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Resch S, Ziegler SI, Sheikh G, Unterrainer LM, Zacherl MJ, Bartenstein P, Böning G, Brosch-Lenz J, Delker A. Impact of the Reference Multiple-Time-Point Dosimetry Protocol on the Validity of Single-Time-Point Dosimetry for [ 177Lu]Lu-PSMA-I&T Therapy. J Nucl Med 2024; 65:1272-1278. [PMID: 38936975 PMCID: PMC11294067 DOI: 10.2967/jnumed.123.266871] [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: 10/17/2023] [Accepted: 04/22/2024] [Indexed: 06/29/2024] Open
Abstract
Internal dosimetry supports safe and effective patient management during radionuclide therapy. Yet, it is associated with high clinical workload, costs, and patient burden, as patient scans at multiple time points (MTPs) must be acquired. Dosimetry based on imaging at a single time point (STP) has continuously gained popularity. However, MTP protocols, used as a reference to judge the validity of STP dosimetry, differ depending on local requirements and deviate from the unknown patient-specific ground truth pharmacokinetics. The aim of this study was to compare the error and optimum time point for different STP approaches using different reference MTP protocols. Methods: Whole-body SPECT/CT scans of 7 patients (7.4-8.9 GBq of [177Lu]Lu-PSMA-I&T) were scheduled at 24, 48, 72, and 168 h after injection. Sixty lesions, 14 kidneys, and 10 submandibular glands were delineated in the SPECT/CT data. Two curve models, that is, a mono- and a biexponential model, were fitted to the MTP data, in accordance with goodness-of-fit analysis (coefficients of variation, sum of squared errors). Three population-based STP approaches were compared: one method published by Hänscheid et al., one by Jackson et al., and one using population-based effective half-lives in the mono- or biexponential curve models. Percentage differences between STP and MTP dosimetry were evaluated. Results: Goodness-of-fit parameters show that a monoexponential function and a biexponential function with shared population-based parameters and physical tail are reasonable reference models. When comparing both reference models, we observed maximum differences of -44%, -19%, and -28% in the estimated absorbed doses for lesions, kidneys, and salivary glands, respectively. STP dosimetry with an average deviation of less than 10% from MTP dosimetry may be feasible; however, this deviation and the optimum imaging time point showed a dependence on the chosen reference protocol. Conclusion: STP dosimetry for [177Lu]Lu-PSMA therapy is promising to boost the integration of dosimetry into clinical routine. According to our patient cohort, 48 h after injection may be regarded as a compromise for STP dosimetry for lesions and at-risk organs. The results from this analysis show that a common gold standard for dosimetry is desirable to allow for reliable and comparable STP dosimetry.
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Affiliation(s)
- Sandra Resch
- Department of Nuclear Medicine, LMU University Hospital, LMU, Munich, Germany;
| | - Sibylle I Ziegler
- Department of Nuclear Medicine, LMU University Hospital, LMU, Munich, Germany
| | - Gabriel Sheikh
- Department of Nuclear Medicine, LMU University Hospital, LMU, Munich, Germany
| | - Lena M Unterrainer
- Department of Nuclear Medicine, LMU University Hospital, LMU, Munich, Germany
- Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, UCLA, Los Angeles, California; and
| | - Mathias J Zacherl
- Department of Nuclear Medicine, LMU University Hospital, LMU, Munich, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, LMU University Hospital, LMU, Munich, Germany
| | - Guido Böning
- Department of Nuclear Medicine, LMU University Hospital, LMU, Munich, Germany
| | - Julia Brosch-Lenz
- Department of Nuclear Medicine, Technical University of Munich, Munich, Germany
| | - Astrid Delker
- Department of Nuclear Medicine, LMU University Hospital, LMU, Munich, Germany
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Yazdani E, Asadi M, Geramifar P, Karamzade-Ziarati N, Vosoughi H, Kazemi-Jahromi M, Sadeghi M. A step toward simplified dosimetry of radiopharmaceutical therapy via SPECT frame duration reduction. Appl Radiat Isot 2024; 210:111378. [PMID: 38820867 DOI: 10.1016/j.apradiso.2024.111378] [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: 01/11/2024] [Revised: 05/09/2024] [Accepted: 05/26/2024] [Indexed: 06/02/2024]
Abstract
Despite being time-consuming, SPECT/CT data is necessary for accurate dosimetry in patient-specific radiopharmaceutical therapy. We investigated how reducing the frame duration (FD) during SPECT acquisition can simplify the dosimetry workflow for [177Lu]Lu-PSMA radioligand therapy (RLT). We aimed to determine the impact of shortened acquisition times on dosimetric precision. Three SPECT scans with FD of 20, 10, and 5 second/frame (sec/fr) were obtained 48 h post-RLT from one metastatic castration-resistant prostate cancer (mCRPC) patient's pelvis. Planar images at 4, 48, and 72 h post-therapy were used to calculate time-integrated activities (TIAs). Using accurate activity calibrations and GATE Monte Carlo (MC) dosimetry, absorbed doses in tumor lesions and kidneys were estimated. Dosimetry precision was assessed by comparing shorter FD results to the 20 sec/fr reference using relative percentage difference (RPD). We observed consistent calibration factors (CFs) across different FDs. Using the same CF, we obtained marginal RPD deviations less than 4% for the right kidney and tumor lesions and less than 7% for the left kidney. By reducing FD, simulation time was slightly decreased. This study shows we can shorten SPECT acquisition time in RLT dosimetry by reducing FD without sacrificing dosimetry accuracy. These findings pave the way for streamlined personalized internal dosimetry workflows.
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Affiliation(s)
- Elmira Yazdani
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mahboobeh Asadi
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran.
| | | | - Habibeh Vosoughi
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahmood Kazemi-Jahromi
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mahdi Sadeghi
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran, Iran.
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Zhao X, Jakobsson V, Tao Y, Zhao T, Wang J, Khong PL, Chen X, Zhang J. Targeted Radionuclide Therapy in Glioblastoma. ACS APPLIED MATERIALS & INTERFACES 2024. [PMID: 39042829 DOI: 10.1021/acsami.4c07850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2024]
Abstract
Despite the development of various novel therapies, glioblastoma (GBM) remains a devastating disease, with a median survival of less than 15 months. Recently, targeted radionuclide therapy has shown significant progress in treating solid tumors, with the approval of Lutathera for neuroendocrine tumors and Pluvicto for prostate cancer by the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). This achievement has shed light on the potential of targeted radionuclide therapy for other solid tumors, including GBM. This review presents the current status of targeted radionuclide therapy in GBM, highlighting the commonly used therapeutic radionuclides emitting alpha, beta particles, and Auger electrons that could induce potent molecular and cellular damage to treat GBM. We then explore a range of targeting vectors, including small molecules, peptides, and antibodies, which selectively target antigen-expressing tumor cells with minimal or no binding to healthy tissues. Considering that radiopharmaceuticals for GBM are often administered locoregionally to bypass the blood-brain barrier (BBB), we review prominent delivery methods such as convection-enhanced delivery, local implantation, and stereotactic injections. Finally, we address the challenges of this therapeutic approach for GBM and propose potential solutions.
