1
|
Dimitrakopoulou-Strauss A, Pan L, Sachpekidis C. Total Body PET-CT Protocols in Oncology. Semin Nucl Med 2024:S0001-2998(24)00050-3. [PMID: 38851935 DOI: 10.1053/j.semnuclmed.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 05/23/2024] [Accepted: 05/23/2024] [Indexed: 06/10/2024]
Abstract
Recently developed long axial field of view (LAFOV) PET-CT scanners, including total body scanners, are already in use in a few centers worldwide. These systems have some major advantages over standard axial field of view (SAFOV) PET-CT scanners, mainly due to up to 20 times higher sensitivity and therefore improved lesion detectability. Other advantages are the reduction of the PET acquisition time for a static whole-body measurement, the reduction of the administered radiotracer dose, and the ability to perform delayed scans with good image quality, which is important for imaging radionuclides with long half-lives and pharmaceuticals with long biodistribution times, such as 89Zr-labeled antibodies. The reduction of the applied tracer dose leads to less radiation exposure and may facilitate longitudinal studies, especially in oncological patients, for the evaluation of therapy. The reduction in acquisition time for a static whole body (WB) study allows a markedly higher patient throughput. Furthermore, LAFOV PET-CT scanners enable for the first-time WB dynamic PET scanning and WB parametric imaging with an improved image quality due to increased sensitivity and time resolution. WB tracer kinetics is of particular interest for the characterization of novel radiopharmaceuticals and for a better biological characterization of cancer diseases, as well as for a more accurate assessment of the response to new targeted therapies. Further technological developments based on artificial intelligence (AI) approaches are underway and may in the future allow CT-less attenuation correction or ultralow dose CT for attenuation correction as well as segmentation algorithms for the evaluation of total metabolic tumor volume. The aim of this review is to present dedicated PET acquisition protocols for oncological studies with LAFOV scanners, including static and dynamic acquisition as well as parametric scans, and to present literature data to date on this topic.
Collapse
Affiliation(s)
| | - Leyun Pan
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Heidelberg, Germany
| | - Christos Sachpekidis
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Heidelberg, Germany
| |
Collapse
|
2
|
Liu G, Shi Y, Hou X, Yu H, Hu Y, Zhang Y, Shi H. Dynamic total-body PET/CT imaging with reduced acquisition time shows acceptable performance in quantification of [ 18F]FDG tumor kinetic metrics. Eur J Nucl Med Mol Imaging 2024; 51:1371-1382. [PMID: 38078950 DOI: 10.1007/s00259-023-06526-4] [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: 05/06/2023] [Accepted: 11/14/2023] [Indexed: 03/22/2024]
Abstract
PURPOSE To investigate the feasibility of reducing the acquisition time for continuous dynamic positron emission tomography (PET) while retaining acceptable performance in quantifying kinetic metrics of 2-[18F]-fluoro-2-deoxy-D-glucose ([18F]FDG) in tumors. METHODS In total, 78 oncological patients underwent total-body dynamic PET imaging for ≥ 60 min, with 8, 20, and 50 patients receiving full activity (3.7 MBq/kg), half activity (1.85 MBq/kg), and ultra-low activity (0.37 MBq/kg) of [18F]FDG, respectively. The dynamic data were divided into 21-, 30-, 45- and ≥ 60-min groups. The kinetic analysis involved model fitting to derive constant rates (VB, K1 to k3, and Ki) for both tumors and normal tissues, using both reversible and irreversible two-tissue-compartment models. One-way ANOVA with repeated measures or the Freidman test compared the kinetic metrics among groups, while the Deming regression assessed the correlation of kinetic metrics among groups. RESULTS All kinetic metrics in the 30-min and 45-min groups were statistically comparable to those in the ≥ 60-min group. The relative differences between the 30-min and ≥ 60-min groups ranged from 12.3% ± 15.1% for K1 to 29.8% ± 30.0% for VB, and those between the 45-min and ≥ 60-min groups ranged from 7.5% ± 8.7% for Ki to 24.0% ± 24.3% for VB. However, this comparability was not observed between the 21-min and ≥ 60-min groups. The significance trend of these comparisons remained consistent across different models (reversible or irreversible), administrated activity levels, and partial volume corrections for lesions. Significant correlations in tumor kinetic metrics were identified between the 30-/45-min and ≥ 60-min groups, with Deming regression slopes > 0.813. In addition, the comparability of kinetic metrics between the 30-min and ≥ 60-min groups were established for normal tissues. CONCLUSION The acquisition time for dynamic PET imaging can be reduced to 30 min without compromising the ability to reveal tumor kinetic metrics of [18F]FDG, using the total-body PET/CT system.
Collapse
Affiliation(s)
- Guobing Liu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, P.R. China
- Department of Nuclear Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, 361015, China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, 361015, China
| | - Yimeng Shi
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, P.R. China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Xiaoguang Hou
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, P.R. China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Haojun Yu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, P.R. China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yan Hu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, P.R. China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yiqiu Zhang
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, P.R. China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Hongcheng Shi
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, P.R. China.
- Department of Nuclear Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, 361015, China.
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China.
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, 361015, China.
| |
Collapse
|
3
|
Kaneko K, Nagao M, Yamamoto A, Yano K, Honda G, Tokushige K, Sakai S. Patlak Reconstruction Using Dynamic 18 F-FDG PET Imaging for Evaluation of Malignant Liver Tumors : A Comparison of HCC, ICC, and Metastatic Liver Tumors. Clin Nucl Med 2024; 49:116-123. [PMID: 38108830 DOI: 10.1097/rlu.0000000000005013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
PURPOSE OF THE REPORT The aim of this study was to explore the different patterns of dynamic whole-body (D-WB) FDG PET/CT parameters among liver malignancy types as potential diagnostic clues and investigate the association between static and dynamic PET/CT parameters for each tumor histology. PATIENTS AND METHODS Seventy-one patients with intrahepatic cholangiocarcinoma (ICC), metastatic liver tumor (MLT), or hepatocellular carcinoma (HCC) who underwent D-WB and static dual-time-point FDG PET/CT were enrolled. We obtained Pearson correlation coefficients between the metabolic rate of FDG (MR FDG ; mg/min/ 100ml) or distribution volume of free FDG (DV FDG , %) and static PET/CT parameters. We compared MR FDG and DV FDG values by tumor type and performed receiver operating characteristic analyses for MR FDG and static images. RESULTS A total of 12 ICC, 116 MLT, and 36 HCC lesions were analyzed. MR FDG and DV FDG showed excellent correlation with early (SUV e ) and delayed SUV max (SUV d ) ( r = 0.71~0.97), but DV FDG in the HCC lesions did not ( r = 0.62 and 0.69 for SUV e and SUV d , respectively) ( P < 0.001 for all). HCC lesions showed significantly lower MR FDG (2.43 ± 1.98) and DV FDG (139.95 ± 62.58) than ICC (5.02 ± 3.56, 207.06 ± 97.13) and MLT lesions (4.51 ± 2.47, 180.13 ± 75.58) ( P < 0.01 for all). The optimal MR FDG could differentiate HCC from ICC and MLT with areas under the curve of 0.84 and 0.80, respectively. Metastatic liver tumor lesions showed the widest distribution of MR FDG and DV FDG values but with no significant difference among most primary sites. CONCLUSIONS MR FDG was strongly correlated with SUV max in the 3 malignancies and showed utility for differentiating HCC from ICC and MLT. Each tumor type has a different glucose metabolism, and D-WB FDG PET/CT imaging has the potential to visualize those differences.
Collapse
Affiliation(s)
- Koichiro Kaneko
- From the Department of Diagnostic Imaging & Nuclear Medicine
| | - Michinobu Nagao
- From the Department of Diagnostic Imaging & Nuclear Medicine
| | | | - Kyoko Yano
- From the Department of Diagnostic Imaging & Nuclear Medicine
| | - Goro Honda
- Department of Surgery, Institute of Gastroenterology
| | - Katsutoshi Tokushige
- Department of Internal Medicine, Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan
| | - Shuji Sakai
- From the Department of Diagnostic Imaging & Nuclear Medicine
| |
Collapse
|
4
|
Chen R, Ng YL, Yang X, Zhu Y, Li L, Zhao H, Huang G, Liu J. Assessing dynamic metabolic heterogeneity in prostate cancer patients via total-body [ 68Ga]Ga-PSMA-11 PET/CT imaging: quantitative analysis of [ 68Ga]Ga-PSMA-11 uptake in pathological lesions and normal organs. Eur J Nucl Med Mol Imaging 2024; 51:896-906. [PMID: 37889299 DOI: 10.1007/s00259-023-06475-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 10/13/2023] [Indexed: 10/28/2023]
Abstract
PURPOSE This study aimed to quantitatively assess [68Ga]Ga-PSMA-11 uptake in pathological lesions and normal organs in prostate cancer using the total-body [68Ga]Ga-PSMA-11 PET/CT and to characterize the dynamic metabolic heterogeneity of prostate cancer. METHODS Dynamic total-body [68Ga]Ga-PSMA-11 PET/CT scans were performed on ten prostate cancer patients. Manual delineation of volume-of-interests (VOIs) was performed on multiple normal organs displaying high [68Ga]Ga-PSMA-11 uptake, as well as pathological lesions. Time-to-activity curves (TACs) were generated, and the four compartment models including one-tissue compartmental model (1T1k), reversible one-tissue compartmental model (1T2k), irreversible two-tissue compartment model (2T3k) and reversible two-tissue compartmental model (2T4k) were fitted to each tissue TAC. Various rate constants, including K1 (forward transport rate from plasma to the reversible compartment), k2 (reverse transport rate from the reversible compartment to plasma), k3 (tracer binding on the PSMA-receptor and its internalization), k4 (the externalization rate of the tracer) and Ki (net influx rate), were obtained. The selection of the optimal model for describing the uptake of both lesions and normal organs was determined using the Akaike information criteria (AIC). Receiver operating characteristic (ROC) curve analysis was performed to determine the cut-off values for differentiating physiological and pathological [68Ga]Ga-PSMA-11 uptake. RESULTS Both 1T1k and 1T2k models showed relatively high AIC values compared to the 2T3k and 2T4k models in both pathological lesions and normal organs. The kinetic behavior of pathological lesions was better described by the 2T3k model compared to the 2T4k model, while the normal organs were better described by the 2T4k model. Significant variations in kinetic metrics, such as K1, k2, and k3, and Ki, were observed among normal organs with high [68Ga]Ga-PSMA-11 uptake and pathological lesions. The high Ki value in normal organs was primarily determined by elevated K1 and low k3, rather than k2. Conversely, the high Ki value in pathological lesions, ranking second to the kidney and similar to salivary glands and spleen, was predominantly determined by the highest k3 value. Notably, k3 exhibited the highest performance in distinguishing between physiological and pathological [68Ga]Ga-PSMA-11 uptake, with an area under the curve (AUC) of 0.844 (95% CI, 0.773-0.915), sensitivity of 82.9%, and specificity of 74.1%. The k3 values showed better performance than SUVmean (AUC, 0.659), SUVmax (AUC, 0.637), and other kinetic parameter including K1 (AUC, 0.604), k2 (AUC, 0.634), and Ki (AUC, 0.651). CONCLUSIONS Significant discrepancies in kinetic metrics were detected between pathological lesions and normal organs, despite their shared high uptake of [68Ga]Ga-PSMA-11. Notably, the k3 value exhibits a noteworthy capability to distinguish between pathological lesions and normal organs with elevated [68Ga]Ga-PSMA-11 uptake. This discovery implies that k3 holds promise as a prospective imaging biomarker for distinguishing between pathologic and non-specific [68Ga]Ga-PSMA-11 uptake in patients with prostate cancer.
Collapse
Affiliation(s)
- Ruohua Chen
- Department of Nuclear Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China
| | - Yee Ling Ng
- Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China
| | - Xinlan Yang
- Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China
| | - Yinjie Zhu
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China
| | - Lianghua Li
- Department of Nuclear Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China
| | - Haitao Zhao
- Department of Nuclear Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China
| | - Gang Huang
- Department of Nuclear Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China.
| | - Jianjun Liu
- Department of Nuclear Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China.
| |
Collapse
|
5
|
Moradi H, Vashistha R, O'Brien K, Hammond A, Vegh V, Reutens D. A short 18F-FDG imaging window triple injection neuroimaging protocol for parametric mapping in PET. EJNMMI Res 2024; 14:1. [PMID: 38169031 PMCID: PMC10761663 DOI: 10.1186/s13550-023-01061-7] [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: 04/16/2023] [Accepted: 12/14/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND In parametric PET, kinetic parameters are extracted from dynamic PET images. It is not commonly used in clinical practice because of long scan times and the requirement for an arterial input function (AIF). To address these limitations, we designed an 18F-fluorodeoxyglucose (18F-FDG) triple injection dynamic PET protocol for brain imaging with a standard field of view PET scanner using a 24-min imaging window and an input function modeled using measurements from a region of interest placed over the left ventricle. METHODS To test the protocol in 6 healthy participants, we examined the quality of voxel-based maps of kinetic parameters in the brain generated using the two-tissue compartment model and compared estimated parameter values with previously published values. We also utilized data from a 36-min validation imaging window to compare (1) the modeled AIF against the input function measured in the validation window; and (2) the net influx rate ([Formula: see text]) computed using parameter estimates from the short imaging window against the net influx rate obtained using Patlak analysis in the validation window. RESULTS Compared to the AIF measured in the validation window, the input function estimated from the short imaging window achieved a mean area under the curve error of 9%. The voxel-wise Pearson's correlation between [Formula: see text] estimates from the short imaging window and the validation imaging window exceeded 0.95. CONCLUSION The proposed 24-min triple injection protocol enables parametric 18F-FDG neuroimaging with noninvasive estimation of the AIF from cardiac images using a standard field of view PET scanner.
Collapse
Affiliation(s)
- Hamed Moradi
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Rajat Vashistha
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
| | - Kieran O'Brien
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Amanda Hammond
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Viktor Vegh
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia.
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia.
| | - David Reutens
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
| |
Collapse
|
6
|
Gu F, Wu Q. Quantitation of dynamic total-body PET imaging: recent developments and future perspectives. Eur J Nucl Med Mol Imaging 2023; 50:3538-3557. [PMID: 37460750 PMCID: PMC10547641 DOI: 10.1007/s00259-023-06299-w] [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: 12/22/2022] [Accepted: 06/05/2023] [Indexed: 10/04/2023]
Abstract
BACKGROUND Positron emission tomography (PET) scanning is an important diagnostic imaging technique used in disease diagnosis, therapy planning, treatment monitoring, and medical research. The standardized uptake value (SUV) obtained at a single time frame has been widely employed in clinical practice. Well beyond this simple static measure, more detailed metabolic information can be recovered from dynamic PET scans, followed by the recovery of arterial input function and application of appropriate tracer kinetic models. Many efforts have been devoted to the development of quantitative techniques over the last couple of decades. CHALLENGES The advent of new-generation total-body PET scanners characterized by ultra-high sensitivity and long axial field of view, i.e., uEXPLORER (United Imaging Healthcare), PennPET Explorer (University of Pennsylvania), and Biograph Vision Quadra (Siemens Healthineers), further stimulates valuable inspiration to derive kinetics for multiple organs simultaneously. But some emerging issues also need to be addressed, e.g., the large-scale data size and organ-specific physiology. The direct implementation of classical methods for total-body PET imaging without proper validation may lead to less accurate results. CONCLUSIONS In this contribution, the published dynamic total-body PET datasets are outlined, and several challenges/opportunities for quantitation of such types of studies are presented. An overview of the basic equation, calculation of input function (based on blood sampling, image, population or mathematical model), and kinetic analysis encompassing parametric (compartmental model, graphical plot and spectral analysis) and non-parametric (B-spline and piece-wise basis elements) approaches is provided. The discussion mainly focuses on the feasibilities, recent developments, and future perspectives of these methodologies for a diverse-tissue environment.
