, Segars WP, Shazeeb MS, King MA. Correction of hysteretic respiratory motion in SPECT myocardial perfusion imaging: Simulation and patient studies. Med Phys 2017; 44:437-450. [PMID: 28032913 DOI: 10.1002/mp.12072] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Revised: 11/18/2016] [Accepted: 12/09/2016] [Indexed: 11/11/2022] Open
Abstract
PURPOSE Amplitude-based respiratory gating is known to capture the extent of respiratory motion (RM) accurately but results in residual motion in the presence of respiratory hysteresis. In our previous study, we proposed and developed a novel approach to account for respiratory hysteresis by applying the Bouc-Wen (BW) model of hysteresis to external surrogate signals of anterior/posterior motion of the abdomen and chest with respiration. In this work, using simulated and clinical SPECT myocardial perfusion imaging (MPI) studies, we investigate the effects of respiratory hysteresis and evaluate the benefit of correcting it using the proposed BW model in comparison with the abdomen signal typically employed clinically. METHODS The MRI navigator data acquired in free-breathing human volunteers were used in the specially modified 4D NCAT phantoms to allow simulating three types of respiratory patterns: monotonic, mild hysteresis, and strong hysteresis with normal myocardial uptake, and perfusion defects in the anterior, lateral, inferior, and septal locations of the mid-ventricular wall. Clinical scans were performed using a Tc-99m sestamibi MPI protocol while recording respiratory signals from thoracic and abdomen regions using a visual tracking system (VTS). The performance of the correction using the respiratory signals was assessed through polar map analysis in phantom and 10 clinical studies selected on the basis of having substantial RM. RESULTS In phantom studies, simulations illustrating normal myocardial uptake showed significant differences (P < 0.001) in the uniformity of the polar maps between the RM uncorrected and corrected. No significant differences were seen in the polar map uniformity across the RM corrections. Studies simulating perfusion defects showed significantly decreased errors (P < 0.001) in defect severity and extent for the RM corrected compared to the uncorrected. Only for the strong hysteretic pattern, there was a significant difference (P < 0.001) among the RM corrections. The errors in defect severity and extent for the RM correction using abdomen signal were significantly higher compared to that of the BW (severity = -4.0%, P < 0.001; extent = -65.4%, P < 0.01) and chest (severity = -4.1%, P < 0.001; extent = -52.5%, P < 0.01) signals. In clinical studies, the quantitative analysis of the polar maps demonstrated qualitative and quantitative but not statistically significant differences (P = 0.73) between the correction methods that used the BW signal and the abdominal signal. CONCLUSIONS This study shows that hysteresis in respiration affects the extent of residual motion left in the RM-binned data, which can impact wall uniformity and the visualization of defects. Thus, there appears to be the potential for improved accuracy in reconstruction in the presence of hysteretic RM with the BW model method providing a possible step in the direction of improvement.
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Affiliation(s)
- Paul K R Dasari
- Department of Radiology, Division of Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Arda Könik
- Department of Radiology, Division of Nuclear Medicine, University of Massachusetts Medical School, Worcester, MA, 01655, USA
| | - P Hendrik Pretorius
- Department of Radiology, Division of Nuclear Medicine, University of Massachusetts Medical School, Worcester, MA, 01655, USA
| | - Karen L Johnson
- Department of Radiology, Division of Nuclear Medicine, University of Massachusetts Medical School, Worcester, MA, 01655, USA
| | - William P Segars
- Department of Radiology, Carl E. Ravin Advanced Imaging Laboratory, Duke University Medical Center, Durham, NC, 27705, USA
| | - Mohammed S Shazeeb
- Department of Radiology, Division of Nuclear Medicine, University of Massachusetts Medical School, Worcester, MA, 01655, USA.,Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA
| | - Michael A King
- Department of Radiology, Division of Nuclear Medicine, University of Massachusetts Medical School, Worcester, MA, 01655, USA
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