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Affiliation(s)
- Xiaobin Zhao
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
- Theranostics Center of Excellence, Yong Loo Lin School of Medicine, National University of Singapore, 11 Biopolis Way, Helios, Singapore 138667, Singapore
- Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Vivianne Jakobsson
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
- Theranostics Center of Excellence, Yong Loo Lin School of Medicine, National University of Singapore, 11 Biopolis Way, Helios, Singapore 138667, Singapore
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Yucen Tao
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Theranostics Center of Excellence, Yong Loo Lin School of Medicine, National University of Singapore, 11 Biopolis Way, Helios, Singapore 138667, Singapore
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Tianzhi Zhao
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
- Theranostics Center of Excellence, Yong Loo Lin School of Medicine, National University of Singapore, 11 Biopolis Way, Helios, Singapore 138667, Singapore
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Jingyan Wang
- Xiamen University, School of Public Health, Xiang'an South Road, Xiamen 361102, China
| | - Pek-Lan Khong
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
| | - Xiaoyuan Chen
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
- Theranostics Center of Excellence, Yong Loo Lin School of Medicine, National University of Singapore, 11 Biopolis Way, Helios, Singapore 138667, Singapore
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
- Departments of Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Singapore
| | - Jingjing Zhang
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
- Theranostics Center of Excellence, Yong Loo Lin School of Medicine, National University of Singapore, 11 Biopolis Way, Helios, Singapore 138667, Singapore
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
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Gustafsson J, Larsson E, Ljungberg M, Sjögreen Gleisner K. Pareto optimization of SPECT acquisition and reconstruction settings for 177Lu activity quantification. EJNMMI Phys 2024; 11:62. [PMID: 39004644 PMCID: PMC11247071 DOI: 10.1186/s40658-024-00667-7] [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: 02/15/2024] [Accepted: 07/05/2024] [Indexed: 07/16/2024] Open
Abstract
BACKGROUND The aim was to investigate the noise and bias properties of quantitative 177Lu-SPECT with respect to the number of projection angles, and the number of subsets and iterations in the OS-EM reconstruction, for different total acquisition times. METHODS Experimental SPECT acquisition of six spheres in a NEMA body phantom filled with 177Lu was performed, using medium-energy collimators and 120 projections with 180 s per projection. Bootstrapping was applied to generate data sets representing acquisitions with 20 to 120 projections for 10 min, 20 min, and 40 min, with 32 noise realizations per setting. Monte Carlo simulations were performed of 177Lu-DOTA-TATE in an anthropomorphic computer phantom with three tumours (2.8 mL to 40.0 mL). Projections representing 24 h and 168 h post administration were simulated, each with 32 noise realizations. Images were reconstructed using OS-EM with compensation for attenuation, scatter, and distance-dependent resolution. The number of subsets and iterations were varied within a constrained range of the product number of iterations × number of projections ≤ 2400 . Volumes-of-interest were defined following the physical size of the spheres and tumours, the mean activity-concentrations estimated, and the absolute mean relative error and coefficient of variation (CV) over noise realizations calculated. Pareto fronts were established by analysis of CV versus mean relative error. RESULTS Points at the Pareto fronts with low CV and high mean error resulted from using a low number of subsets, whilst points at the Pareto fronts associated with high CV but low mean error resulted from reconstructions with a high number of subsets. The number of projection angles had limited impact. CONCLUSIONS For accurate estimation of the 177Lu activity-concentration from SPECT images, the number of projection angles has limited importance, whilst the total acquisition time and the number of subsets and iterations are parameters of importance.
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Affiliation(s)
| | - Erik Larsson
- Radiation Physics, Skåne University Hospital, Lund, Sweden
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5
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Liubchenko G, Böning G, Zacherl M, Rumiantcev M, Unterrainer LM, Gildehaus FJ, Brendel M, Resch S, Bartenstein P, Ziegler SI, Delker A. Image-based dosimetry for [ 225Ac]Ac-PSMA-I&T therapy and the effect of daughter-specific pharmacokinetics. Eur J Nucl Med Mol Imaging 2024; 51:2504-2514. [PMID: 38512484 PMCID: PMC11178588 DOI: 10.1007/s00259-024-06681-2] [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: 09/29/2023] [Accepted: 03/10/2024] [Indexed: 03/23/2024]
Abstract
PURPOSE Although 221Fr and 213Bi have sufficient gamma emission probabilities, quantitative SPECT after [225Ac]Ac-PSMA-I&T therapy remains challenging due to low therapeutic activities. Furthermore, 221Fr and 213Bi may underlie a different pharmacokinetics due to alpha recoil. We conducted a quantitative SPECT study and a urine analysis to investigate the pharmacokinetics of 221Fr and 213Bi and the impact on image-based lesion and kidney dosimetry. METHODS Five patients (7.7 ± 0.2 MBq [225Ac]Ac-PSMA-I&T) underwent an abdominal SPECT/CT (1 h) at 24 and 48 h (Siemens Symbia T2, high-energy collimator, 440 keV/218 keV (width 20%), 78 keV (width 50%)). Quantitative SPECT was reconstructed using MAP-EM with attenuation and transmission-dependent scatter corrections and resolution modelling. Time-activity curves for kidneys (CT-based) and lesions (80% isocontour 24 h) were fitted mono-exponentially. Urine samples collected along with each SPECT/CT were measured in a gamma counter until secular equilibrium was reached. RESULTS Mean kidney and lesion effective half-lives were as follows: 213Bi, 27 ± 6/38 ± 10 h; 221Fr, 24 ± 6/38 ± 11 h; 78 keV, 23 ± 7/39 ± 13 h. The 213Bi-to-221Fr kidney SUV ratio increased by an average of 9% from 24 to 48 h. Urine analysis revealed an increasing 213Bi-to-225Ac ratio (24 h, 0.98 ± 0.15; 48 h, 1.08 ± 0.09). Mean kidney and lesion absorbed doses were 0.17 ± 0.06 and 0.36 ± 0.1 Sv RBE = 5 /MBq using 221Fr and 213Bi SPECT images, compared to 0.16 ± 0.05/0.18 ± 0.06 and 0.36 ± 0.1/0.38 ± 0.1 Sv RBE = 5 /MBq considering either the 221Fr or 213Bi SPECT. CONCLUSION SPECT/CT imaging and urine analysis showed minor differences of up to 10% in the daughter-specific pharmacokinetics. These variances had a minimal impact on the lesion and kidney dosimetry which remained within 8%.