Collapse
Affiliation(s)
- Fengyun Gu
- School of Mathematics and Physics, North China Electric Power University, 102206, Beijing, China.
- School of Mathematical Sciences, University College Cork, T12XF62, Cork, Ireland.
| | - Qi Wu
- School of Mathematical Sciences, University College Cork, T12XF62, Cork, Ireland
| |
Collapse
|
7
|
Dimitrakopoulou-Strauss A, Pan L, Sachpekidis C. Long axial field of view (LAFOV) PET-CT: implementation in static and dynamic oncological studies. Eur J Nucl Med Mol Imaging 2023; 50:3354-3362. [PMID: 37079129 PMCID: PMC10541341 DOI: 10.1007/s00259-023-06222-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/02/2023] [Indexed: 04/21/2023]
Abstract
Long axial field of view (LAFOV) PET-CT scanners have been recently developed and are already in clinical use in few centers worldwide. Although still limited, the hitherto acquired experience with these novel systems highlights an increased sensitivity as their main advantage, which results in an increased lesion detectability. This attribute, alternatively, allows a reduction in PET acquisition time and/or administered radiotracer dose, while it renders delayed scanning of satisfying diagnostic accuracy possible. Another potential advantage of the new generation scanners is CT-less approaches for attenuation correction with the impact of marked reduction of radiation exposure, which may in turn lead to greater acceptance of longitudinal PET studies in the oncological setting. Further, the possibility for the first time of whole-body dynamic imaging, improved compartment modeling, and whole-body parametric imaging represent unique characteristics of the LAFOV PET-CT scanners. On the other hand, the advent of the novel LAFOV scanners is linked to specific challenges, such as the high purchase price and issues related to logistics and their optimal operation in a nuclear medicine department. Moreover, with regard to its research applications in oncology, the full potential of the new scanners can only be reached if different radiopharmaceuticals, both short and long-lived ones, as well as novel tracers, are available for use, which would, in turn, require the appropriate infrastructure in the area of radiochemistry. Although the novel LAFOV scanners are not yet widely used, this development represents an important step in the evolution of molecular imaging. This review presents the advantages and challenges of LAFOV PET-CT imaging for oncological applications with respect to static and dynamic acquisition protocols as well as to new tracers, while it provides an overview of the literature in the field.
Collapse
Affiliation(s)
- Antonia Dimitrakopoulou-Strauss
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany.
| | - Leyun Pan
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany
| | - Christos Sachpekidis
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany
| |
Collapse
|
8
|
Guo X, Wu J, Chen MK, Liu Q, Onofrey JA, Pucar D, Pang Y, Pigg D, Casey ME, Dvornek NC, Liu C. Inter-pass motion correction for whole-body dynamic PET and parametric imaging. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2023; 7:344-353. [PMID: 37842204 PMCID: PMC10569406 DOI: 10.1109/trpms.2022.3227576] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Whole-body dynamic FDG-PET imaging through continuous-bed-motion (CBM) mode multi-pass acquisition protocol is a promising metabolism measurement. However, inter-pass misalignment originating from body movement could degrade parametric quantification. We aim to apply a non-rigid registration method for inter-pass motion correction in whole-body dynamic PET. 27 subjects underwent a 90-min whole-body FDG CBM PET scan on a Biograph mCT (Siemens Healthineers), acquiring 9 over-the-heart single-bed passes and subsequently 19 CBM passes (frames). The inter-pass motion correction was executed using non-rigid image registration with multi-resolution, B-spline free-form deformations. The parametric images were then generated by Patlak analysis. The overlaid Patlak slope Ki and y-intercept Vb images were visualized to qualitatively evaluate motion impact and correction effect. The normalized weighted mean squared Patlak fitting errors (NFE) were compared in the whole body, head, and hypermetabolic regions of interest (ROI). In Ki images, ROI statistics were collected and malignancy discrimination capacity was estimated by the area under the receiver operating characteristic curve (AUC). After the inter-pass motion correction was applied, the spatial misalignment appearance between Ki and Vb images was successfully reduced. Voxel-wise normalized fitting error maps showed global error reduction after motion correction. The NFE in the whole body (p = 0.0013), head (p = 0.0021), and ROIs (p = 0.0377) significantly decreased. The visual performance of each hypermetabolic ROI in Ki images was enhanced, while 3.59% and 3.67% average absolute percentage changes were observed in mean and maximum Ki values, respectively, across all evaluated ROIs. The estimated mean Ki values had substantial changes with motion correction (p = 0.0021). The AUC of both mean Ki and maximum Ki after motion correction increased, possibly suggesting the potential of enhancing oncological discrimination capacity through inter-pass motion correction.
Collapse
Affiliation(s)
- Xueqi Guo
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06511, USA
| | - Jing Wu
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06511, USA, and the Center for Advanced Quantum Studies and Department of Physics, Beijing Normal University, Beijing, China
| | - Ming-Kai Chen
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06511, USA
| | - Qiong Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06511, USA
| | - John A Onofrey
- Department of Biomedical Engineering, the Department of Radiology and Biomedical Imaging, and the Department of Urology, Yale University, New Haven, CT, 06511, USA
| | - Darko Pucar
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06511, USA
| | - Yulei Pang
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06511, USA, and Southern Connecticut State University, New Haven, CT, 06515, USA
| | - David Pigg
- Siemens Medical Solutions USA, Inc., Knoxville, TN, 37932, USA
| | - Michael E Casey
- Siemens Medical Solutions USA, Inc., Knoxville, TN, 37932, USA
| | - Nicha C Dvornek
- Department of Biomedical Engineering and the Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06511, USA
| | - Chi Liu
- Department of Biomedical Engineering and the Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06511, USA
| |
Collapse
|
9
|
Li Y, Hu J, Sari H, Xue S, Ma R, Kandarpa S, Visvikis D, Rominger A, Liu H, Shi K. A deep neural network for parametric image reconstruction on a large axial field-of-view PET. Eur J Nucl Med Mol Imaging 2023; 50:701-714. [PMID: 36326869 DOI: 10.1007/s00259-022-06003-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 10/09/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE The PET scanners with long axial field of view (AFOV) having ~ 20 times higher sensitivity than conventional scanners provide new opportunities for enhanced parametric imaging but suffer from the dramatically increased volume and complexity of dynamic data. This study reconstructed a high-quality direct Patlak Ki image from five-frame sinograms without input function by a deep learning framework based on DeepPET to explore the potential of artificial intelligence reducing the acquisition time and the dependence of input function in parametric imaging. METHODS This study was implemented on a large AFOV PET/CT scanner (Biograph Vision Quadra) and twenty patients were recruited with 18F-fluorodeoxyglucose (18F-FDG) dynamic scans. During training and testing of the proposed deep learning framework, the last five-frame (25 min, 40-65 min post-injection) sinograms were set as input and the reconstructed Patlak Ki images by a nested EM algorithm on the vendor were set as ground truth. To evaluate the image quality of predicted Ki images, mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) were calculated. Meanwhile, a linear regression process was applied between predicted and true Ki means on avid malignant lesions and tumor volume of interests (VOIs). RESULTS In the testing phase, the proposed method achieved excellent MSE of less than 0.03%, high SSIM, and PSNR of ~ 0.98 and ~ 38 dB, respectively. Moreover, there was a high correlation (DeepPET: [Formula: see text]= 0.73, self-attention DeepPET: [Formula: see text]=0.82) between predicted Ki and traditionally reconstructed Patlak Ki means over eleven lesions. CONCLUSIONS The results show that the deep learning-based method produced high-quality parametric images from small frames of projection data without input function. It has much potential to address the dilemma of the long scan time and dependency on input function that still hamper the clinical translation of dynamic PET.
Collapse
Affiliation(s)
- Y Li
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, People's Republic of China.,College of Optical Science and Engineering, Zhejiang University, Hangzhou, People's Republic of China
| | - J Hu
- Department of Nuclear Medicine, Inselpital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - H Sari
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - S Xue
- Department of Nuclear Medicine, Inselpital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - R Ma
- Department of Nuclear Medicine, Inselpital, Bern University Hospital, University of Bern, Bern, Switzerland.,Department of Engineering Physics, Tsinghua University, Beijing, China
| | - S Kandarpa
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
| | - D Visvikis
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
| | - A Rominger
- Department of Nuclear Medicine, Inselpital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - H Liu
- College of Optical Science and Engineering, Zhejiang University, Hangzhou, People's Republic of China.
| | - K Shi
- Department of Nuclear Medicine, Inselpital, Bern University Hospital, University of Bern, Bern, Switzerland.,Computer Aided Medical Procedures and Augmented Reality, Institute of Informatics I16, Technical University of Munich, Munich, Germany
| |
Collapse
|
10
|
van Sluis J, van Snick JH, Brouwers AH, Noordzij W, Dierckx RAJO, Borra RJH, Lammertsma AA, Glaudemans AWJM, Slart RHJA, Yaqub M, Tsoumpas C, Boellaard R. Shortened duration whole body 18F-FDG PET Patlak imaging on the Biograph Vision Quadra PET/CT using a population-averaged input function. EJNMMI Phys 2022; 9:74. [PMID: 36308568 PMCID: PMC9618000 DOI: 10.1186/s40658-022-00504-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/21/2022] [Indexed: 11/13/2022] Open
Abstract
Background Excellent performance characteristics of the Vision Quadra PET/CT, e.g. a substantial increase in sensitivity, allow for precise measurements of image-derived input functions (IDIF) and tissue time activity curves. Previously we have proposed a method for a reduced 30 min (as opposed to 60 min) whole body 18F-FDG Patlak PET imaging procedure using a previously published population-averaged input function (PIF) scaled to IDIF values at 30–60 min post-injection (p.i.). The aim of the present study was to apply this method using the Vision Quadra PET/CT, including the use of a PIF to allow for shortened scan durations. Methods Twelve patients with suspected lung malignancy were included and received a weight-based injection of 18F-FDG. Patients underwent a 65-min dynamic PET acquisition which were reconstructed using European Association of Nuclear Medicine Research Ltd. (EARL) standards 2 reconstruction settings. A volume of interest (VOI) was placed in the ascending aorta (AA) to obtain the IDIF. An external PIF was scaled to IDIF values at 30–60, 40–60, and 50–60 min p.i., respectively, and parametric 18F-FDG influx rate constant (Ki) images were generated using a t* of 30, 40 or 50 min, respectively. Herein, tumour lesions as well as healthy tissues, i.e. liver, muscle tissue, spleen and grey matter, were segmented. Results Good agreement between the IDIF and corresponding PIF scaled to 30–60 min p.i. and 40–60 min p.i. was obtained with 7.38% deviation in Ki. Bland–Altman plots showed excellent agreement in Ki obtained using the PIF scaled to the IDIF at 30–60 min p.i. and at 40–60 min p.i. as all data points were within the limits of agreement (LOA) (− 0.004–0.002, bias: − 0.001); for the 50–60 min p.i. Ki, all except one data point fell in between the LOA (− 0.021–0.012, bias: − 0.005). Conclusions Parametric whole body 18F-FDG Patlak Ki images can be generated non-invasively on a Vision Quadra PET/CT system. In addition, using a scaled PIF allows for a substantial (factor 2 to 3) reduction in scan time without substantial loss of accuracy (7.38% bias) and precision (image quality and noise interference). Supplementary Information The online version contains supplementary material available at 10.1186/s40658-022-00504-9.
Collapse
|
11
|
Skawran S, Messerli M, Kotasidis F, Trinckauf J, Weyermann C, Kudura K, Ferraro DA, Pitteloud J, Treyer V, Maurer A, Huellner MW, Burger IA. Can Dynamic Whole-Body FDG PET Imaging Differentiate between Malignant and Inflammatory Lesions? Life (Basel) 2022; 12:life12091350. [PMID: 36143386 PMCID: PMC9501027 DOI: 10.3390/life12091350] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/16/2022] [Accepted: 08/29/2022] [Indexed: 11/18/2022] Open
Abstract
Background: Investigation of the clinical feasibility of dynamic whole-body (WB) [18F]FDG PET, including standardized uptake value (SUV), rate of irreversible uptake (Ki), and apparent distribution volume (Vd) in physiologic tissues, and comparison between inflammatory/infectious and cancer lesions. Methods: Twenty-four patients were prospectively included to undergo dynamic WB [18F]FDG PET/CT for clinically indicated re-/staging of oncological diseases. Parametric maps of Ki and Vd were generated using Patlak analysis alongside SUV images. Maximum parameter values (SUVmax, Kimax, and Vdmax) were measured in liver parenchyma and in malignant or inflammatory/infectious lesions. Lesion-to-background ratios (LBRs) were calculated by dividing the measurements by their respective mean in the liver tissue. Results: Seventy-seven clinical target lesions were identified, 60 malignant and 17 inflammatory/infectious. Kimax was significantly higher in cancer than in inflammatory/infections lesions (3.0 vs. 2.0, p = 0.002) while LBRs of SUVmax, Kimax, and Vdmax did not differ significantly between the etiologies: LBR (SUVmax) 3.3 vs. 2.9, p = 0.06; LBR (Kimax) 5.0 vs. 4.4, p = 0.05, LBR (Vdmax) 1.1 vs. 1.0, p = 0.18). LBR of inflammatory/infectious and cancer lesions was higher in Kimax than in SUVmax (4.5 vs. 3.2, p < 0.001). LBRs of Kimax and SUVmax showed a strong correlation (Spearman’s rho = 0.83, p < 0.001). Conclusions: Dynamic WB [18F]FDG PET/CT is feasible in a clinical setting. LBRs of Kimax were higher than SUVmax. Kimax was higher in malignant than in inflammatory/infectious lesions but demonstrated a large overlap between the etiologies.
Collapse
Affiliation(s)
- Stephan Skawran
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | | | - Josephine Trinckauf
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Corina Weyermann
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Ken Kudura
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
- Claraspital, 4058 Basel, Switzerland
| | - Daniela A. Ferraro
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | - Janique Pitteloud
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | - Valerie Treyer
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | - Alexander Maurer
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | - Martin W. Huellner
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | - Irene A. Burger
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
- Department of Nuclear Medicine, Kantonsspital Baden, 5404 Baden, Switzerland
- Correspondence:
| |
Collapse
|
12
|
Viswanath V, Sari H, Pantel AR, Conti M, Daube-Witherspoon ME, Mingels C, Alberts I, Eriksson L, Shi K, Rominger A, Karp JS. Abbreviated scan protocols to capture 18F-FDG kinetics for long axial FOV PET scanners. Eur J Nucl Med Mol Imaging 2022; 49:3215-3225. [PMID: 35278108 PMCID: PMC10695012 DOI: 10.1007/s00259-022-05747-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 02/25/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE Kinetic parameters from dynamic 18F-fluorodeoxyglucose (FDG) imaging offer complementary insights to the study of disease compared to static clinical imaging. However, dynamic imaging protocols are cumbersome due to the long acquisition time. Long axial field-of-view (LAFOV) PET scanners (> 70 cm) have two advantages for dynamic imaging over clinical PET scanners with a standard axial field-of-view (SAFOV; 16-30 cm). The large axial coverage enables multi-organ dynamic imaging in a single bed position, and the high sensitivity may enable clinically routine abbreviated dynamic imaging protocols. METHODS In this work, we studied two abbreviated protocols using data from a 65-min dynamic 18F-FDG scan: (A) dynamic imaging immediately post-injection (p.i.) for variable durations, and (B) dynamic imaging immediately p.i. for variable durations plus a 1-h p.i. (5-min-long) datapoint. Nine cancer patients were imaged on the Biograph Vision Quadra (Siemens Healthineers). Time-activity curves over the lesions (N = 39) were fitted using the Patlak graphical analysis and a 2-tissue-compartment (2C, k4 = 0) model for variable scan durations (5-60 min). Kinetic parameters from the complete dataset served as the reference. Lesions from all cancers were grouped into low, medium, and high flux groups, and bias and precision of Ki (Patlak) and Ki, K1, k2, and k3 (2C) were calculated for each group. RESULTS Using only early dynamic data with the 2C (or Patlak) model, accurate quantification of Ki required at least 50 (or 55) min of dynamic data for low flux lesions, at least 30 (or 40) min for medium flux lesions, and at least 15 (or 20) min for high flux lesions to achieve both 10% bias and precision. The addition of the final (5-min) datapoint allowed for accurate quantification of Ki with a bias and precision of 10% using only 10-15 min of early dynamic data for either model. CONCLUSION Dynamic imaging for 10-15 min immediately p.i. followed by a 5-min scan at 1-h p.i can accurately and precisely quantify 18F-FDG on a long axial FOV scanner, potentially allowing for more widespread use of dynamic 18F-FDG imaging.
Collapse
Affiliation(s)
- Varsha Viswanath
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hasan Sari
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, 18, 3010, Bern, Switzerland
| | - Austin R Pantel
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Clemens Mingels
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, 18, 3010, Bern, Switzerland
| | - Ian Alberts
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, 18, 3010, Bern, Switzerland
| | - Lars Eriksson
- Siemens Medical Solutions, USA Inc., Knoxville, TN, USA
- Department of Oncology and Pathology, Medical Radiation Physics, Karolinska Institutet, Stockholm, Sweden
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, 18, 3010, Bern, Switzerland
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, 18, 3010, Bern, Switzerland
| | - Joel S Karp
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| |
Collapse
|
13
|
Liu G, Yu H, Shi D, Hu P, Hu Y, Tan H, Zhang Y, Yin H, Shi H. Short-time total-body dynamic PET imaging performance in quantifying the kinetic metrics of 18F-FDG in healthy volunteers. Eur J Nucl Med Mol Imaging 2022; 49:2493-2503. [PMID: 34417855 DOI: 10.1007/s00259-021-05500-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 07/18/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE To investigate the performance of short-time dynamic imaging in quantifying kinetic metrics of 2-[18F]-fluoro-2-deoxy-D-glucose (18F-FDG). METHODS Dynamic total-body positron emission tomography (PET) imaging was performed in 11 healthy volunteers for 75 min. The data were divided into 30-, 45- and 75-min groups. Nonlinear regression (NLR) generated constant rates (k1 to k3) and NLR-based Ki in various organs. The Patlak method calculated parametric Ki images to generate Patlak-based Ki values. Paired samples t-test or the Wilcoxon signed-rank test compared the kinetic metrics between the groups, depending on data normality. Deming regression and Bland-Altman analysis assessed the correlation and agreement between NLR- and Patlak-based Ki. A two-sided P < 0.05 was considered statistically significant. RESULTS The 45- and 75-min groups were similar in NLR-based kinetic metrics. The relative difference ranges were as follows: k1, from 3.4% (P = 0.627) in the spleen to 57.9% (P = 0.130) in the white matter; k2, from 6.0% (P = 0.904) in the spleen to 60.7% (P = 0.235) in the left ventricle (LV) myocardium; k3, from 45.6% (P = 0.302) in the LV myocardium to 96.3% (P = 0.478) in the liver; Ki, from 14.0% (P = 0.488) in the liver to 77.8% (P = 0.067) in the kidney. Patlak-based Ki values were also similar between these groups in all organs, except the grey matter (9.6%, P = 0.029) and cerebellum (14.4%, P = 0.002). However, significant differences in kinetic metrics were found between the 30-min and 75-min groups in most organs both in NLR- and Patlak-based analyses. The NLR- and Patlak-based Ki values significantly correlated, with no bias in any of the organs. CONCLUSION Dynamic imaging using a high-sensitivity total-body PET scanner for a shorter time of 45 min could achieve relevant kinetic metrics of 18F-FDG as done by long-time imaging.
Collapse
Affiliation(s)
- Guobing Liu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Haojun Yu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Dai Shi
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Pengcheng Hu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yan Hu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Hui Tan
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yiqiu Zhang
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Hongyan Yin
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Hongcheng Shi
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China.
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China.
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
| |
Collapse
|
14
|
Zaker N, Haddad K, Faghihi R, Arabi H, Zaidi H. Direct inference of Patlak parametric images in whole-body PET/CT imaging using convolutional neural networks. Eur J Nucl Med Mol Imaging 2022; 49:4048-4063. [PMID: 35716176 PMCID: PMC9525418 DOI: 10.1007/s00259-022-05867-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 06/09/2022] [Indexed: 11/20/2022]
Abstract
Purpose This study proposed and investigated the feasibility of estimating Patlak-derived influx rate constant (Ki) from standardized uptake value (SUV) and/or dynamic PET image series. Methods Whole-body 18F-FDG dynamic PET images of 19 subjects consisting of 13 frames or passes were employed for training a residual deep learning model with SUV and/or dynamic series as input and Ki-Patlak (slope) images as output. The training and evaluation were performed using a nine-fold cross-validation scheme. Owing to the availability of SUV images acquired 60 min post-injection (20 min total acquisition time), the data sets used for the training of the models were split into two groups: “With SUV” and “Without SUV.” For “With SUV” group, the model was first trained using only SUV images and then the passes (starting from pass 13, the last pass, to pass 9) were added to the training of the model (one pass each time). For this group, 6 models were developed with input data consisting of SUV, SUV plus pass 13, SUV plus passes 13 and 12, SUV plus passes 13 to 11, SUV plus passes 13 to 10, and SUV plus passes 13 to 9. For the “Without SUV” group, the same trend was followed, but without using the SUV images (5 models were developed with input data of passes 13 to 9). For model performance evaluation, the mean absolute error (MAE), mean error (ME), mean relative absolute error (MRAE%), relative error (RE%), mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) were calculated between the predicted Ki-Patlak images by the two groups and the reference Ki-Patlak images generated through Patlak analysis using the whole acquired data sets. For specific evaluation of the method, regions of interest (ROIs) were drawn on representative organs, including the lung, liver, brain, and heart and around the identified malignant lesions. Results The MRAE%, RE%, PSNR, and SSIM indices across all patients were estimated as 7.45 ± 0.94%, 4.54 ± 2.93%, 46.89 ± 2.93, and 1.00 ± 6.7 × 10−7, respectively, for models predicted using SUV plus passes 13 to 9 as input. The predicted parameters using passes 13 to 11 as input exhibited almost similar results compared to the predicted models using SUV plus passes 13 to 9 as input. Yet, the bias was continuously reduced by adding passes until pass 11, after which the magnitude of error reduction was negligible. Hence, the predicted model with SUV plus passes 13 to 9 had the lowest quantification bias. Lesions invisible in one or both of SUV and Ki-Patlak images appeared similarly through visual inspection in the predicted images with tolerable bias. Conclusion This study concluded the feasibility of direct deep learning-based approach to estimate Ki-Patlak parametric maps without requiring the input function and with a fewer number of passes. This would lead to shorter acquisition times for WB dynamic imaging with acceptable bias and comparable lesion detectability performance. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-022-05867-w.
Collapse
Affiliation(s)
- Neda Zaker
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.,School of Mechanical Engineering, Department of Nuclear Engineering, Shiraz University, Shiraz, Iran
| | - Kamal Haddad
- School of Mechanical Engineering, Department of Nuclear Engineering, Shiraz University, Shiraz, Iran
| | - Reza Faghihi
- School of Mechanical Engineering, Department of Nuclear Engineering, Shiraz University, Shiraz, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland. .,Geneva University Neurocenter, Geneva University, Geneva, Switzerland. .,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. .,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
| |
Collapse
|
15
|
Karakatsanis NA, Nehmeh MH, Conti M, Bal G, González AJ, Nehmeh SA. Physical performance of adaptive axial FOV PET scanners with a sparse detector block rings or a checkerboard configuration. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6aa1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 04/26/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. Using Monte-Carlo simulations, we evaluated the physical performance of a hypothetical state-of-the-art clinical PET scanner with adaptive axial field-of-view (AFOV) based on the validated GATE model of the Siemens Biograph VisionTM PET/CT scanner. Approach. Vision consists of 16 compact PET rings, each consisting of 152 mini-blocks of 5 × 5 Lutetium Oxyorthosilicate crystals (3.2 × 3.2 × 20 mm3). The Vision 25.6 cm AFOV was extended by adopting (i) a sparse mini-block ring (SBR) configuration of 49.6 cm AFOV, with all mini-block rings interleaved with 16 mm axial gaps, or (ii) a sparse mini-block checkerboard (SCB) configuration of 51.2 cm AFOV, with all mini-blocks interleaved with gaps of 16 mm (transaxial) × 16 mm (axial) width in checkerboard pattern. For sparse configurations, a ‘limited’ continuous bed motion (limited-CBM) acquisition was employed to extend AFOVs by 2.9 cm. Spatial resolution, sensitivity, image quality (IQ), NECR and scatter fraction were assessed per NEMA NU2-2012. Main Results. All IQ phantom spheres were distinguishable with all configurations. SBR and SCB percent contrast recovery (% CR) and background variability (% BV) were similar (p-value > 0.05). Compared to Vision, SBR and SCB %CRs were similar (p-values > 0.05). However, SBR and SCB %BVs were deteriorated by 30% and 26% respectively (p-values < 0.05). SBR, SCB and Vision exhibited system sensitivities of 16.6, 16.8, and 15.8 kcps MBq−1, NECRs of 311 kcps @35 kBq cc−1, 266 kcps @25.8 kBq cc−1, and 260 kcps @27.8 kBq cc−1, and scatter fractions of 31.2%, 32.4%, and 32.6%, respectively. SBR and SCB exhibited a smoother sensitivity reduction and noise enhancement rate from AFOV center to its edges. SBR and SCB attained comparable spatial resolution in all directions (p-value > 0.05), yet, up to 1.5 mm worse than Vision (p-values < 0.05). Significance. The proposed sparse configurations may offer a clinically adoptable solution for cost-effective adaptive AFOV PET with either highly-sensitive or long-AFOV acquisitions.
Collapse
|
16
|
Huang Z, Wu Y, Fu F, Meng N, Gu F, Wu Q, Zhou Y, Yang Y, Liu X, Zheng H, Liang D, Wang M, Hu Z. Parametric image generation with the uEXPLORER total-body PET/CT system through deep learning. Eur J Nucl Med Mol Imaging 2022; 49:2482-2492. [DOI: 10.1007/s00259-022-05731-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 02/13/2022] [Indexed: 11/25/2022]
|
17
|
Pantel AR, Viswanath V, Muzi M, Doot RK, Mankoff DA. Principles of Tracer Kinetic Analysis in Oncology, Part I: Principles and Overview of Methodology. J Nucl Med 2022; 63:342-352. [PMID: 35232879 DOI: 10.2967/jnumed.121.263518] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 01/12/2022] [Indexed: 12/12/2022] Open
Abstract
Learning Objectives: On successful completion of this activity, participants should be able to describe (1) describe principles of PET tracer kinetic analysis for oncologic applications; (2) list methods used for PET kinetic analysis for oncology; and (3) discuss application of kinetic modeling for cancer-specific diagnostic needs.Financial Disclosure: This work was supported by KL2 TR001879, R01 CA211337, R01 CA113941, R33 CA225310, Komen SAC130060, R50 CA211270, and K01 DA040023. Dr. Pantel is a consultant or advisor for Progenics and Blue Earth Diagnostics and is a meeting participant or lecturer for Blue Earth Diagnostics. Dr. Mankoff is on the scientific advisory boards of GE Healthcare, Philips Healthcare, Reflexion, and ImaginAb and is the owner of Trevarx; his wife is the chief executive officer of Trevarx. The authors of this article have indicated no other relevant relationships that could be perceived as a real or apparent conflict of interest.CME Credit: SNMMI is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to sponsor continuing education for physicians. SNMMI designates each JNM continuing education article for a maximum of 2.0 AMA PRA Category 1 Credits. Physicians should claim only credit commensurate with the extent of their participation in the activity. For CE credit, SAM, and other credit types, participants can access this activity through the SNMMI website (http://www.snmmilearningcenter.org) through March 2025PET enables noninvasive imaging of regional in vivo cancer biology. By engineering a radiotracer to target specific biologic processes of relevance to cancer (e.g., cancer metabolism, blood flow, proliferation, and tumor receptor expression or ligand binding), PET can detect cancer spread, characterize the cancer phenotype, and assess its response to treatment. For example, imaging of glucose metabolism using the radiolabeled glucose analog 18F-FDG has widespread applications to all 3 of these tasks and plays an important role in cancer care. However, the current clinical practice of imaging at a single time point remote from tracer injection (i.e., static imaging) does not use all the information that PET cancer imaging can provide, especially to address questions beyond cancer detection. Reliance on tracer measures obtained only from static imaging may also lead to misleading results. In this 2-part continuing education paper, we describe the principles of tracer kinetic analysis for oncologic PET (part 1), followed by examples of specific implementations of kinetic analysis for cancer PET imaging that highlight the added benefits over static imaging (part 2). This review is designed to introduce nuclear medicine clinicians to basic concepts of kinetic analysis in oncologic imaging, with a goal of illustrating how kinetic analysis can augment our understanding of in vivo cancer biology, improve our approach to clinical decision making, and guide the interpretation of quantitative measures derived from static images.