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Affiliation(s)
- Grigory Liubchenko
- Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Marchioninstrasse 15, 81377, Munich, Germany.
| | - Guido Böning
- Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Marchioninstrasse 15, 81377, Munich, Germany
| | - Mathias Zacherl
- Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Marchioninstrasse 15, 81377, Munich, Germany
| | - Mikhail Rumiantcev
- Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Marchioninstrasse 15, 81377, Munich, Germany
| | - Lena M Unterrainer
- Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Marchioninstrasse 15, 81377, Munich, Germany
- Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, CA, USA
| | - Franz Josef Gildehaus
- Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Marchioninstrasse 15, 81377, Munich, Germany
| | - Matthias Brendel
- Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Marchioninstrasse 15, 81377, Munich, Germany
- SyNergy, University of Munich, Munich, Germany
- DZNE - German Center for Neurodegenerative Diseases, Munich, Germany
| | - Sandra Resch
- Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Marchioninstrasse 15, 81377, Munich, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Marchioninstrasse 15, 81377, Munich, Germany
| | - Sibylle I Ziegler
- Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Marchioninstrasse 15, 81377, Munich, Germany
| | - Astrid Delker
- Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Marchioninstrasse 15, 81377, Munich, Germany
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Wikberg E, Essen MV, Rydén T, Svensson J, Gjertsson P, Bernhardt P. Improvements of 177Lu SPECT images from sparsely acquired projections by reconstruction with deep-learning-generated synthetic projections. EJNMMI Phys 2024; 11:53. [PMID: 38941040 PMCID: PMC11213840 DOI: 10.1186/s40658-024-00655-x] [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: 01/22/2024] [Accepted: 06/03/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND For dosimetry, the demand for whole-body SPECT/CT imaging, which require long acquisition durations with dual-head Anger cameras, is increasing. Here we evaluated sparsely acquired projections and assessed whether the addition of deep-learning-generated synthetic intermediate projections (SIPs) could improve the image quality while preserving dosimetric accuracy. METHODS This study included 16 patients treated with 177Lu-DOTATATE with SPECT/CT imaging (120 projections, 120P) at four time points. Deep neural networks (CUSIPs) were designed and trained to compile 90 SIPs from 30 acquired projections (30P). The 120P, 30P, and three different CUSIP sets (30P + 90 SIPs) were reconstructed using Monte Carlo-based OSEM reconstruction (yielding 120P_rec, 30P_rec, and CUSIP_recs). The noise levels were visually compared. Quantitative measures of normalised root mean square error, normalised mean absolute error, peak signal-to-noise ratio, and structural similarity were evaluated, and kidney and bone marrow absorbed doses were estimated for each reconstruction set. RESULTS The use of SIPs visually improved noise levels. All quantitative measures demonstrated high similarity between CUSIP sets and 120P. Linear regression showed nearly perfect concordance of the kidney and bone marrow absorbed doses for all reconstruction sets, compared to the doses of 120P_rec (R2 ≥ 0.97). Compared to 120P_rec, the mean relative difference in kidney absorbed dose, for all reconstruction sets, was within 3%. For bone marrow absorbed doses, there was a higher dissipation in relative differences, and CUSIP_recs outperformed 30P_rec in mean relative difference (within 4% compared to 9%). Kidney and bone marrow absorbed doses for 30P_rec were statistically significantly different from those of 120_rec, as opposed to the absorbed doses of the best performing CUSIP_rec, where no statistically significant difference was found. CONCLUSION When performing SPECT/CT reconstruction, the use of SIPs can substantially reduce acquisition durations in SPECT/CT imaging, enabling acquisition of multiple fields of view of high image quality with satisfactory dosimetric accuracy.
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Affiliation(s)
- Emma Wikberg
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
- Medical Physics and Medical Bioengineering, Sahlgrenska University Hospital, Gothenburg, Sweden.
| | - Martijn van Essen
- Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Tobias Rydén
- Medical Physics and Medical Bioengineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Johanna Svensson
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Peter Gjertsson
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Peter Bernhardt
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Medical Physics and Medical Bioengineering, Sahlgrenska University Hospital, Gothenburg, Sweden
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Leube J, Gustafsson J, Lassmann M, Salas-Ramirez M, Tran-Gia J. A Deep-Learning-Based Partial-Volume Correction Method for Quantitative 177Lu SPECT/CT Imaging. J Nucl Med 2024; 65:980-987. [PMID: 38637141 PMCID: PMC11149598 DOI: 10.2967/jnumed.123.266889] [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: 10/27/2023] [Revised: 03/15/2024] [Indexed: 04/20/2024] Open
Abstract
With the development of new radiopharmaceutical therapies, quantitative SPECT/CT has progressively emerged as a crucial tool for dosimetry. One major obstacle of SPECT is its poor resolution, which results in blurring of the activity distribution. Especially for small objects, this so-called partial-volume effect limits the accuracy of activity quantification. Numerous methods for partial-volume correction (PVC) have been proposed, but most methods have the disadvantage of assuming a spatially invariant resolution of the imaging system, which does not hold for SPECT. Furthermore, most methods require a segmentation based on anatomic information. Methods: We introduce DL-PVC, a methodology for PVC of 177Lu SPECT/CT imaging using deep learning (DL). Training was based on a dataset of 10,000 random activity distributions placed in extended cardiac-torso body phantoms. Realistic SPECT acquisitions were created using the SIMIND Monte Carlo simulation program. SPECT reconstructions without and with resolution modeling were performed using the CASToR and STIR reconstruction software, respectively. The pairs of ground-truth activity distributions and simulated SPECT images were used for training various U-Nets. Quantitative analysis of the performance of these U-Nets was based on metrics such as the structural similarity index measure or normalized root-mean-square error, but also on volume activity accuracy, a new metric that describes the fraction of voxels in which the determined activity concentration deviates from the true activity concentration by less than a certain margin. On the basis of this analysis, the optimal parameters for normalization, input size, and network architecture were identified. Results: Our simulation-based analysis revealed that DL-PVC (0.95/7.8%/35.8% for structural similarity index measure/normalized root-mean-square error/volume activity accuracy) outperforms SPECT without PVC (0.89/10.4%/12.1%) and after iterative Yang PVC (0.94/8.6%/15.1%). Additionally, we validated DL-PVC on 177Lu SPECT/CT measurements of 3-dimensionally printed phantoms of different geometries. Although DL-PVC showed activity recovery similar to that of the iterative Yang method, no segmentation was required. In addition, DL-PVC was able to correct other image artifacts such as Gibbs ringing, making it clearly superior at the voxel level. Conclusion: In this work, we demonstrate the added value of DL-PVC for quantitative 177Lu SPECT/CT. Our analysis validates the functionality of DL-PVC and paves the way for future deployment on clinical image data.
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Affiliation(s)
- Julian Leube
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany; and
| | | | - Michael Lassmann
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany; and
| | - Maikol Salas-Ramirez
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany; and
| | - Johannes Tran-Gia
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany; and
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Yazdani E, Geramifar P, Karamzade-Ziarati N, Sadeghi M, Amini P, Rahmim A. Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens. Diagnostics (Basel) 2024; 14:181. [PMID: 38248059 PMCID: PMC10814892 DOI: 10.3390/diagnostics14020181] [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: 11/23/2023] [Revised: 01/11/2024] [Accepted: 01/12/2024] [Indexed: 01/23/2024] Open
Abstract
Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.