Collapse
Affiliation(s)
- Austin R Pantel
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - Varsha Viswanath
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, Washington
| | - Robert K Doot
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - David A Mankoff
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; and
| |
Collapse
|
18
|
Ivanidze J, Roytman M, Skafida M, Kim S, Glynn S, Osborne JR, Pannullo SC, Nehmeh S, Ramakrishna R, Schwartz TH, Knisely JPS, Lin E, Karakatsanis NA. Dynamic 68Ga-DOTATATE PET/MRI in the Diagnosis and Management of Intracranial Meningiomas. Radiol Imaging Cancer 2022; 4:e210067. [PMID: 35275019 DOI: 10.1148/rycan.210067] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Purpose To evaluate dynamic gallium 68 (68Ga) tetraazacyclododecane tetraacetic acid octreotate (DOTATATE) brain PET/MRI as an adjunct modality in meningioma, enabling multiparametric standardized uptake value (SUV) and Patlak net binding rate constant (Ki) imaging, and to optimize static acquisition period. Materials and Methods In this prospective study (ClinicalTrials.gov no. NCT04081701, DOMINO-START), 68Ga-DOTATATE PET/MRI-derived time-activity curves (TACs) were measured in 84 volumes of interest in 19 participants (mean age, 63 years; range, 36-89 years; 13 women; 2019-2021) with meningiomas. Region- and voxel-specific Ki were determined using Patlak analysis with a validated population-based reference tissue TAC model built from an independent data set of nine participants. Mean and maximum absolute and relative-to-superior-sagittal-sinus SUVs were extracted from the entire 50 minutes (SUV50) and last 10 minutes (SUV10) of acquisition. SUV versus Ki Spearman correlation, SUV and Ki meningioma versus posttreatment-change Mann-Whitney U tests, and SUV50 versus SUV10 Wilcoxon matched-pairs signed rank tests were performed. Results Absolute and relative maximum SUV50 demonstrated a strong positive correlation with Patlak Ki in meningioma (r = 0.82, P < .001 and r = 0.85, P < .001, respectively) and posttreatment-change lesions (r = 0.88, P = .007 and r = 0.83, P = .02, respectively). Patlak Ki images yielded higher lesion contrast by mitigating nonspecific background signal. All SUV50 and SUV10 metrics differed between meningioma and posttreatment-change regions (P < .001). Within the meningioma group, SUV10 attained higher mean scores than SUV50 (P < .001). Conclusion Combined SUV and Patlak K i 68Ga-DOTATATE PET/MRI enabled multiparametric evaluation of meningioma, offering the potential to enhance lesion contrast with Ki imaging and optimize the SUV measurement postinjection window. Keywords: Molecular Imaging-Clinical Translation, Neuro-Oncology, PET/MRI, Dynamic, Patlak ClinicalTrials.gov registration no. NCT04081701 © RSNA, 2022.
Collapse
Affiliation(s)
- Jana Ivanidze
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Michelle Roytman
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Myrto Skafida
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Sean Kim
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Shannon Glynn
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Joseph R Osborne
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Susan C Pannullo
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Sadek Nehmeh
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Rohan Ramakrishna
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Theodore H Schwartz
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Jonathan P S Knisely
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Eaton Lin
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Nicolas A Karakatsanis
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| |
Collapse
|
19
|
Sari H, Mingels C, Alberts I, Hu J, Buesser D, Shah V, Schepers R, Caluori P, Panin V, Conti M, Afshar-Oromieh A, Shi K, Eriksson L, Rominger A, Cumming P. First results on kinetic modelling and parametric imaging of dynamic 18F-FDG datasets from a long axial FOV PET scanner in oncological patients. Eur J Nucl Med Mol Imaging 2022; 49:1997-2009. [PMID: 34981164 DOI: 10.1007/s00259-021-05623-6] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 11/15/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE To investigate the kinetics of 18F-fluorodeoxyglucose (18F-FDG) by positron emission tomography (PET) in multiple organs and test the feasibility of total-body parametric imaging using an image-derived input function (IDIF). METHODS Twenty-four oncological patients underwent dynamic 18F-FDG scans lasting 65 min using a long axial FOV (LAFOV) PET/CT system. Time activity curves (TAC) were extracted from semi-automated segmentations of multiple organs, cerebral grey and white matter, and from vascular structures. The tissue and tumor lesion TACs were fitted using an irreversible two-tissue compartment (2TC) and a Patlak model. Parametric images were also generated using direct and indirect Patlak methods and their performances were evaluated. RESULTS We report estimates of kinetic parameters and metabolic rate of glucose consumption (MRFDG) for different organs and tumor lesions. In some organs, there were significant differences between MRFDG values estimated using 2TC and Patlak models. No statistically significant difference was seen between MRFDG values estimated using 2TC and Patlak methods in tumor lesions (paired t-test, P = 0.65). Parametric imaging showed that net influx (Ki) images generated using direct and indirect Patlak methods had superior tumor-to-background ratio (TBR) to standard uptake value (SUV) images (3.1- and 3.0-fold mean increases in TBRmean, respectively). Influx images generated using the direct Patlak method had twofold higher contrast-to-noise ratio in tumor lesions compared to images generated using the indirect Patlak method. CONCLUSION We performed pharmacokinetic modelling of multiple organs using linear and non-linear models using dynamic total-body 18F-FDG images. Although parametric images did not reveal more tumors than SUV images, the results confirmed that parametric imaging furnishes improved tumor contrast. We thus demonstrate the feasibility of total-body kinetic modelling and parametric imaging in basic research and oncological studies. LAFOV PET can enhance dynamic imaging capabilities by providing high sensitivity parametric images and allowing total-body pharmacokinetic analysis.
Collapse
Affiliation(s)
- Hasan Sari
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland.
| | - Clemens Mingels
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Ian Alberts
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Jicun Hu
- Siemens Medical Solutions, USA Inc., Knoxville, TN, USA
| | - Dorothee Buesser
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Vijay Shah
- Siemens Medical Solutions, USA Inc., Knoxville, TN, USA
| | - Robin Schepers
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Patrik Caluori
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | | | | | - Ali Afshar-Oromieh
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Lars Eriksson
- Siemens Medical Solutions, USA Inc., Knoxville, TN, USA
- Department of Oncology and Pathology, Medical Radiation Physics, Karolinska Institutet, Stockholm, Sweden
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Paul Cumming
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
- School of Psychology and Counselling, Queensland University of Technology, Brisbane, Australia
| |
Collapse
|
20
|
Tiwari A, Merrick M, Graves SA, Sunderland J. Monte Carlo evaluation of hypothetical long axial field-of-view PET scanner using GE discovery MI PET front-end architecture. Med Phys 2021; 49:1139-1152. [PMID: 34954831 DOI: 10.1002/mp.15422] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 12/08/2021] [Accepted: 12/10/2021] [Indexed: 11/11/2022] Open
Abstract
PURPOSE The development of total-body PET scanners is of growing interest in the PET community. Investigation into the imaging properties of a hypothetical extended axial field-of-view (AFOV) GE Healthcare SiPM-based Discovery MI (DMI) system architecture has not yet been performed. In this work, we assessed its potential as a whole-body scanner using Monte Carlo simulations. The aim of this work was to (1) develop and validate a Monte Carlo model of a 4-ring scanner and (2) extend its AFOV up to 2 m to evaluate performance gain through NEMA-based evaluation. METHODS The DMI 4-ring geometry and its pulse digitization scheme were modeled within the GATE Monte Carlo platform using published literature. The GATE scanner model was validated by comparing results against published NEMA performance measurements. Following the validation of the 4-ring model, the model was extended to simulate 8, 20, 30, and 40-ring systems. Spatial resolution, sensitivity, NECR, and scatter fraction were characterized with modified NEMA NU-2 2018 standards; however, the image quality measurements were not acquired due to computational limitations. Spatial resolutions were simulated for all scanner ring configurations using point sources to examine the effects of parallax errors. NEMA count rates were estimated using a standard 70 cm scatter phantom and an extended version of scatter phantom of length 200 cm with (1-800) MBq of 18 F for all scanners. Sensitivity was evaluated using NEMA methods with a 70 cm standard and a 200 cm long line source. RESULTS The average FWHM of the radial/tangential/axial spatial resolution reconstructed with filtered back-projection at 1 and 10 cm from the scanner center were 3.94/4.10/4.41 mm and 5.29/4.89/5.90 mm for the 4-ring scanner. Sensitivity was determined to be 14.86 cps/kBq at the center of the FOV for the 4-ring scanner using a 70 cm line source. Sensitivity enhancement up to 21-fold and 60-fold were observed for 1 m and 2 m AFOV scanners compared to 4-ring scanner using a 200 cm long line source. Spatial resolution simulations in a 2 m AFOV scanner suggest a maximum degradation of ∼23.8% in the axial resolution compared to the 4-ring scanner. However, the transverse resolution was found to be relatively constant when increasing the axial acceptance angle up to ±70°. The peak NECR was 212.92 kcps at 22.70 kBq/mL with a scatter fraction of 38.9% for a 4-ring scanner with a 70 cm scatter phantom. Comparison of peak NECR using the 200 cm long scatter phantom relative to the 4-ring scanner resulted in a NECR gain of 15 for the 20-ring and 28 for the 40-ring geometry. Spatial resolution, sensitivity, and scatter fraction showed an agreement within ∼7% compared with published measured values. CONCLUSIONS The 4-ring DMI scanner simulation was successfully validated against published NEMA measurements. Sensitivity and NECR performance of extended 1 and 2 meters AFOV scanners based upon the DMI architecture were subsequently simulated. Increases in sensitivity and count-rate performance are consistent with prior simulation studies utilizing extensions of the Siemens mCT architecture and published NEMA measurements with the uEXPLORER system. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Ashok Tiwari
- Department of Radiology, University of Iowa Hospitals and Clinics, 200 Hawkins Dr., Iowa City, IA, 52242, USA.,Department of Physics and Astronomy, University of Iowa, 203 Van Allen Hall, Iowa City, IA, 52242, USA
| | - Michael Merrick
- Department of Radiology, University of Iowa Hospitals and Clinics, 200 Hawkins Dr., Iowa City, IA, 52242, USA.,Department of Biomedical Engineering, University of Iowa, 5601 Seamans Center, Iowa City, IA, 52242, USA
| | - Stephen A Graves
- Department of Radiology, University of Iowa Hospitals and Clinics, 200 Hawkins Dr., Iowa City, IA, 52242, USA.,Department of Biomedical Engineering, University of Iowa, 5601 Seamans Center, Iowa City, IA, 52242, USA.,Department of Radiation Oncology, University of Iowa Hospitals and Clinics, 200 Hawkins Dr., Iowa City, IA, 52242, USA
| | - John Sunderland
- Department of Radiology, University of Iowa Hospitals and Clinics, 200 Hawkins Dr., Iowa City, IA, 52242, USA.,Department of Physics and Astronomy, University of Iowa, 203 Van Allen Hall, Iowa City, IA, 52242, USA.,Department of Radiation Oncology, University of Iowa Hospitals and Clinics, 200 Hawkins Dr., Iowa City, IA, 52242, USA
| |
Collapse
|
21
|
Indovina L, Scolozzi V, Capotosti A, Sestini S, Taralli S, Cusumano D, Giancipoli RG, Ciasca G, Cardillo G, Calcagni ML. Short 2-[ 18F]Fluoro-2-Deoxy-D-Glucose PET Dynamic Acquisition Protocol to Evaluate the Influx Rate Constant by Regional Patlak Graphical Analysis in Patients With Non-Small-Cell Lung Cancer. Front Med (Lausanne) 2021; 8:725387. [PMID: 34881253 PMCID: PMC8647994 DOI: 10.3389/fmed.2021.725387] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 10/04/2021] [Indexed: 11/25/2022] Open
Abstract
Purpose: To test a short 2-[18F]Fluoro-2-deoxy-D-glucose (2-[18F]FDG) PET dynamic acquisition protocol to calculate Ki using regional Patlak graphical analysis in patients with non-small-cell lung cancer (NSCLC). Methods: 24 patients with NSCLC who underwent standard dynamic 2-[18F]FDG acquisitions (60 min) were randomly divided into two groups. In group 1 (n = 10), a population-based image-derived input function (pIDIF) was built using a monoexponential trend (10–60 min), and a leave-one-out cross-validation (LOOCV) method was performed to validate the pIDIF model. In group 2 (n = 14), Ki was obtained by standard regional Patlak plot analysis using IDIF (0–60 min) and tissue response (10–60 min) curves from the volume of interests (VOIs) placed on descending thoracic aorta and tumor tissue, respectively. Moreover, with our method, the Patlak analysis was performed to obtain Ki,s using IDIFFitted curve obtained from PET counts (0–10 min) followed by monoexponential coefficients of pIDIF (10–60 min) and tissue response curve obtained from PET counts at 10 min and between 40 and 60 min, simulating two short dynamic acquisitions. Both IDIF and IDIFFitted curves were modeled to assume the value of 2-[18F]FDG plasma activity measured in the venous blood sampling performed at 45 min in each patient. Spearman's rank correlation, coefficient of determination, and Passing–Bablok regression were used for the comparison between Ki and Ki,s. Finally, Ki,s was obtained with our method in a separate group of patients (group 3, n = 8) that perform two short dynamic acquisitions. Results: Population-based image-derived input function (10–60 min) was modeled with a monoexponential curve with the following fitted parameters obtained in group 1: a = 9.684, b = 16.410, and c = 0.068 min−1. The LOOCV error was 0.4%. In patients of group 2, the mean values of Ki and Ki,s were 0.0442 ± 0.0302 and 0.33 ± 0.0298, respectively (R2 = 0.9970). The Passing–Bablok regression for comparison between Ki and Ki,s showed a slope of 0.992 (95% CI: 0.94–1.06) and intercept value of −0.0003 (95% CI: −0.0033–0.0011). Conclusions: Despite several practical limitations, like the need to position the patient twice and to perform two CT scans, our method contemplates two short 2-[18F]FDG dynamic acquisitions, a population-based input function model, and a late venous blood sample to obtain robust and personalized input function and tissue response curves and to provide reliable regional Ki estimation.