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Affiliation(s)
- Elmira Yazdani
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
- Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran 14117-13135, Iran; (P.G.); (N.K.-Z.)
| | - Najme Karamzade-Ziarati
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran 14117-13135, Iran; (P.G.); (N.K.-Z.)
| | - Mahdi Sadeghi
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
- Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Payam Amini
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC V5Z 1L3, Canada
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Brosch-Lenz JF, Delker A, Schmidt F, Tran-Gia J. On the Use of Artificial Intelligence for Dosimetry of Radiopharmaceutical Therapies. Nuklearmedizin 2023; 62:379-388. [PMID: 37827503 DOI: 10.1055/a-2179-6872] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Routine clinical dosimetry along with radiopharmaceutical therapies is key for future treatment personalization. However, dosimetry is considered complex and time-consuming with various challenges amongst the required steps within the dosimetry workflow. The general workflow for image-based dosimetry consists of quantitative imaging, the segmentation of organs and tumors, fitting of the time-activity-curves, and the conversion to absorbed dose. This work reviews the potential and advantages of the use of artificial intelligence to improve speed and accuracy of every single step of the dosimetry workflow.
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Affiliation(s)
| | - Astrid Delker
- Department of Nuclear Medicine, LMU University Hospital, Munich, Germany
| | - Fabian Schmidt
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, Tuebingen, Germany
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Tuebingen, Germany
| | - Johannes Tran-Gia
- Department of Nuclear Medicine, University Hospital Wuerzburg, Wuerzburg, Germany
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10
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Wikberg E, van Essen M, Rydén T, Svensson J, Gjertsson P, Bernhardt P. Evaluation of reconstruction methods and image noise levels concerning visual assessment of simulated liver lesions in 111In-octreotide SPECT imaging. EJNMMI Phys 2023; 10:36. [PMID: 37266738 DOI: 10.1186/s40658-023-00557-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 05/15/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Early cancer detection is crucial for patients' survival. The image quality in 111In-octreotide SPECT imaging could be improved by using Monte Carlo (MC)-based reconstruction. The aim of this observational study was to determine the detection rate of simulated liver lesions for MC-based ordered subset expectation maximization (OSEM) reconstruction compared to conventional attenuation-corrected OSEM reconstruction. METHODS Thirty-seven SPECT/CT examinations with 111In-octreotide were randomly selected. The inclusion criterion was no liver lesions at the time of examination and for the following 3 years. SPECT images of spheres representing lesions were simulated using MC. The raw data of the spheres were added to the raw data of the established healthy patients in 26 of the examinations, and the remaining 11 examinations were not modified. The images were reconstructed using conventional OSEM reconstruction with attenuation correction and post filtering (fAC OSEM) and MC-based OSEM reconstruction without and with post filtering (MC OSEM and fMC OSEM, respectively). The images were visually and blindly evaluated by a nuclear medicine specialist. The criteria evaluated were liver lesion yes or no, including coordinates if yes, with confidence level 1-3. The percentage of detected lesions and accuracy (percentage of correctly classified cases), as well as tumor-to-normal tissue concentration (TNC) ratios and signal-to-noise ratios (SNRs), were evaluated. RESULTS The detection rates were 30.8% for fAC OSEM, 42.3% for fMC OSEM, and 50.0% for MC OSEM. The accuracies were 45.9% for fAC OSEM, 45.9% for fMC OSEM, and 54.1% for MC OSEM. The number of false positives was higher for fMC and MC OSEM. The observer's confidence level was higher in filtered images than in unfiltered images. TNC ratios were significantly higher, statistically, with MC OSEM and fMC OSEM than with AC OSEM, but SNRs were similar due to higher noise with MC OSEM. CONCLUSION One in two lesions were found using MC OSEM versus one in three using conventional reconstruction. TNC ratios were significantly improved, statistically, using MC-based reconstruction, but the noise levels increased and consequently the confidence level of the observer decreased. For further improvements, image noise needs to be suppressed.
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Affiliation(s)
- Emma Wikberg
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
- Medical Physics and Medical Bioengineering, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden.
| | - Martijn van Essen
- Department of Clinical Physiology, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
| | - Tobias Rydén
- Medical Physics and Medical Bioengineering, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
| | - Johanna Svensson
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Peter Gjertsson
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Physiology, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
| | - Peter Bernhardt
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Medical Physics and Medical Bioengineering, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
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Gear J, Stokke C, Terwinghe C, Gnesin S, Sandström M, Tran-Gia J, Cremonesi M, Cicone F, Verburg F, Hustinx R, Giovanella L, Herrmann K, Gabiña PM. EANM enabling guide: how to improve the accessibility of clinical dosimetry. Eur J Nucl Med Mol Imaging 2023; 50:1861-1868. [PMID: 37086275 PMCID: PMC10287783 DOI: 10.1007/s00259-023-06226-z] [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: 02/11/2023] [Accepted: 04/04/2023] [Indexed: 04/23/2023]
Abstract
Dosimetry can be a useful tool for personalization of molecular radiotherapy (MRT) procedures, enabling the continuous development of theranostic concepts. However, the additional resource requirements are often seen as a barrier to implementation. This guide discusses the requirements for dosimetry and demonstrates how a dosimetry regimen can be tailored to the available facilities of a centre. The aim is to help centres wishing to initiate a dosimetry service but may not have the experience or resources of some of the more established therapy and dosimetry centres. The multidisciplinary approach and different personnel requirements are discussed and key equipment reviewed example protocols demonstrating these factors are given in the supplementary material for the main therapies carried out in nuclear medicine, including [131I]-NaI for benign thyroid disorders, [177Lu]-DOTATATE and 131I-mIBG for neuroendocrine tumours and [90Y]-microspheres for unresectable hepatic carcinoma.
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Affiliation(s)
- Jonathan Gear
- Joint Department of Physics, Royal Marsden NHSFT & Institute of Cancer Research, Sutton, UK.
| | - Caroline Stokke
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
- Department of Physics, University of Oslo, Oslo, Norway
| | - Christelle Terwinghe
- Department of Nuclear Medicine, Universitair Ziekenhuis Leuven, Louvain, Belgium
| | - Silvano Gnesin
- Institute of Radiation Physics, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Mattias Sandström
- Section of Nuclear Medicine and PET, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Sweden & Section of Medical Physics, Department of Immunology, Genetics and Pathology, Uppsala University, 751 85, Uppsala, Sweden
| | - Johannes Tran-Gia
- Department of Nuclear Medicine, University Hospital Würzburg, Oberdürrbacher Str. 6, 97080, Würzburg, Germany
| | - Marta Cremonesi
- Radiation Research Unit, Department of Medical Imaging and Radiation Sciences, Istituto Europeo Di Oncologia, IRCCS, Milan, Italy
| | - Francesco Cicone
- Department of Experimental and Clinical Medicine, Neuroscience Research Centre, PET/RM Unit, "Magna Graecia" University of Catanzaro, Catanzaro, Italy
- Nuclear Medicine Unit, University Hospital "Mater Domini, Catanzaro, Italy
| | - Fredrik Verburg
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
- GIGA-CRC in Vivo Imaging, University of Liège, Liège, Belgium
| | - Luca Giovanella
- Clinic for Nuclear Medicine and Molecular Imaging, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
| | - Ken Herrmann
- Department of Nuclear Medicine, University of Duisburg-Essen, Duisburg, Germany
- German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Pablo Minguez Gabiña
- Department of Medical Physics and Radiation Protection, Gurutzeta-Cruces University Hospital/Biocruces Health Research Institute, Barakaldo, Spain
- Department of Applied Physics, Faculty of Engineering, UPV/EHU, Bilbao, Spain
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Xie H, Liu Z, Shi L, Greco K, Chen X, Zhou B, Feher A, Stendahl JC, Boutagy N, Kyriakides TC, Wang G, Sinusas AJ, Liu C. Segmentation-Free PVC for Cardiac SPECT Using a Densely-Connected Multi-Dimensional Dynamic Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:1325-1336. [PMID: 36459599 PMCID: PMC10204821 DOI: 10.1109/tmi.2022.3226604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
In nuclear imaging, limited resolution causes partial volume effects (PVEs) that affect image sharpness and quantitative accuracy. Partial volume correction (PVC) methods incorporating high-resolution anatomical information from CT or MRI have been demonstrated to be effective. However, such anatomical-guided methods typically require tedious image registration and segmentation steps. Accurately segmented organ templates are also hard to obtain, particularly in cardiac SPECT imaging, due to the lack of hybrid SPECT/CT scanners with high-end CT and associated motion artifacts. Slight mis-registration/mis-segmentation would result in severe degradation in image quality after PVC. In this work, we develop a deep-learning-based method for fast cardiac SPECT PVC without anatomical information and associated organ segmentation. The proposed network involves a densely-connected multi-dimensional dynamic mechanism, allowing the convolutional kernels to be adapted based on the input images, even after the network is fully trained. Intramyocardial blood volume (IMBV) is introduced as an additional clinical-relevant loss function for network optimization. The proposed network demonstrated promising performance on 28 canine studies acquired on a GE Discovery NM/CT 570c dedicated cardiac SPECT scanner with a 64-slice CT using Technetium-99m-labeled red blood cells. This work showed that the proposed network with densely-connected dynamic mechanism produced superior results compared with the same network without such mechanism. Results also showed that the proposed network without anatomical information could produce images with statistically comparable IMBV measurements to the images generated by anatomical-guided PVC methods, which could be helpful in clinical translation.