Collapse
Affiliation(s)
- Luca Indovina
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Valentina Scolozzi
- Unità Operativa Complessa (UOC) di Medicina Nucleare, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Amedeo Capotosti
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | - Silvia Taralli
- Unità Operativa Complessa (UOC) di Medicina Nucleare, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Romina Grazia Giancipoli
- Unità Operativa Complessa (UOC) di Medicina Nucleare, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Gabriele Ciasca
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.,Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giuseppe Cardillo
- Unit of Thoracic Surgery, San Camillo Forlanini Hospital, Rome, Italy
| | - Maria Lucia Calcagni
- Unità Operativa Complessa (UOC) di Medicina Nucleare, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.,Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| |
Collapse
|
22
|
Kotani T, Nishimura M, Tamaki N, Matsushima S, Akiyama S, Kanayama T, Bamba C, Tanada Y, Nii T, Yamada K. Comparison between dynamic whole-body FDG-PET and early-delayed imaging for the assessment of motion in focal uptake in colorectal area. Ann Nucl Med 2021; 35:1305-1311. [PMID: 34426890 DOI: 10.1007/s12149-021-01671-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 08/15/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVES Serial changes of focal uptake in whole-body dynamic positron emission tomography (PET) imaging were assessed and compared with those in early-delayed imaging to differentiate pathological uptake from physiological uptake in the colorectal area, based on the change in uptake shape. METHODS In 60 patients with at least 1 pathologically diagnosed colorectal cancer or adenoma, a serial 3 min dynamic whole-body PET/computed tomography imaging was performed four times around 60 min after the administration of 18F-fluorodeoxyglucose (FDG) to create a conventional (early) image by summation. Delayed imaging was performed separately at 110 min after FDG administration. High focal uptake lesions in the colorectal area were visually assessed as "changed" or "unchanged" on serial dynamic imaging and early-delayed imaging, based on the alteration in uptake shape over time. These criteria on the images were used to differentiate pathological uptake from physiological uptake. RESULTS In this study, 334 lesions with high focal FDG uptake were observed. Among 73 histologically proven pathological FDG uptakes, no change was observed in 69 on serial dynamic imaging and 72 on early-delayed imaging (sensitivity of 95 vs. 99%, respectively; ns). In contrast, out of 261 physiological FDG uptakes, a change in uptake shape was seen in 159 on dynamic PET imaging and 66 on early-delayed imaging (specificity of 61 vs. 25%, respectively; p < 0.01). High and similar negative predictive values for identifying pathological uptake were obtained by both methods (98 vs 99%, respectively). Thus, the overall accuracy for differentiating pathological from physiological FDG uptake based on change in uptake shape tended to be higher on serial dynamic imaging (68%) than on early-delayed imaging (41%; p < 0.01). CONCLUSIONS Dynamic whole-body FDG imaging enables differentiation of pathological uptake from physiological uptake based on the serial changes in uptake shape in the colorectal area. It may provide greater diagnostic value than early-delayed PET imaging. Thus, this technique holds a promise for minimizing the need for delayed imaging.
Collapse
Affiliation(s)
- Tomoya Kotani
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
| | - Motoki Nishimura
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Nagara Tamaki
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Shigenori Matsushima
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Shimpei Akiyama
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Taisei Kanayama
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Chisa Bamba
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yasutomo Tanada
- Department of Quantum Medical Technology, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
- Department of Radiological Technology, University Hospital, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Takeshi Nii
- Department of Radiological Technology, University Hospital, Kyoto Prefectural University of Medicine, Kyoto, Japan
- Department of Radiation Science, Graduate School of Health Sciences, Hirosaki University, Hirosaki, Japan
| | - Kei Yamada
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| |
Collapse
|
23
|
Dimitrakopoulou-Strauss A, Pan L, Sachpekidis C. Parametric Imaging With Dynamic PET for Oncological Applications: Protocols, Interpretation, Current Applications and Limitations for Clinical Use. Semin Nucl Med 2021; 52:312-329. [PMID: 34809877 DOI: 10.1053/j.semnuclmed.2021.10.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Nuclear medicine imaging modalities, and in particular positron emission tomography (PET), provide functional images that demonstrate the mean radioactivity distribution at a defined point in time. With the help of mathematical model's, it is possible to depict isolated parameters of the radiotracers' pharmacokinetics and to visualize them. These so called parametric images add a new dimension to the existing conventional PET images and provide more detailed information about the tracer distribution over time and space. Prerequisite for the calculation of parametric images, which reflect specific pharmacokinetic parameters, is the dynamic PET (dPET) data acquisition. Hitherto, PET parametric imaging has mainly found use for research purposes. However, it has not been yet implemented into clinical routine, since it is more time-consuming, it requires a complicated analysis and still lacks a clear benefit over conventional PET imaging. However, the recent introduction of new PET-CT scanners with an ultralong field of view, which allow a faster data acquisition and are associated with higher sensitivity, as well as the development of more sophisticated evaluation software packages will probably lead to a renaissance of dPET and parametric maps even of the whole body. The implementation of dPET imaging in daily routine with appropriate acquisition protocols, as well as the calculation, interpretation and potential clinical applications of parametric images will be discussed in this review article.
Collapse
Affiliation(s)
| | - Leyun Pan
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Heidelberg, Germany
| | - Christos Sachpekidis
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Heidelberg, Germany
| |
Collapse
|
24
|
Dynamic PET image reconstruction incorporating a median nonlocal means kernel method. Comput Biol Med 2021; 139:104713. [PMID: 34768034 DOI: 10.1016/j.compbiomed.2021.104713] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 11/20/2022]
Abstract
In dynamic positron emission tomography (PET) imaging, the reconstructed image of a single frame often exhibits high noise due to limited counting statistics of projection data. This study proposed a median nonlocal means (MNLM)-based kernel method for dynamic PET image reconstruction. The kernel matrix is derived from median nonlocal means of pre-reconstructed composite images. Then the PET image intensities in all voxels were modeled as a kernel matrix multiplied by coefficients and incorporated into the forward model of PET projection data. Then, the coefficients of each feature were estimated by the maximum likelihood method. Using simulated low-count dynamic data of Zubal head phantom, the quantitative performance of the proposed MNLM kernel method was investigated and compared with the maximum-likelihood method, conventional kernel method with and without median filter, and nonlocal means (NLM) kernel method. Simulation results showed that the MNLM kernel method achieved visual and quantitative accuracy improvements (in terms of the ensemble mean squared error, bias versus variance, and contrast versus noise performances). Especially for frame 2 with the lowest count level of a single frame, the MNLM kernel method achieves lower ensemble mean squared error (10.43%) than the NLM kernel method (13.68%), conventional kernel method with and without median filter (11.88% and 23.50%), and MLEM algorithm (24.77%). The study on real low-dose 18F-FDG rat data also showed that the MNLM kernel method outperformed other methods in visual and quantitative accuracy improvements (in terms of regional noise versus intensity mean performance).
Collapse
|
25
|
Chalampalakis Z, Stute S, Filipović M, Sureau F, Comtat C. Use of dynamic reconstruction for parametric Patlak imaging in dynamic whole body PET. Phys Med Biol 2021; 66. [PMID: 34433155 DOI: 10.1088/1361-6560/ac2128] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 08/25/2021] [Indexed: 11/11/2022]
Abstract
Dynamic whole body (DWB) PET acquisition protocols enable the use of whole body parametric imaging for clinical applications. In FDG imaging, accurate parametric images of PatlakKican be complementary to regular standardised uptake value images and improve on current applications or enable new ones. In this study we consider DWB protocols implemented on clinical scanners with a limited axial field of view with the use of multiple whole body sweeps. These protocols result in temporal gaps in the dynamic data which produce noisier and potentially more biased parametric images, compared to single bed (SB) dynamic protocols. Dynamic reconstruction using the Patlak model has been previously proposed to overcome these limits and shown improved DWB parametric images ofKi. In this work, we propose and make use of a spectral analysis based model for dynamic reconstruction and parametric imaging of PatlakKi. Both dynamic reconstruction methods were evaluated for DWB FDG protocols and compared against 3D reconstruction based parametric imaging from SB dynamic protocols. This work was conducted on simulated data and results were tested against real FDG dynamic data. We showed that dynamic reconstruction can achieve levels of parametric image noise and bias comparable to 3D reconstruction in SB dynamic studies, with the spectral model offering additional flexibility and further reduction of image noise. Comparisons were also made between step and shoot and continuous bed motion (CBM) protocols, which showed that CBM can achieve lower parametric image noise due to reduced acquisition temporal gaps. Finally, our results showed that dynamic reconstruction improved VOI parametric mean estimates but did not result to fully converged values before resulting in undesirable levels of noise. Additional regularisation methods need to be considered for DWB protocols to ensure both accurate quantification and acceptable noise levels for clinical applications.
Collapse
Affiliation(s)
- Zacharias Chalampalakis
- Laboratoire d'Imagerie Biomédicale Multimodale (BioMaps), Université Paris-Saclay, CEA, CNRS, Inserm, Service Hospitalier Frédéric Joliot, Orsay, France
| | - Simon Stute
- Nuclear Medicine Department, Nantes University Hospital, Nantes, France.,CRCINA, Inserm, CNRS, Université d'Angers, Université de Nantes, France
| | - Marina Filipović
- Laboratoire d'Imagerie Biomédicale Multimodale (BioMaps), Université Paris-Saclay, CEA, CNRS, Inserm, Service Hospitalier Frédéric Joliot, Orsay, France
| | - Florent Sureau
- Laboratoire d'Imagerie Biomédicale Multimodale (BioMaps), Université Paris-Saclay, CEA, CNRS, Inserm, Service Hospitalier Frédéric Joliot, Orsay, France
| | - Claude Comtat
- Laboratoire d'Imagerie Biomédicale Multimodale (BioMaps), Université Paris-Saclay, CEA, CNRS, Inserm, Service Hospitalier Frédéric Joliot, Orsay, France
| |
Collapse
|
26
|
Wu J, Liu H, Ye Q, Gallezot JD, Naganawa M, Miao T, Lu Y, Chen MK, Esserman DA, Kyriakides TC, Carson RE, Liu C. Generation of parametric K i images for FDG PET using two 5-min scans. Med Phys 2021; 48:5219-5231. [PMID: 34287939 DOI: 10.1002/mp.15113] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 06/23/2021] [Accepted: 07/08/2021] [Indexed: 12/13/2022] Open
Abstract
PURPOSE The net uptake rate constant (Ki ) derived from dynamic imaging is considered the gold standard quantification index for FDG PET. In this study, we investigated the feasibility and assessed the clinical usefulness of generating Ki images for FDG PET using only two 5-min scans with population-based input function (PBIF). METHODS Using a Siemens Biograph mCT, 10 subjects with solid lung nodules underwent a single-bed dynamic FDG PET scan and 13 subjects (five healthy and eight cancer patients) underwent a whole-body dynamic FDG PET scan in continuous-bed-motion mode. For each subject, a standard Ki image was generated using the complete 0-90 min dynamic data with Patlak analysis (t* = 20 min) and individual patient's input function, while a dual-time-point Ki image was generated from two 5-min scans based on the Patlak equations at early and late scans with the PBIF. Different start times for the early (ranging from 20 to 55 min with an increment of 5 min) and late (ranging from 50 to 85 min with an increment of 5 min) scans were investigated with the interval between scans being at least 30 min (36 protocols in total). The optimal dual-time-point protocols were then identified. Regions of interest (ROI) were drawn on nodules for the lung nodule subjects, and on tumors, cerebellum, and bone marrow for the whole-body-imaging subjects. Quantification accuracy was compared using the mean value of each ROI between standard Ki (gold standard) and dual-time-point Ki , as well as between standard Ki and relative standardized uptake value (SUV) change that is currently used in clinical practice. Correlation coefficients and least squares fits were calculated for each dual-time-point protocol and for each ROI. Then, the predefined criteria for identifying a reliable dual-time-point Ki estimation for each ROI were empirically determined as: (1) the squared correlation coefficient (R2 ) between standard Ki and dual-time-point Ki is larger than 0.9; (2) the absolute difference between the slope of the equality line (1.0) and that of the fitted line when plotting standard Ki versus dual-time-point Ki is smaller than 0.1; (3) the absolute value of the intercept of the fitted line when plotting standard Ki versus dual-time-point Ki normalized by the mean of the standard Ki across all subjects for each ROI is smaller than 10%. Using Williams' one-tailed t test, the correlation coefficient (R) between standard Ki and dual-time-point Ki was further compared with that between standard Ki and relative SUV change, for each dual-time-point protocol and for each ROI. RESULTS Reliable dual-time-point Ki images were obtained for all the subjects using our proposed method. The percentage error introduced by the PBIF on the dual-time-point Ki estimation was smaller than 1% for all 36 protocols. Using the predefined criteria, reliable dual-time-point Ki estimation could be obtained in 25 of 36 protocols for nodules and in 34 of 36 protocols for tumors. A longer time interval between scans provided a more accurate Ki estimation in general. Using the protocol of 20-25 min plus 80-85 or 85-90 min, very high correlations were obtained between standard Ki and dual-time-point Ki (R2 = 0.994, 0.980, 0.971 and 0.925 for nodule, tumor, cerebellum, and bone marrow), with all the slope values with differences ≤0.033 from 1 and all the intercept values with differences ≤0.0006 mL/min/cm3 from 0. The corresponding correlations were much lower between standard Ki and relative SUV change (R2 = 0.673, 0.684, 0.065, 0.246). Dual-time-point Ki showed a significantly higher quantification accuracy with respect to standard Ki than relative SUV change for all the 36 protocols (p < 0.05 using Williams' one-tailed t test). CONCLUSIONS Our proposed approach can obtain reliable Ki images and accurate Ki quantification from dual-time-point scans (5-min per scan), and provide significantly higher quantification accuracy than relative SUV change that is currently used in clinical practice.
Collapse
Affiliation(s)
- Jing Wu
- Center for Advanced Quantum Studies and Department of Physics, Beijing Normal University, Beijing, China.,Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Hui Liu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.,Department of Engineering Physics, Tsinghua University, Beijing, China.,Key Laboratory of Particle & Radiation Imaging, Ministry of Education (Tsinghua University), Beijing, China
| | - Qing Ye
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.,Department of Engineering Physics, Tsinghua University, Beijing, China.,Key Laboratory of Particle & Radiation Imaging, Ministry of Education (Tsinghua University), Beijing, China
| | | | - Mika Naganawa
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Tianshun Miao
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Yihuan Lu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Ming-Kai Chen
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Denise A Esserman
- School of Public Health: Biostatistics, Yale University, New Haven, CT, USA
| | | | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Chi Liu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| |
Collapse
|
27
|
Xie N, Gong K, Guo N, Qin Z, Wu Z, Liu H, Li Q. Rapid high-quality PET Patlak parametric image generation based on direct reconstruction and temporal nonlocal neural network. Neuroimage 2021; 240:118380. [PMID: 34252526 DOI: 10.1016/j.neuroimage.2021.118380] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 07/04/2021] [Accepted: 07/07/2021] [Indexed: 11/25/2022] Open
Abstract
Parametric imaging based on dynamic positron emission tomography (PET) has wide applications in neurology. Compared to indirect methods, direct reconstruction methods, which reconstruct parametric images directly from the raw PET data, have superior image quality due to better noise modeling and richer information extracted from the PET raw data. For low-dose scenarios, the advantages of direct methods are more obvious. However, the wide adoption of direct reconstruction is inevitably impeded by the excessive computational demand and deficiency of the accessible raw data. In addition, motion modeling inside dynamic PET image reconstruction raises more computational challenges for direct reconstruction methods. In this work, we focused on the 18F-FDG Patlak model, and proposed a data-driven approach which can estimate the motion corrected full-dose direct Patlak images from the dynamic PET reconstruction series, based on a proposed novel temporal non-local convolutional neural network. During network training, direct reconstruction with motion correction based on full-dose dynamic PET sinograms was performed to obtain the training labels. The reconstructed full-dose /low-dose dynamic PET images were supplied as the network input. In addition, a temporal non-local block based on the dynamic PET images was proposed to better recover the structural information and reduce the image noise. During testing, the proposed network can directly output high-quality Patlak parametric images from the full-dose /low-dose dynamic PET images in seconds. Experiments based on 15 full-dose and 15 low-dose 18F-FDG brain datasets were conducted and analyzed to validate the feasibility of the proposed framework. Results show that the proposed framework can generate better image quality than reference methods.
Collapse
Affiliation(s)
- Nuobei Xie
- College of Optical Science and Engineering, Zhejiang University, 38 Zheda Road, Building 3, Hangzhou 310027, China; Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, White 427, 125 Nashua Street, Suite 660, Boston, MA 02114, United States.
| | - Kuang Gong
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, White 427, 125 Nashua Street, Suite 660, Boston, MA 02114, United States.
| | - Ning Guo
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, White 427, 125 Nashua Street, Suite 660, Boston, MA 02114, United States
| | - Zhixing Qin
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Zhifang Wu
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Huafeng Liu
- College of Optical Science and Engineering, Zhejiang University, 38 Zheda Road, Building 3, Hangzhou 310027, China.
| | - Quanzheng Li
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, White 427, 125 Nashua Street, Suite 660, Boston, MA 02114, United States.
| |
Collapse
|
28
|
Mao X, Zhao S, Gao D, Hu Z, Zhang N. Direct and indirect parameter imaging methods for dynamic PET. Biomed Phys Eng Express 2021; 7. [PMID: 34087810 DOI: 10.1088/2057-1976/ac086c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/04/2021] [Indexed: 01/04/2023]
Abstract
The method of reconstructing parametric images from dynamic positron emission tomography (PET) data with the linear Patlak model has been widely used in scientific research and clinical practice. Whether for direct or indirect image reconstruction, researchers have deeply investigated the associated methods and effects. Among the existing methods, the traditional maximum likelihood expectation maximization (MLEM) reconstruction algorithm is fast but produces a substantial amount of noise. If the parameter images obtained by the MLEM algorithm are postfiltered, a large amount of image edge information is lost. Additionally, although the kernel method has a better noise reduction effect, its calculation costs are very high due to the complexity of the algorithm. Therefore, to obtain parametric images with a high signal-to-noise ratio (SNR) and good retention of detailed information, here, we use guided kernel means (GKM) and dynamic PET image information to conduct guided filtering and perform parametric image reconstruction. We apply this method to direct and indirect reconstruction, and through computer simulations, we show that our proposed method has higher identifiability and a greater SNR than conventional direct and indirect reconstruction methods. We also show that our method produces better images with direct than with indirect reconstruction.
Collapse
Affiliation(s)
- Xin Mao
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou 450001, People's Republic of China.,Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
| | - Shujun Zhao
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou 450001, People's Republic of China
| | - Dongfang Gao
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
| | - Na Zhang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
| |
Collapse
|
29
|
|
30
|
Rowe SP, Solnes LB, Yin Y, Kitchen G, Lodge MA, Karakatsanis NA, Rahmim A, Pomper MG, Leal JP. Imager-4D: New Software for Viewing Dynamic PET Scans and Extracting Radiomic Parameters from PET Data. J Digit Imaging 2021; 32:1071-1080. [PMID: 31388864 DOI: 10.1007/s10278-019-00255-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Extensive research is currently being conducted into dynamic positron emission tomography (PET) acquisitions (including dynamic whole-body imaging) as well as extraction of radiomic features from imaging modalities. We describe a new PET viewing software known as Imager-4D that provides a facile means of viewing and analyzing dynamic PET data and obtaining associated quantitative metrics including radiomic parameters. The Imager-4D was programmed in the Java language utilizing the FX extensions. It is executable on any system for which a Java w/FX compliant virtual machine is available. The software incorporates the ability to view and analyze dynamic data acquired with different types of dynamic protocols. For image display, the program maintains a built-in library of 62 different lookup tables with monochromatic and full-color distributions. The Imager-4D provides multiple display layouts and can display fused images. Multiple methods of volume-of-interest (VOI) selection are available. Dynamic analysis features, such as image summation and full Patlak analysis, are also available. The user interface includes window width and level, blending, and zoom functionality. VOI sizes are adjustable and data from VOIs can either be displayed numerically or graphically within the software or exported. An example case of a 50-year-old woman with metastatic colorectal cancer and thyroiditis is included and demonstrates the steps for a user to obtain standard PET parameters, dynamic data, and radiomic features using selected VOIs. The Imager-4D represents a novel PET viewer that allows the user to view dynamic PET data, to derive dynamic and radiomic parameters from that data, and to combine dynamic data with radiomics ("dynomics"). The Imager-4D is available as a free download. This software has the potential to speed the adoption of advanced analysis of dynamic PET data into routine clinical use.
Collapse
Affiliation(s)
- Steven P Rowe
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 550 N. Broadway Suite 300, Baltimore, MD, 21205, USA. .,The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline JHOC 3233, Baltimore, MD, 21287, USA.
| | - Lilja B Solnes
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 550 N. Broadway Suite 300, Baltimore, MD, 21205, USA
| | - Yafu Yin
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 550 N. Broadway Suite 300, Baltimore, MD, 21205, USA.,Department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Grant Kitchen
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA
| | - Martin A Lodge
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 550 N. Broadway Suite 300, Baltimore, MD, 21205, USA
| | - Nicolas A Karakatsanis
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 550 N. Broadway Suite 300, Baltimore, MD, 21205, USA.,Department of Radiology, Weill Cornell Medical College of Cornell University, New York, NY, USA
| | - Arman Rahmim
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 550 N. Broadway Suite 300, Baltimore, MD, 21205, USA.,Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| | - Martin G Pomper
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 550 N. Broadway Suite 300, Baltimore, MD, 21205, USA
| | - Jeffrey P Leal
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 550 N. Broadway Suite 300, Baltimore, MD, 21205, USA.
| |
Collapse
|
31
|
Prospective study of dynamic whole-body 68Ga-DOTATOC-PET/CT acquisition in patients with well-differentiated neuroendocrine tumors. Sci Rep 2021; 11:4727. [PMID: 33649421 PMCID: PMC7921579 DOI: 10.1038/s41598-021-83965-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 02/04/2021] [Indexed: 12/04/2022] Open
Abstract
To present the feasibility of a dynamic whole-body (DWB) 68Ga-DOTATOC-PET/CT acquisition in patients with well-differentiated neuroendocrine tumors (WD-NETs). Sixty-one patients who underwent a DWB 68Ga-DOTATOC-PET/CT for a histologically proven/highly suspected WD-NET were prospectively included. The acquisition consisted in single-bed dynamic acquisition centered on the heart, followed by the DWB and static acquisitions. For liver, spleen and tumor (1–5/patient), Ki values (in ml/min/100 ml) were calculated according to Patlak's analysis and tumor-to-liver (TLR-Ki) and tumor-to-spleen ratios (TSR-Ki) were recorded. Ki-based parameters were compared to static parameters (SUVmax/SUVmean, TLR/TSRmean, according to liver/spleen SUVmean), in the whole-cohort and according to the PET system (analog/digital). A correlation analysis between SUVmean/Ki was performed using linear and non-linear regressions. Ki-liver was not influenced by the PET system used, unlike SUVmax/SUVmean. The regression analysis showed a non-linear relation between Ki/SUVmean (R2 = 0.55,0.68 and 0.71 for liver, spleen and tumor uptake, respectively) and a linear relation between TLRmean/TLR-Ki (R2 = 0.75). These results were not affected by the PET system, on the contrary of the relation between TSRmean/TSR-Ki (R2 = 0.94 and 0.73 using linear and non-linear regressions in digital and analog systems, respectively). Our study is the first showing the feasibility of a DWB 68Ga-DOTATOC-PET/CT acquisition in WD-NETs.
Collapse
|
32
|
Hu J, Panin V, Smith AM, Spottiswoode B, Shah V, CA von Gall C, Baker M, Howe W, Kehren F, Casey M, Bendriem B. Design and Implementation of Automated Clinical Whole Body Parametric PET With Continuous Bed Motion. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2020. [DOI: 10.1109/trpms.2020.2994316] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
33
|
Wang G, Rahmim A, Gunn RN. PET Parametric Imaging: Past, Present, and Future. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2020; 4:663-675. [PMID: 33763624 PMCID: PMC7983029 DOI: 10.1109/trpms.2020.3025086] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Positron emission tomography (PET) is actively used in a diverse range of applications in oncology, cardiology, and neurology. The use of PET in the clinical setting focuses on static (single time frame) imaging at a specific time-point post radiotracer injection and is typically considered as semi-quantitative; e.g. standardized uptake value (SUV) measures. In contrast, dynamic PET imaging requires increased acquisition times but has the advantage that it measures the full spatiotemporal distribution of a radiotracer and, in combination with tracer kinetic modeling, enables the generation of multiparametric images that more directly quantify underlying biological parameters of interest, such as blood flow, glucose metabolism, and receptor binding. Parametric images have the potential for improved detection and for more accurate and earlier therapeutic response assessment. Parametric imaging with dynamic PET has witnessed extensive research in the past four decades. In this paper, we provide an overview of past and present activities and discuss emerging opportunities in the field of parametric imaging for the future.
Collapse
Affiliation(s)
- Guobao Wang
- Department of Radiology, University of California Davis Health, Sacramento, CA 95817, USA
| | - Arman Rahmim
- University of British Columbia, Vancouver, BC, Canada
| | | |
Collapse
|
34
|
|
35
|
|
36
|
Viswanath V, Pantel AR, Daube-Witherspoon ME, Doot R, Muzi M, Mankoff DA, Karp JS. Quantifying bias and precision of kinetic parameter estimation on the PennPET Explorer, a long axial field-of-view scanner. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2020; 4:735-749. [PMID: 33225120 DOI: 10.1109/trpms.2020.3021315] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Long axial field-of-view (AFOV) PET scanners allow for full-body dynamic imaging in a single bed-position at very high sensitivity. However, the benefits for kinetic parameter estimation have yet to be studied. This work uses (1) a dynamic GATE simulation of [18F]-fluorothymidine (FLT) in a modified NEMA IQ phantom and (2) a lesion embedding study of spheres in a dynamic [18F]-fluorodeoxyglucose (FDG) human subject imaged on the PennPET Explorer. Both studies were designed using published kinetic data of lung and liver cancers and modeled using two tissue compartments. Data were reconstructed at various emulated doses. Sphere time-activity curves (TACs) were measured on resulting dynamic images, and TACs were fit using a two-tissue-compartment model (k4 ≠ 0) for the FLT study and both a two-tissue-compartment model (k4 = 0) and Patlak graphical analysis for the FDG study to estimate flux (Ki) and delivery (K1) parameters. Quantification of flux and K1 shows lower bias and better precision for both radiotracers on the long AFOV scanner, especially at low doses. Dynamic imaging on a long AFOV system can be achieved for a greater range of injected doses, as low as 0.5-2 mCi depending on the sphere size and flux, compared to a standard AFOV scanner, while maintaining good kinetic parameter estimation.
Collapse
Affiliation(s)
- Varsha Viswanath
- Bioengineering Department, University of Pennsylvania, Philadelphia, PA 19104
| | - Austin R Pantel
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104
| | | | - Robert Doot
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA 98195
| | - David A Mankoff
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104
| | - Joel S Karp
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104
| |
Collapse
|
37
|
Karakatsanis NA, Abgral R, Trivieri MG, Dweck MR, Robson PM, Calcagno C, Boeykens G, Senders ML, Mulder WJM, Tsoumpas C, Fayad ZA. Hybrid PET- and MR-driven attenuation correction for enhanced 18F-NaF and 18F-FDG quantification in cardiovascular PET/MR imaging. J Nucl Cardiol 2020; 27:1126-1141. [PMID: 31667675 PMCID: PMC7190435 DOI: 10.1007/s12350-019-01928-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 10/02/2019] [Accepted: 10/02/2019] [Indexed: 10/25/2022]
Abstract
BACKGROUND The standard MR Dixon-based attenuation correction (AC) method in positron emission tomography/magnetic resonance (PET/MR) imaging segments only the air, lung, fat and soft-tissues (4-class), thus neglecting the highly attenuating bone tissues and affecting quantification in bones and adjacent vessels. We sought to address this limitation by utilizing the distinctively high bone uptake rate constant Ki expected from 18F-Sodium Fluoride (18F-NaF) to segment bones from PET data and support 5-class hybrid PET/MR-driven AC for 18F-NaF and 18F-Fluorodeoxyglucose (18F-FDG) PET/MR cardiovascular imaging. METHODS We introduce 5-class Ki/MR-AC for (i) 18F-NaF studies where the bones are segmented from Patlak Ki images and added as the 5th tissue class to the MR Dixon 4-class AC map. Furthermore, we propose two alternative dual-tracer protocols to permit 5-class Ki/MR-AC for (ii) 18F-FDG-only data, with a streamlined simultaneous administration of 18F-FDG and 18F-NaF at 4:1 ratio (R4:1), or (iii) for 18F-FDG-only or both 18F-FDG and 18F-NaF dual-tracer data, by administering 18F-NaF 90 minutes after an equal 18F-FDG dosage (R1:1). The Ki-driven bone segmentation was validated against computed tomography (CT)-based segmentation in rabbits, followed by PET/MR validation on 108 vertebral bone and carotid wall regions in 16 human volunteers with and without prior indication of carotid atherosclerosis disease (CAD). RESULTS In rabbits, we observed similar (< 1.2% mean difference) vertebral bone 18F-NaF SUVmean scores when applying 5-class AC with Ki-segmented bone (5-class Ki/CT-AC) vs CT-segmented bone (5-class CT-AC) tissue. Considering the PET data corrected with continuous CT-AC maps as gold-standard, the percentage SUVmean bias was reduced by 17.6% (18F-NaF) and 15.4% (R4:1) with 5-class Ki/CT-AC vs 4-class CT-AC. In humans without prior CAD indication, we reported 17.7% and 20% higher 18F-NaF target-to-background ratio (TBR) at carotid bifurcations wall and vertebral bones, respectively, with 5- vs 4-class AC. In the R4:1 human cohort, the mean 18F-FDG:18F-NaF TBR increased by 12.2% at carotid bifurcations wall and 19.9% at vertebral bones. For the R1:1 cohort of subjects without CAD indication, mean TBR increased by 15.3% (18F-FDG) and 15.5% (18F-NaF) at carotid bifurcations and 21.6% (18F-FDG) and 22.5% (18F-NaF) at vertebral bones. Similar TBR enhancements were observed when applying the proposed AC method to human subjects with prior CAD indication. CONCLUSIONS Ki-driven bone segmentation and 5-class hybrid PET/MR-driven AC is feasible and can significantly enhance 18F-NaF and 18F-FDG contrast and quantification in bone tissues and carotid walls.