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13
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Automation: A revolutionary vision of artificial intelligence in theranostics. Bull Cancer 2023; 110:233-241. [PMID: 36509576 DOI: 10.1016/j.bulcan.2022.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 10/12/2022] [Accepted: 10/26/2022] [Indexed: 12/13/2022]
Abstract
The last two decades have witnessed an extraordinary evolution of automation and artificial intelligence (AI), which has become an integral part of our daily lives. Lately, AI has also been assimilated in the field of medicine to upgrade overall healthcare system and encourage personalized treatment. Theranostics literally meaning combination of diagnosis and therapeutics, is a targeted pharmacotherapy, based on specific targeted diagnostic tests. Numerous theranostic agents/biomarkers are available which can identify the most beneficial treatment, correct dose or predict response to a medicine, thus, maximizing drug efficacy, minimizing toxicity and providing informed treatment choice. For instance, a statistics based Cluster-FLIM technology provides precise data on drug-receptor binding behavior in biological tissues using fluorescence real experimental imaging. Automated Idylla™ qPCR System is another approach in oncology to determine the EGFR mutations at initial stage as well as during the treatment and also assists the oncologist in designing the treatment protocol. Recent incorporation of automation and AI in theranostics has brought a drastic change in early detection and treatment protocols for various diseases such as cancer and diabetes. Also, it leads to quick analysis of number of diverse experimental datum with accuracy. The approach mainly uses computer algorithms to unveil relevant and significant information from clinical data, thereby assisting in making accurate, logical and pertinent decisions. This review highlights the emerging uses/role of automation and AI in theranostics, technical difficulties and focuses on its future prospects to facilitate a patient specific, reliable and efficient pharmacotherapy.
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Xie H, Thorn S, Chen X, Zhou B, Liu H, Liu Z, Lee S, Wang G, Liu YH, Sinusas AJ, Liu C. Increasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction. J Nucl Cardiol 2023; 30:86-100. [PMID: 35508796 DOI: 10.1007/s12350-022-02972-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 03/18/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND The GE Discovery NM (DNM) 530c/570c are dedicated cardiac SPECT scanners with 19 detector modules designed for stationary imaging. This study aims to incorporate additional projection angular sampling to improve reconstruction quality. A deep learning method is also proposed to generate synthetic dense-view image volumes from few-view counterparts. METHODS By moving the detector array, a total of four projection angle sets were acquired and combined for image reconstructions. A deep neural network is proposed to generate synthetic four-angle images with 76 ([Formula: see text]) projections from corresponding one-angle images with 19 projections. Simulated data, pig, physical phantom, and human studies were used for network training and evaluation. Reconstruction results were quantitatively evaluated using representative image metrics. The myocardial perfusion defect size of different subjects was quantified using an FDA-cleared clinical software. RESULTS Multi-angle reconstructions and network results have higher image resolution, improved uniformity on normal myocardium, more accurate defect quantification, and superior quantitative values on all the testing data. As validated against cardiac catheterization and diagnostic results, deep learning results showed improved image quality with better defect contrast on human studies. CONCLUSION Increasing angular sampling can substantially improve image quality on DNM, and deep learning can be implemented to improve reconstruction quality in case of stationary imaging.
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Affiliation(s)
- Huidong Xie
- Department of Biomedical Engineering, Yale University, 801 Howard Avenue, New Haven, CT, 06520, USA
| | - Stephanie Thorn
- Department of Internal Medicine (Cardiology), Yale University, New Haven, CT, USA
| | - Xiongchao Chen
- Department of Biomedical Engineering, Yale University, 801 Howard Avenue, New Haven, CT, 06520, USA
| | - Bo Zhou
- Department of Biomedical Engineering, Yale University, 801 Howard Avenue, New Haven, CT, 06520, USA
| | - Hui Liu
- Department of Radiology and Biomedical Imaging, Yale University, 801 Howard Avenue, New Haven, CT, 06520, USA
- Department of Engineering Physics, Tsinghua University, Beijing, China
| | - Zhao Liu
- Department of Radiology and Biomedical Imaging, Yale University, 801 Howard Avenue, New Haven, CT, 06520, USA
| | - Supum Lee
- Department of Internal Medicine (Cardiology), Yale University, New Haven, CT, USA
| | - Ge Wang
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Yi-Hwa Liu
- Department of Internal Medicine (Cardiology), Yale University, New Haven, CT, USA
- Department of Biomedical Imaging and Radiological Sciences, School of Biomedical Science and Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Albert J Sinusas
- Department of Biomedical Engineering, Yale University, 801 Howard Avenue, New Haven, CT, 06520, USA
- Department of Internal Medicine (Cardiology), Yale University, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale University, 801 Howard Avenue, New Haven, CT, 06520, USA
| | - Chi Liu
- Department of Biomedical Engineering, Yale University, 801 Howard Avenue, New Haven, CT, 06520, USA.
- Department of Radiology and Biomedical Imaging, Yale University, 801 Howard Avenue, New Haven, CT, 06520, USA.
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15
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Chen X, Zhou B, Xie H, Miao T, Liu H, Holler W, Lin M, Miller EJ, Carson RE, Sinusas AJ, Liu C. DuDoSS: Deep-learning-based dual-domain sinogram synthesis from sparsely sampled projections of cardiac SPECT. Med Phys 2023; 50:89-103. [PMID: 36048541 PMCID: PMC9868054 DOI: 10.1002/mp.15958] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 08/04/2022] [Accepted: 08/19/2022] [Indexed: 01/26/2023] Open
Abstract
PURPOSE Myocardial perfusion imaging (MPI) using single-photon emission-computed tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases. In clinical practice, the long scanning procedures and acquisition time might induce patient anxiety and discomfort, motion artifacts, and misalignments between SPECT and computed tomography (CT). Reducing the number of projection angles provides a solution that results in a shorter scanning time. However, fewer projection angles might cause lower reconstruction accuracy, higher noise level, and reconstruction artifacts due to reduced angular sampling. We developed a deep-learning-based approach for high-quality SPECT image reconstruction using sparsely sampled projections. METHODS We proposed a novel deep-learning-based dual-domain sinogram synthesis (DuDoSS) method to recover full-view projections from sparsely sampled projections of cardiac SPECT. DuDoSS utilized the SPECT images predicted in the image domain as guidance to generate synthetic full-view projections in the sinogram domain. The synthetic projections were then reconstructed into non-attenuation-corrected and attenuation-corrected (AC) SPECT images for voxel-wise and segment-wise quantitative evaluations in terms of normalized mean square error (NMSE) and absolute percent error (APE). Previous deep-learning-based approaches, including direct sinogram generation (Direct Sino2Sino) and direct image prediction (Direct Img2Img), were tested in this study for comparison. The dataset used in this study included a total of 500 anonymized clinical stress-state MPI studies acquired on a GE NM/CT 850 scanner with 60 projection angles following the injection of 99m Tc-tetrofosmin. RESULTS Our proposed DuDoSS generated more consistent synthetic projections and SPECT images with the ground truth than other approaches. The average voxel-wise NMSE between the synthetic projections by DuDoSS and the ground-truth full-view projections was 2.08% ± 0.81%, as compared to 2.21% ± 0.86% (p < 0.001) by Direct Sino2Sino. The averaged voxel-wise NMSE between the AC SPECT images by DuDoSS and the ground-truth AC SPECT images was 1.63% ± 0.72%, as compared to 1.84% ± 0.79% (p < 0.001) by Direct Sino2Sino and 1.90% ± 0.66% (p < 0.001) by Direct Img2Img. The averaged segment-wise APE between the AC SPECT images by DuDoSS and the ground-truth AC SPECT images was 3.87% ± 3.23%, as compared to 3.95% ± 3.21% (p = 0.023) by Direct Img2Img and 4.46% ± 3.58% (p < 0.001) by Direct Sino2Sino. CONCLUSIONS Our proposed DuDoSS is feasible to generate accurate synthetic full-view projections from sparsely sampled projections for cardiac SPECT. The synthetic projections and reconstructed SPECT images generated from DuDoSS are more consistent with the ground-truth full-view projections and SPECT images than other approaches. DuDoSS can potentially enable fast data acquisition of cardiac SPECT.
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Affiliation(s)
- Xiongchao Chen
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, United States, 06511
| | - Bo Zhou
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, United States, 06511
| | - Huidong Xie
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, United States, 06511
| | - Tianshun Miao
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, United States, 06511
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, United States, 06511
| | - Hui Liu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, United States, 06511
| | | | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, United States, 06511
- Visage Imaging, Inc., San Diego, California, United States, 92130
| | - Edward J. Miller
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, United States, 06511
- Department of Internal Medicine (Cardiology), Yale University School of Medicine, New Haven, Connecticut, United States, 06511
| | - Richard E. Carson
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, United States, 06511
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, United States, 06511
| | - Albert J. Sinusas
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, United States, 06511
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, United States, 06511
- Department of Internal Medicine (Cardiology), Yale University School of Medicine, New Haven, Connecticut, United States, 06511
| | - Chi Liu
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, United States, 06511
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, United States, 06511
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Leube J, Gustafsson J, Lassmann M, Salas-Ramirez M, Tran-Gia J. Analysis of a deep learning-based method for generation of SPECT projections based on a large Monte Carlo simulated dataset. EJNMMI Phys 2022; 9:47. [PMID: 35852673 PMCID: PMC9296746 DOI: 10.1186/s40658-022-00476-w] [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: 04/04/2022] [Accepted: 07/03/2022] [Indexed: 12/05/2022] Open
Abstract
Background In recent years, a lot of effort has been put in the enhancement of medical imaging using artificial intelligence. However, limited patient data in combination with the unavailability of a ground truth often pose a challenge to a systematic validation of such methodologies. The goal of this work was to investigate a recently proposed method for an artificial intelligence-based generation of synthetic SPECT projections, for acceleration of the image acquisition process based on a large dataset of realistic SPECT simulations. Methods A database of 10,000 SPECT projection datasets of heterogeneous activity distributions of randomly placed random shapes was simulated for a clinical SPECT/CT system using the SIMIND Monte Carlo program. Synthetic projections at fixed angular increments from a set of input projections at evenly distributed angles were generated by different u-shaped convolutional neural networks (u-nets). These u-nets differed in noise realization used for the training data, number of input projections, projection angle increment, and number of training/validation datasets. Synthetic projections were generated for 500 test projection datasets for each u-net, and a quantitative analysis was performed using statistical hypothesis tests based on structural similarity index measure and normalized root-mean-squared error. Additional simulations with varying detector orbits were performed on a subset of the dataset to study the effect of the detector orbit on the performance of the methodology. For verification of the results, the u-nets were applied to Jaszczak and NEMA physical phantom data obtained on a clinical SPECT/CT system. Results No statistically significant differences were observed between u-nets trained with different noise realizations. In contrast, a statistically significant deterioration was found for training with a small subset (400 datasets) of the 10,000 simulated projection datasets in comparison with using a large subset (9500 datasets) for training. A good agreement between synthetic (i.e., u-net generated) and simulated projections before adding noise demonstrates a denoising effect. Finally, the physical phantom measurements show that our findings also apply for projections measured on a clinical SPECT/CT system. Conclusion Our study shows the large potential of u-nets for accelerating SPECT/CT imaging. In addition, our analysis numerically reveals a denoising effect when generating synthetic projections with a u-net. Clinically interesting, the methodology has proven robust against camera orbit deviations in a clinically realistic range. Lastly, we found that a small number of training samples (e.g., ~ 400 datasets) may not be sufficient for reliable generalization of the u-net. Supplementary Information The online version contains supplementary material available at 10.1186/s40658-022-00476-w.
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Affiliation(s)
- Julian Leube
- Department of Nuclear Medicine, University of Würzburg, Oberdürrbacher Str. 6, 97080, Würzburg, Germany.
| | - Johan Gustafsson
- Medical Radiation Physics, Lund, Lund University, Skåne University Hospital, Lund, 221 85, Lund, Sweden
| | - Michael Lassmann
- Department of Nuclear Medicine, University of Würzburg, Oberdürrbacher Str. 6, 97080, Würzburg, Germany
| | - Maikol Salas-Ramirez
- Department of Nuclear Medicine, University of Würzburg, Oberdürrbacher Str. 6, 97080, Würzburg, Germany
| | - Johannes Tran-Gia
- Department of Nuclear Medicine, University of Würzburg, Oberdürrbacher Str. 6, 97080, Würzburg, Germany
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Liberini V, Laudicella R, Balma M, Nicolotti DG, Buschiazzo A, Grimaldi S, Lorenzon L, Bianchi A, Peano S, Bartolotta TV, Farsad M, Baldari S, Burger IA, Huellner MW, Papaleo A, Deandreis D. Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics. Eur Radiol Exp 2022; 6:27. [PMID: 35701671 PMCID: PMC9198151 DOI: 10.1186/s41747-022-00282-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 04/20/2022] [Indexed: 11/21/2022] Open
Abstract
In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients’ risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these “big data” in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer.