Collapse
Affiliation(s)
- Nicolas A Karakatsanis
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA.
- Department of Radiology, Weill Cornell Medical College, Cornell University, 515 E 71st Street, S-120, New York, NY, 10021, USA.
| | - Ronan Abgral
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA
- Department of Nuclear Medicine, University Hospital of Brest, Brest, France
| | - Maria Giovanna Trivieri
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA
| | - Marc R Dweck
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA
- British Heart Foundation, Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Philip M Robson
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA
| | - Claudia Calcagno
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA
| | - Gilles Boeykens
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA
- Department of Medical Biochemistry, Academic Medical Center, Amsterdam, The Netherlands
| | - Max L Senders
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA
- Department of Medical Biochemistry, Academic Medical Center, Amsterdam, The Netherlands
| | - Willem J M Mulder
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA
- Department of Medical Biochemistry, Academic Medical Center, Amsterdam, The Netherlands
| | - Charalampos Tsoumpas
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA
- Biomedical Imaging Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Zahi A Fayad
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA
| |
Collapse
|
38
|
Assessment of Lesion Detectability in Dynamic Whole-Body PET Imaging Using Compartmental and Patlak Parametric Mapping. Clin Nucl Med 2020; 45:e221-e231. [PMID: 32108696 DOI: 10.1097/rlu.0000000000002954] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE Hybrid dynamic imaging allows not only the estimation of whole-body (WB) macroparametric maps but also the estimation of microparameters in the initial bed position targeting the blood pool region containing the pathology owing to the limited axial field of view of PET scanners. In this work, we assessed the capability of multipass WB F-FDG PET parametric imaging in terms of lesion detectability through qualitative and quantitative evaluation of simulation and clinical studies. METHODS Simulation studies were conducted by generating data incorporating 3 liver and 3 lung lesions produced by 3 noise levels and 20 noise realizations for each noise level to estimate bias and lesion detection features. The total scan time for the clinical studies of 8 patients addressed for lung and liver lesions staging, including dynamic and static WB imaging, lasted 80 minutes. An in-house-developed MATLAB code was utilized to derive the microparametric and macroparametric maps. We compared lesion detectability and different image-derived PET metrics including the SUVs, Patlak-derived influx rate constant (Ki) and distribution volume (V) and K1, k2, k3, blood volume (bv) microparameters, and Ki estimated using the generalized linear least square approach. RESULTS In total, 104 lesions were detected, among which 47 were located in the targeted blood pool bed position where all quantitative parameters were calculated, thus enabling comparative analysis across all parameters. The evaluation encompassed visual interpretation performed by an expert nuclear medicine specialist and quantitative analysis. High correlation coefficients were observed between SUVmax and Kimax derived from the generalized linear least square approach, as well as Ki generated by Patlak graphical analysis. Moreover, 3 contrast-enhanced CT-proven malignant lesions located in the liver and a biopsy-proven malignant liver lesion not visible on static SUV images and Patlak maps were clearly pinpointed on K1 and k2 maps. CONCLUSIONS Our results demonstrate that full compartmental modeling for the region containing the pathology has the potential of providing complementary information and, in some cases, more accurate diagnosis than conventional static SUV imaging, favorably comparing to Patlak graphical analysis.
Collapse
|
39
|
Dimitrakopoulou-Strauss A, Pan L, Sachpekidis C. Kinetic modeling and parametric imaging with dynamic PET for oncological applications: general considerations, current clinical applications, and future perspectives. Eur J Nucl Med Mol Imaging 2020; 48:21-39. [PMID: 32430580 PMCID: PMC7835173 DOI: 10.1007/s00259-020-04843-6] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 04/27/2020] [Indexed: 02/07/2023]
Abstract
Dynamic PET (dPET) studies have been used until now primarily within research purposes. Although it is generally accepted that the information provided by dPET is superior to that of conventional static PET acquisitions acquired usually 60 min post injection of the radiotracer, the duration of dynamic protocols, the limited axial field of view (FOV) of current generation clinical PET systems covering a relatively small axial extent of the human body for a dynamic measurement, and the complexity of data evaluation have hampered its implementation into clinical routine. However, the development of new-generation PET/CT scanners with an extended FOV as well as of more sophisticated evaluation software packages that offer better segmentation algorithms, automatic retrieval of the arterial input function, and automatic calculation of parametric imaging, in combination with dedicated shorter dynamic protocols, will facilitate the wider use of dPET. This is expected to aid in oncological diagnostics and therapy assessment. The aim of this review is to present some general considerations about dPET analysis in oncology by means of kinetic modeling, based on compartmental and noncompartmental approaches, and parametric imaging. Moreover, the current clinical applications and future perspectives of the modality are outlined.
Collapse
Affiliation(s)
- Antonia Dimitrakopoulou-Strauss
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
| | - Leyun Pan
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Christos Sachpekidis
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| |
Collapse
|
40
|
Applied Systems Biology-embracing molecular imaging for systemic medicine. Eur J Nucl Med Mol Imaging 2020; 47:2721-2725. [PMID: 32266456 DOI: 10.1007/s00259-020-04798-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
41
|
Nishimura M, Tamaki N, Matsushima S, Kiba M, Kotani T, Bamba C, Nakamura Y, Yamada K. Dynamic whole-body 18F-FDG PET for differentiating abnormal lesions from physiological uptake. Eur J Nucl Med Mol Imaging 2020; 47:2293-2300. [PMID: 32189027 DOI: 10.1007/s00259-020-04726-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 02/14/2020] [Indexed: 01/23/2023]
Abstract
PURPOSE Serial assessment of visual change in 18F-FDG uptake on whole-body 18F-FDG PET imaging was performed to differentiate pathological uptake from physiological uptake in the urinary and gastrointestinal tracts. METHODS In 88 suspected cancer patients, serial 3-min dynamic whole-body PET imaging was performed four times, from 60 min after 18F-FDG administration. In dynamic image evaluation, high 18F-FDG uptake was evaluated by two nuclear medicine physicians and classified as "changed" or "unchanged" based on change in uptake shape over time. Detectability of pathological uptake based on these criteria was assessed and compared with conventional image evaluation. RESULTS Dynamic whole-body PET imaging provided images of adequate quality for visual assessment. Dynamic image evaluation was "changed" in 118/154 regions of high physiological 18F-FDG uptake (77%): in 9/19 areas in the stomach (47%), in 32/39 in the small intestine (82%), in 17/33 in the colon (52%), and in 60/63 in the urinary tract (95%). In the 86 benign or malignant lesions, 84 lesions (98%) were "unchanged." A high 18F-FDG uptake area that shows no change over time using these criteria is highly likely to represent pathological uptake, with sensitivity of 97%, specificity of 76%, PPV of 70%, NPV of 98%, and accuracy of 84%. CONCLUSION Dynamic whole-body 18F-FDG PET imaging enabled differentiation of pathological uptake from physiological uptake in the urinary and gastrointestinal tracts, based on visual change of uptake shape.
Collapse
Affiliation(s)
- Motoki Nishimura
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Nagara Tamaki
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
| | - Shigenori Matsushima
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Maki Kiba
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Tomoya Kotani
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Chisa Bamba
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yasunori Nakamura
- Department of Radiological Technology, University Hospital, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Kei Yamada
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| |
Collapse
|
42
|
Zhuang M, Karakatsanis NA, Dierckx RAJO, Zaidi H. Impact of Tissue Classification in MRI-Guided Attenuation Correction on Whole-Body Patlak PET/MRI. Mol Imaging Biol 2019; 21:1147-1156. [PMID: 30838550 DOI: 10.1007/s11307-019-01338-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
PURPOSE The aim of this work is to investigate the impact of tissue classification in magnetic resonance imaging (MRI)-guided positron emission tomography (PET) attenuation correction (AC) for whole-body (WB) Patlak net uptake rate constant (Ki) imaging in PET/MRI studies. PROCEDURES WB dynamic PET/CT data were acquired for 14 patients. The CT images were utilized to generate attenuation maps (μ-mapCTAC) of continuous attenuation coefficient values (Acoeff). The μ-mapCTAC were then segmented into four tissue classes (μ-map4-classes), namely background (air), lung, fat, and soft tissue, where a predefined Acoeff was assigned to each class. To assess the impact of bone for AC, the bones in the μ-mapCTAC were then assigned a predefined soft tissue Acoeff (0.1 cm-1) to produce an AC μ-map without bones (μ-mapno-bones). Thereafter, both WB static SUV and dynamic PET images were reconstructed using μ-mapCTAC, μ-map4-classes, and μ-mapno-bones (PETCTAC, PET4-classes, and PETno-bones), respectively. WB indirect and direct parametric Ki images were generated using Patlak graphical analysis. Malignant lesions were delineated on PET images with an automatic segmentation method that uses an active contour model (MASAC). Then, the quantitative metrics of the metabolically active tumor volume (MATV), target-to-background (TBR), contrast-to-noise ratio (CNR), peak region-of-interest (ROIpeak), maximum region-of-interest (ROImax), mean region-of-interest (ROImean), and metabolic volume product (MVP) were analyzed. The Wilcoxon test was conducted to assess the difference between PET4-classes and PETno-bones against PETCTAC for all images. The same test was also adopted to compare the differences between SUV, indirect Ki, and direct Ki images for each evaluated AC method. RESULTS No significant differences in MATV, TBR, and CNR were observed between PET4-classes and PETCTAC for either SUV or Ki images. PET4-classes significantly overestimated ROIpeak, ROImax, ROImean, as well as MVP scores compared with PETCTAC in both SUV and Ki images. SUV images exhibited the highest median relative errors for PET4-classes with respect to PETCTAC (RE4-classes): 6.91 %, 6.55 %, 5.90 %, and 6.56 % for ROIpeak, ROImax, ROImean, and MVP, respectively. On the contrary, Ki images showed slightly reduced RE4-classes (indirect 5.52 %, 5.95 %, 4.43 %, and 5.70 %, direct 6.61 %, 6.33 %, 5.53 %, and 4.96 %) for ROIpeak, ROImax, ROImean, and MVP, respectively. A higher TBR was observed on indirect and direct Ki images relative to SUV, while direct Ki images demonstrated the highest CNR. CONCLUSIONS Four-tissue class AC may impact SUV and Ki parameter estimation but only to a limited extent, thereby suggesting that WB Patlak Ki imaging for dynamic WB PET/MRI studies is feasible. Patlak Ki imaging can enhance TBR, thereby facilitating lesion segmentation and quantification. However, patient-specific Acoeff for each tissue class should be used when possible to address the high inter-patient variability of Acoeff distributions.
Collapse
Affiliation(s)
- Mingzan Zhuang
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center, 9700 RB, Groningen, The Netherlands.,Department of Radiation Oncology, Affiliated Hospital of Yangzhou University, Yangzhou, 225012, Jiangsu, China
| | - Nicolas A Karakatsanis
- Division of Radiopharmaceutical Sciences, Department of Radiology, Weill Cornell Medical College, Cornell University, New York, NY, 10021, USA
| | - Rudi A J O Dierckx
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center, 9700 RB, Groningen, The Netherlands
| | - Habib Zaidi
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center, 9700 RB, Groningen, The Netherlands. .,Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland. .,Geneva University Neurocenter, University of Geneva, 1205, Geneva, Switzerland. .,Department of Nuclear Medicine, University of Southern Denmark, 500, Odense, Denmark.
| |
Collapse
|
43
|
Yamamoto H, Takemoto S, Maebatake A, Karube S, Yamashiro Y, Nakanishi A, Murakami K. Verification of image quality and quantification in whole-body positron emission tomography with continuous bed motion. Ann Nucl Med 2019; 33:288-294. [PMID: 30707349 DOI: 10.1007/s12149-019-01334-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 01/14/2019] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Whole-body dynamic imaging using positron emission tomography (PET) facilitates the quantification of tracer kinetics. It is potentially valuable for the differential diagnosis of tumors and for the evaluation of therapeutic efficacy. In whole-body dynamic PET with continuous bed motion (CBM) (WBDCBM-PET), the pass number and bed velocity are key considerations. In the present study, we aimed to investigate the effect of a combination of pass number and bed velocity on the quantitative accuracy and quality of WBDCBM-PET images. METHODS In this study, WBDCBM-PET imaging was performed at a body phantom using seven bed velocity settings in combination with pass numbers. The resulting image quality was evaluated. For comparing different acquisition settings, the dynamic index (DI) was obtained using the following formula: [P/S], where P represents the pass number, and S represents the bed velocity (mm/s). The following physical parameters were evaluated: noise equivalent count at phantom (NECphantom), percent background variability (N10 mm), percent contrast of the 10 mm hot sphere (QH, 10 mm), the QH, 10 mm/N10 mm ratio, and the maximum standardized uptake value (SUVmax). Furthermore, visual evaluation was performed. RESULTS The NECphantom was equivalent for the same DI settings regardless of the bed velocity. The N10 mm exhibited an inverse correlation (r < - 0.89) with the DI. QH,10 mm was not affected by DI, and a correlation between QH,10 mm/N10 mm ratio and DI was found at all the velocities (r > 0.93). The SUVmax of the spheres was not influenced by the DI. The coefficient of variations caused by bed velocity decreased in larger spheres. There was no significant difference between the bed velocities on visual evaluation. CONCLUSION The quantitative accuracy and image quality achieved with WBDCBM-PET was comparable to that achieved with non-dynamic CBM, regardless of the pass number and bed velocity used during imaging for a given acquisition time.
Collapse
Affiliation(s)
- Hideo Yamamoto
- Department of Radiology, Juntendo University School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
| | - Shota Takemoto
- Department of Radiology, Juntendo University School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Akira Maebatake
- Department of Radiology, Juntendo University School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Shuhei Karube
- Department of Radiology, Juntendo University School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Yuki Yamashiro
- Department of Radiology, Juntendo University School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Atsushi Nakanishi
- Department of Radiology, Juntendo University School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Koji Murakami
- Department of Radiology, Juntendo University School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| |
Collapse
|
44
|
Fahrni G, Karakatsanis NA, Di Domenicantonio G, Garibotto V, Zaidi H. Does whole-body Patlak 18F-FDG PET imaging improve lesion detectability in clinical oncology? Eur Radiol 2019; 29:4812-4821. [PMID: 30689031 DOI: 10.1007/s00330-018-5966-1] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 11/07/2018] [Accepted: 12/11/2018] [Indexed: 11/28/2022]
Abstract
PURPOSE Single-pass whole-body (WB) 18F-FDG PET/CT imaging is routinely employed for the clinical assessment of malignant, infectious, and inflammatory diseases. Our aim in this study is the systematic clinical assessment of lesion detectability in multi-pass WB parametric imaging enabling direct imaging of the highly quantitative 18F-FDG influx rate constant Ki, as a complement to standard-of-care standardized uptake value (SUV) imaging for a range of oncologic studies. METHODS We compared SUV and Ki images of 18 clinical studies of different oncologic indications (lesion characterization and staging) including standard-of-care SUV and dynamic WB PET protocols in a single session. The comparison involved both the visual assessment and the quantitative evaluation of SUVmean, SUVmax, Kimean, Kimax, tumor-to-background ratio (TBRSUV, TBRKi), and contrast-to-noise ratio (CNRSUV, CNRKi) quality metrics. RESULTS Overall, both methods provided good-quality images suitable for visual interpretation. A total of 118 lesions were detected, including 40 malignant (proven) and 78 malignant (unproven) lesions. Of those, 111 were detected on SUV and 108 on Ki images. One proven malignant lesion was detected only on Ki images whereas none of the proven malignant lesions was visible only on SUV images. The proven malignant lesions had overall higher Ki TBR and CNR scores. One unproven lesion, which was later confirmed as benign, was detected only on the SUV images (false-positive). Overall, our results from 40 proven malignant lesions suggested improved sensitivity (from 92.5 to 95%) and accuracy (from 90.24 to 95.12%) and potentially enhanced specificity with Ki over SUV imaging. CONCLUSION Oncologic WB Patlak Ki imaging may achieve equivalent or superior lesion detectability with reduced false-positive rates when complementing standard-of-care SUV imaging. KEY POINTS • The whole-body spatio-temporal distribution of 18 F-FDG uptake may reveal clinically useful information on oncologic diseases to complement the standard-of-care SUV metric. • Parametric imaging resulted in less false-positive indications of non-specific 18 F-FDG uptake relative to SUV. • Parametric imaging may achieve equivalent or superior 18 F-FDG lesion detectability than standard-of-care SUV imaging in oncology.