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Affiliation(s)
- Virginia Liberini
- Medical Physiopathology - A.O.U. Città della Salute e della Scienza di Torino, Division of Nuclear Medicine, Department of Medical Science, University of Torino, 10126, Torino, Italy. .,Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy.
| | - Riccardo Laudicella
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, 8006, Zurich, Switzerland.,Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and of Morpho-Functional Imaging, University of Messina, 98125, Messina, Italy.,Nuclear Medicine Unit, Fondazione Istituto G. Giglio, Ct.da Pietrapollastra Pisciotto, Cefalù, Palermo, Italy
| | - Michele Balma
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy
| | | | - Ambra Buschiazzo
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy
| | - Serena Grimaldi
- Medical Physiopathology - A.O.U. Città della Salute e della Scienza di Torino, Division of Nuclear Medicine, Department of Medical Science, University of Torino, 10126, Torino, Italy
| | - Leda Lorenzon
- Medical Physics Department, Central Bolzano Hospital, 39100, Bolzano, Italy
| | - Andrea Bianchi
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy
| | - Simona Peano
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy
| | | | - Mohsen Farsad
- Nuclear Medicine, Central Hospital Bolzano, 39100, Bolzano, Italy
| | - Sergio Baldari
- Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and of Morpho-Functional Imaging, University of Messina, 98125, Messina, Italy
| | - Irene A Burger
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, 8006, Zurich, Switzerland.,Department of Nuclear Medicine, Kantonsspital Baden, 5004, Baden, Switzerland
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, 8006, Zurich, Switzerland
| | - Alberto Papaleo
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy
| | - Désirée Deandreis
- Medical Physiopathology - A.O.U. Città della Salute e della Scienza di Torino, Division of Nuclear Medicine, Department of Medical Science, University of Torino, 10126, Torino, Italy
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EANM dosimetry committee recommendations for dosimetry of 177Lu-labelled somatostatin-receptor- and PSMA-targeting ligands. Eur J Nucl Med Mol Imaging 2022; 49:1778-1809. [PMID: 35284969 PMCID: PMC9015994 DOI: 10.1007/s00259-022-05727-7] [Citation(s) in RCA: 79] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 02/13/2022] [Indexed: 12/25/2022]
Abstract
The purpose of the EANM Dosimetry Committee is to provide recommendations and guidance to scientists and clinicians on patient-specific dosimetry. Radiopharmaceuticals labelled with lutetium-177 (177Lu) are increasingly used for therapeutic applications, in particular for the treatment of metastatic neuroendocrine tumours using ligands for somatostatin receptors and prostate adenocarcinoma with small-molecule PSMA-targeting ligands. This paper provides an overview of reported dosimetry data for these therapies and summarises current knowledge about radiation-induced side effects on normal tissues and dose-effect relationships for tumours. Dosimetry methods and data are summarised for kidneys, bone marrow, salivary glands, lacrimal glands, pituitary glands, tumours, and the skin in case of radiopharmaceutical extravasation. Where applicable, taking into account the present status of the field and recent evidence in the literature, guidance is provided. The purpose of these recommendations is to encourage the practice of patient-specific dosimetry in therapy with 177Lu-labelled compounds. The proposed methods should be within the scope of centres offering therapy with 177Lu-labelled ligands for somatostatin receptors or small-molecule PSMA.
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Lassmann M, Eberlein U, Gear J, Konijnenberg M, Kunikowska J. Dosimetry for Radiopharmaceutical Therapy: The European Perspective. J Nucl Med 2021; 62:73S-79S. [PMID: 34857624 DOI: 10.2967/jnumed.121.262754] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 10/13/2021] [Indexed: 11/16/2022] Open
Abstract
This review presents efforts in Europe over the last few years with respect to standardization of quantitative imaging and dosimetry and comprises the results of several European research projects on practices regarding radiopharmaceutical therapies (RPTs). Because the European Union has regulatory requirements concerning dosimetry in RPTs, the European Association of Nuclear Medicine released a position paper in 2021 on the use of dosimetry under these requirements. The importance of radiobiology for RPTs is elucidated in another position paper by the European Association of Nuclear Medicine. Furthermore, how dosimetry interacts with clinical requirements is described, with several clinical examples. In the future, more efforts need to be undertaken to increase teaching and standardization efforts and to incorporate radiobiology for further individualizing patient treatment, with the aim of improving the outcome and safety of RPTs.
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Affiliation(s)
- Michael Lassmann
- Department of Nuclear Medicine, University of Würzburg, Würzburg, Germany
| | - Uta Eberlein
- Department of Nuclear Medicine, University of Würzburg, Würzburg, Germany;
| | - Jonathan Gear
- Joint Department of Physics, Royal Marsden NHS Foundation Trust and Institute of Cancer Research, London, United Kingdom
| | - Mark Konijnenberg
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; and
| | - Jolanta Kunikowska
- Nuclear Medicine Department, Medical University of Warsaw, Warsaw, Poland
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Khan J, Rydèn T, Van Essen M, Svensson J, Bernhardt P. ACTIVITY CONCENTRATION ESTIMATION IN AUTOMATED KIDNEY SEGMENTATION BASED ON CONVOLUTION NEURAL NETWORK METHOD FOR 177LU-SPECT/CT KIDNEY DOSIMETRY. RADIATION PROTECTION DOSIMETRY 2021; 195:164-171. [PMID: 34080002 DOI: 10.1093/rpd/ncab079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 04/28/2021] [Accepted: 04/30/2021] [Indexed: 06/12/2023]
Abstract
For 177Lu-DOTATATE treatments, dosimetry based on manual kidney segmentation from computed tomography (CT) is accurate but time consuming and might be affected by misregistration between CT and SPECT images. This study develops a convolution neural network (CNN) for automated kidney segmentation that accurately aligns CT segmented volume of interest (VOI) to the kidneys in SPECT images. The CNN was trained with SPECT/CT images performed over the abdominal area of 137 patients treated with 177Lu-DOTATATE. Activity concentrations in automated and manual segmentations were strongly correlated for both kidneys (r > 0.96, p < 0.01) in the testing cohort (n = 20). The Bland-Altman analyses demonstrated higher accuracy for the CNN segmentation compared to the manual segmented kidneys without VOI adjustment. The CNN demonstrated a potential for accurate kidney segmentation. The CNN was a fast and robust approach for assessment of activity concentrations in SPECT images, and performed equally well as the manual segmentation method.
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Affiliation(s)
- Jehangir Khan
- Department of Medical Physics and Biomedical Engineering (MFT), Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Tobias Rydèn
- Department of Medical Physics and Biomedical Engineering (MFT), Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Martijn Van Essen
- Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Johanna Svensson
- Department of Oncology, Institution of Clinical Sciences, Sahlgrenska Academy at University of Gothenburg, Sweden
| | - Peter Bernhardt
- Department of Medical Physics and Biomedical Engineering (MFT), Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy at University of Gothenburg, Sweden
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Rydén T, Emma W, Van Essen M, Svensson J, Bernhardt P. IMPROVEMENTS OF 111IN SPECT IMAGES RECONSTRUCTED WITH SPARSELY ACQUIRED PROJECTIONS BY DEEP LEARNING GENERATED SYNTHETIC PROJECTIONS. RADIATION PROTECTION DOSIMETRY 2021; 195:152-157. [PMID: 33885130 PMCID: PMC8507466 DOI: 10.1093/rpd/ncab056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 03/16/2021] [Accepted: 03/19/2021] [Indexed: 06/03/2023]
Abstract
The aim was to improve single-photon emission computed tomography (SPECT) quality for sparsely acquired 111In projections by adding deep learning generated synthetic intermediate projections (SIPs). Method: The recently constructed deep convolutional network for generating synthetic intermediate projections (CUSIP) was used for improving 20 sparsely acquired 111In-octreotide SPECTs. Reconstruction was performed with 120 (120P) or 30 (30P) projections, or 120 projections with 90 SIPs generated from 30 projections (30-120SIP). The SPECT reconstructions were performed with attenuation, scatter and collimator response corrections. Postfiltered 30P reconstructed SPECT was also analyzed. Image quality were quantitatively evaluated with root-mean-square error, peak signal-to-noise ratio and structural similarity index metrics. Result: The 30-120SIP reconstructed SPECT had statistically significant improved image quality parameters compared to 30P reconstructed SPECT with and without post filtering. The images visual appearance was similar to slightly filtered 120P SPECTs. Thereby, substantial acquisition time reduction with SIPs seems possible without image quality degradation.
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Affiliation(s)
- T Rydén
- Department of Medical Physics and Bioengineering, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - W Emma
- Department of Medical Physics and Bioengineering, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - M Van Essen
- Department of Clinical Physiology, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - J Svensson
- Department of Oncology, Institution of Clinical Science, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden
| | - P Bernhardt
- Department of Medical Physics and Bioengineering, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
- Department of Medical Radiation Sciences, Institution of Clinical Science, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden
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Wikberg E, van Essen M, Rydén T, Svensson J, Gjertsson P, Bernhardt P. EVALUATION OF THE SPATIAL RESOLUTION IN MONTE CARLO-BASED SPECT/CT RECONSTRUCTION OF 111IN-OCTREOTIDE IMAGES. RADIATION PROTECTION DOSIMETRY 2021; 195:319-326. [PMID: 33885133 PMCID: PMC8507452 DOI: 10.1093/rpd/ncab055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 02/23/2021] [Accepted: 03/30/2021] [Indexed: 06/03/2023]
Abstract
The purpose was to evaluate the spatial resolution in 111In-octreotide single-photom emission computed tomography (SPECT)/computed tomography (CT) imaging following reconstructions with three different ordered subset expectation maximizations (OSEM) reconstruction algorithms; attenuation corrected (AC) OSEM, AC OSEM with resolution recovery (ACRR OSEM) and Monte Carlo-based OSEM reconstruction (MC OSEM). SPECT/CT imaging of a triple line phantom containing 111In in air and water was performed. The spatial resolution, represented by the full width at half maximum (FWHM) of a line profile, was determined for each line, for X and Y direction and for all reconstructions. The mean FWHM was 12.2 mm (±standard deviation [SD] 3.7 mm) for AC OSEM, 9.3 mm (±SD 2.5 mm) for ACRR OSEM and 8.2 mm (±SD 2.0 mm) for MC OSEM. MC-based SPECT/CT reconstruction clearly improves the spatial resolution in 111In-octreotide imaging and since MC simulations can be performed for all photon energies MC OSEM has the potential to improve SPECT/CT imaging overall.
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Affiliation(s)
- Emma Wikberg
- Department of Medical Radiation Sciences, Sahlgrenska Academy, Gothenburg University, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
- Medical Physics and Medical Bioengineering, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
| | - Martijn van Essen
- Department of Clinical Physiology, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
| | - Tobias Rydén
- Medical Physics and Medical Bioengineering, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
| | - Johanna Svensson
- Department of Oncology, Sahlgrenska Academy, Gothenburg University, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
| | - Peter Gjertsson
- Department of Clinical Physiology, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
| | - Peter Bernhardt
- Department of Medical Radiation Sciences, Sahlgrenska Academy, Gothenburg University, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
- Medical Physics and Medical Bioengineering, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
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Brosch-Lenz J, Yousefirizi F, Zukotynski K, Beauregard JM, Gaudet V, Saboury B, Rahmim A, Uribe C. Role of Artificial Intelligence in Theranostics:: Toward Routine Personalized Radiopharmaceutical Therapies. PET Clin 2021; 16:627-641. [PMID: 34537133 DOI: 10.1016/j.cpet.2021.06.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
We highlight emerging uses of artificial intelligence (AI) in the field of theranostics, focusing on its significant potential to enable routine and reliable personalization of radiopharmaceutical therapies (RPTs). Personalized RPTs require patient-specific dosimetry calculations accompanying therapy. Additionally we discuss the potential to exploit biological information from diagnostic and therapeutic molecular images to derive biomarkers for absorbed dose and outcome prediction; toward personalization of therapies. We try to motivate the nuclear medicine community to expand and align efforts into making routine and reliable personalization of RPTs a reality.
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Affiliation(s)
- Julia Brosch-Lenz
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada
| | - Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada
| | - Katherine Zukotynski
- Department of Medicine and Radiology, McMaster University, 1200 Main Street West, Hamilton, Ontario L9G 4X5, Canada
| | - Jean-Mathieu Beauregard
- Department of Radiology and Nuclear Medicine, Cancer Research Centre, Université Laval, 2325 Rue de l'Université, Québec City, Quebec G1V 0A6, Canada; Department of Medical Imaging, Research Center (Oncology Axis), CHU de Québec - Université Laval, 2325 Rue de l'Université, Québec City, Quebec G1V 0A6, Canada
| | - Vincent Gaudet
- Department of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, USA; Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada; Department of Radiology, University of British Columbia, 11th Floor, 2775 Laurel St, Vancouver, British Columbia V5Z 1M9, Canada; Department of Physics, University of British Columbia, 325 - 6224 Agricultural Road, Vancouver, British Columbia V6T 1Z1, Canada
| | - Carlos Uribe
- Department of Radiology, University of British Columbia, 11th Floor, 2775 Laurel St, Vancouver, British Columbia V5Z 1M9, Canada; Department of Functional Imaging, BC Cancer, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada.
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Arabi H, AkhavanAllaf A, Sanaat A, Shiri I, Zaidi H. The promise of artificial intelligence and deep learning in PET and SPECT imaging. Phys Med 2021; 83:122-137. [DOI: 10.1016/j.ejmp.2021.03.008] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 02/18/2021] [Accepted: 03/03/2021] [Indexed: 02/06/2023] Open
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