Collapse
Affiliation(s)
- Guillaume Fahrni
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Nicolas A Karakatsanis
- Division of Radiopharmaceutical Sciences, Department of Radiology, Weill Cornell Medical College of Cornell University, New York, NY, 10021, USA.
| | - Giulia Di Domenicantonio
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.,Faculty of Medicine, University of Geneva, 1205, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland. .,Geneva University Neurocenter, University of Geneva, 1205, Geneva, Switzerland. .,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, 9700 RB, Groningen, Netherlands. .,Department of Nuclear Medicine, University of Southern Denmark, DK-500, Odense, Denmark.
| |
Collapse
|
45
|
Cui J, Yu H, Chen S, Chen Y, Liu H. Simultaneous estimation and segmentation from projection data in dynamic PET. Med Phys 2018; 46:1245-1259. [PMID: 30593666 DOI: 10.1002/mp.13364] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 12/17/2018] [Accepted: 12/17/2018] [Indexed: 11/12/2022] Open
Abstract
PURPOSE Dynamic positron emission tomography (PET) is known for its ability to extract spatiotemporal information of a radio tracer in living tissue. Information of different functional regions based on an accurate reconstruction of the activity images and kinetic parametric images has been widely studied and can be useful in research and clinical setting for diagnosis and other quantitative tasks. In this paper, our purpose is to present a novel framework for estimating the kinetic parametric images directly from the raw measurement data together with a simultaneous segmentation accomplished through kinetic parameters clustering. METHOD An iterative framework is proposed to estimate the kinetic parameter image, activity map and do the segmentation simultaneously from the complete dynamic PET projection data. The clustering process is applied to the kinetic parameter variable rather than to the traditional activity distribution so as to achieve accurate discrimination between different functional areas. Prior information such as total variation regularization is incorporated to reduce the noise in the PET images and a sparseness constraint is integrated to guarantee the solution for kinetic parameters due to the over complete dictionary. Alternating direction method of multipliers (ADMM) method is used to solve the optimization problem. The proposed algorithm was validated with experiments on Monte Carlo-simulated phantoms and real patient data. Symbol error rate (SER) was defined to evaluate the performance of clustering. Bias and variance of the reconstruction activity images were calculated based on ground truth. Relative mean square error (MSE) was used to evaluate parametric results quantitatively. RESULT In brain phantom experiment, when counting rate is 1 × 106 , the bias (variance) of our method is 0.1270 (0.0281), which is lower than maximum likelihood expectation maximization (MLEM) 0.1637 (0.0410) and direct estimation without segmentation (DE) 0.1511 (0.0326). In the Zubal phantom experiment, our method has the lowest bias (variance) 0.1559 (0.0354) with 1 × 105 counting rate, compared with DE 0.1820 (0.0435) and MLEM 0.3043 (0.0644). As for classification, the SER of our method is 18.87% which is the lowest among MLEM + k-means, DE + k-means, and kinetic spectral clustering (KSC). Brain data with MR reference and real patient results also show that the proposed method can get images with clear structure by visual inspection. CONCLUSION In this paper, we presented a joint reconstruction framework for simultaneously estimating the activity distribution, parametric images, and parameter-based segmentation of the ROIs into different functional areas. Total variation regularization is performed on the activity distribution domain to suppress noise and preserve the edges between ROIs. An over complete dictionary for time activity curve basis is constructed. SER, bias, variance, and MSE were calculated to show the effectiveness of the proposed method.
Collapse
Affiliation(s)
- Jianan Cui
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Haiqing Yu
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Shuhang Chen
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Yunmei Chen
- Department of Mathematics, University of Florida, 458 Little Hall, Gainesville, FL, 32611-8105, USA
| | - Huafeng Liu
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| |
Collapse
|
46
|
Leahy R, Boellaard R, Zaidi H. Whole‐body parametric
PET
imaging will replace conventional image‐derived
PET
metrics in clinical oncology. Med Phys 2018; 45:5355-5358. [DOI: 10.1002/mp.13266] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 10/22/2018] [Accepted: 10/23/2018] [Indexed: 01/26/2023] Open
Affiliation(s)
- Richard Leahy
- Signal and Image Processing Institute University of Southern California Los Angeles CA 90089USA
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine Amsterdam University Medical Centers Location VUMC AmsterdamThe Netherlands
- Department of Nuclear Medicine and Molecular Imaging University of Groningen University Medical Center Groningen Groningen The Netherlands
| | | |
Collapse
|
47
|
Colard E, Delcourt S, Padovani L, Thureau S, Dumouchel A, Gouel P, Lequesne J, Ara BF, Vera P, Taïeb D, Gardin I, Barbolosi D, Hapdey S. A new methodology to derive 3D kinetic parametric FDG PET images based on a mathematical approach integrating an error model of measurement. EJNMMI Res 2018; 8:99. [PMID: 30443801 PMCID: PMC6238015 DOI: 10.1186/s13550-018-0454-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 10/31/2018] [Indexed: 11/10/2022] Open
Abstract
Background In FDG-PET, SUV images are hampered by large potential biases. Our aim was to develop an alternative method (ParaPET) to generate 3D kinetic parametric FDG-PET images easy to perform in clinical oncology. Methods The key points of our method are the use of a new error model of PET measurement extracted from a late dynamic PET acquisition of 15 min, centered over the lesion and an image-derived input function (IDIF). The 15-min acquisition is reconstructed to obtain five images of FDG mean activity concentration and images of its variance to model errors of PET measurement. Our approach is carried out on each voxel to derive 3D kinetic parameter images. ParaPET was evaluated and compared to Patlak analysis as a reference. Hunter and Barbolosi methods (Barbolosi-Bl: with blood samples or Barbolosi-Im: with IDIF) were also investigated and compared to Patlak. Our evaluation was carried on Ki index, the net influx rate and its maximum value in the lesion (Ki,max). Results This parameter was obtained from 41 non-small cell lung cancer lesions associated with 4 to 5 blood samples per patient, required for the Patlak analysis. Compare to Patlak, the median relative difference and associated range (median; [min;max]) in Ki,max estimates were not statistically significant (Wilcoxon test) for ParaPET (− 3.0%; [− 31.9%; 47.3%]; p = 0.08) but statistically significant for Barbolosi-Bl (− 8.0%; [− 30.8%; 53.7%]; p = 0.001), Barbolosi-Im (− 7.9%; [− 38.4%; 30.6%]; p = 0.007) or Hunter (32.8%; [− 14.6%; 132.2%]; p < 10− 5). In the Bland-Altman plots, the ratios between the four methods and Patlak are not dependent of the Ki magnitude, except for Hunter. The 95% limits of agreement are comparable for ParaPET (34.7%), Barbolosi-Bl (30.1%) and Barbolosi-Im (30.8%), lower to Hunter (81.1%). In the 25 lesions imaged before and during the radio-chemotherapy, the decrease in the FDG uptake (ΔSUVmax or ΔKi,max) is statistically more important (p < 0.02, Wilcoxon one-tailed test) when estimated from the Ki images than from the SUV images (additional median variation of − 2.3% [− 52.6%; + 19.1%] for ΔKi,max compared to ΔSUVmax). Conclusion None of the four methodologies is yet ready to replace the Patlak approach, and further improvements are still required. Nevertheless, ParaPET remains a promising approach, offering a non-invasive alternative to methods based on multiple blood samples and only requiring a late PET acquisition. It allows deriving Ki values, highly correlated and presenting the lowest relative bias with Patlak estimates, in comparison to the other methods we evaluated. Moreover, ParaPET gives access to quantitative information at the pixel level, which needs to be evaluated in the perspective of radiomic and tumour response. Trial registration NCT 02821936; May 2016.
Collapse
Affiliation(s)
- Elyse Colard
- LITIS-QuantIF-EA4108, University of Rouen, Rouen, France
| | - Sarkis Delcourt
- Department of Nuclear Medicine, La Timone University Hospital, Marseille, France
| | - Laetitia Padovani
- Department of Radiotherapy, La Timone University Hospital, Marseille and SMARTc-INSERM-UMR 911 CR02, Aix-Marseille University, Marseille, France
| | - Sébastien Thureau
- Department of Radiotherapy, Centre Henri Becquerel, Rouen and LITIS-QuantIF-EA4108, University of Rouen, Rouen, France
| | - Arthur Dumouchel
- Department of Nuclear Medicine, Centre Henri Becquerel, Rouen, France
| | - Pierrick Gouel
- Department of Nuclear Medicine, Centre Henri Becquerel, Rouen, France
| | - Justine Lequesne
- Department of Clinical Research, Centre Henri Becquerel, Rouen, France
| | - Bardia Farman Ara
- Department of Nuclear Medicine, La Timone University Hospital, Marseille, France
| | - Pierre Vera
- Department of Radiotherapy, Centre Henri Becquerel, Rouen and LITIS-QuantIF-EA4108, University of Rouen, Rouen, France
| | - David Taïeb
- Department of Nuclear Medicine, La Timone University Hospital, Marseille and CERIMED, Aix-Marseille University, Marseille, France
| | - Isabelle Gardin
- Department of Nuclear Medicine, Centre Henri Becquerel, Rouen and LITIS-QuantIF-EA4108, University of Rouen, Rouen, France
| | - Dominique Barbolosi
- SMARTc-CRCM, INSERM UMR1068, CNRS UMR7258, Aix Marseille Université U105, Institut Paoli Calmette et APHM, Marseille, France
| | - Sébastien Hapdey
- Department of Nuclear Medicine, Centre Henri Becquerel, Rouen and LITIS-QuantIF-EA4108, University of Rouen, Rouen, France.
| |
Collapse
|
48
|
Arabi H, Zaidi H. Improvement of image quality in PET using post-reconstruction hybrid spatial-frequency domain filtering. Phys Med Biol 2018; 63:215010. [PMID: 30272565 DOI: 10.1088/1361-6560/aae573] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
PET images commonly suffer from the high noise level and poor signal-to-noise ratio (SNR), thus adversely impacting lesion detectability and quantitative accuracy. In this work, a novel hybrid dual-domain PET denoising approach is proposed, which combines the advantages of both spatial and transform domain filtering to preserve image textures while minimizing quantification uncertainty. Spatial domain denoising techniques excel at preserving high-contrast patterns compared to transform domain filters, which perform well in recovering low-contrast details normally smoothed out by spatial domain filters. For spatial domain filtering, the non-local mean algorithm was chosen owing to its performance in denoising high-contrast features whereas multi-scale curvelet denoising was exploited for the transform domain owing to its capability to recover small details. The proposed hybrid method was compared to conventional post-reconstruction Gaussian and edge preserving bilateral filters. Computer simulations of a thorax phantom containing three small lesions, experimental measurements using the Jaszczak phantom and clinical whole-body PET/CT studies were used to evaluate the performance of the proposed PET denoising technique. The proposed hybrid filter increased the SNR from 8.0 (non-filtered PET image) to 39.3 for small lesions in the computerized thorax phantom, while Gaussian and bilateral filtering led to SNRs of 23.3 and 24.4, respectively. For the experimental Jaszczak phantom, the contrast-to-noise ratio (CNR) improved from 10.84 when using Gaussian smoothing to 14.02 and 19.39 using the bilateral and the proposed hybrid filters, respectively. The clinical studies further demonstrated the superior performance of the hybrid method, yielding a quantification change (the original noisy OSEM image was used as reference in the absence of ground truth) in malignant lesions of -2.4% compared to -11.9% and -6.6% achieved using Gaussian and bilateral filters, respectively. In some cases, the visual difference between the bilateral and hybrid filtered images is not substantial; however the improved CNR score from 11.3 by OSEM to 17.1 and 21.8 by bilateral to the hybrid filtering, respectively, demonstrates the overall gain achieved by the hybrid approach. The proposed hybrid algorithm improved the contrast, SNR and quantitative accuracy compared to Gaussian and bilateral approaches, and can be utilized as an alternative post-reconstruction filter in clinical PET/CT imaging.
Collapse
Affiliation(s)
- Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland
| | | |
Collapse
|
49
|
Rahmim A, Lodge MA, Karakatsanis NA, Panin VY, Zhou Y, McMillan A, Cho S, Zaidi H, Casey ME, Wahl RL. Dynamic whole-body PET imaging: principles, potentials and applications. Eur J Nucl Med Mol Imaging 2018; 46:501-518. [PMID: 30269154 DOI: 10.1007/s00259-018-4153-6] [Citation(s) in RCA: 123] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Accepted: 08/28/2018] [Indexed: 02/07/2023]
Abstract
PURPOSE In this article, we discuss dynamic whole-body (DWB) positron emission tomography (PET) as an imaging tool with significant clinical potential, in relation to conventional standard uptake value (SUV) imaging. BACKGROUND DWB PET involves dynamic data acquisition over an extended axial range, capturing tracer kinetic information that is not available with conventional static acquisition protocols. The method can be performed within reasonable clinical imaging times, and enables generation of multiple types of PET images with complementary information in a single imaging session. Importantly, DWB PET can be used to produce multi-parametric images of (i) Patlak slope (influx rate) and (ii) intercept (referred to sometimes as "distribution volume"), while also providing (iii) a conventional 'SUV-equivalent' image for certain protocols. RESULTS We provide an overview of ongoing efforts (primarily focused on FDG PET) and discuss potential clinically relevant applications. CONCLUSION Overall, the framework of DWB imaging [applicable to both PET/CT(computed tomography) and PET/MRI (magnetic resonance imaging)] generates quantitative measures that may add significant value to conventional SUV image-derived measures, with limited pitfalls as we also discuss in this work.
Collapse
Affiliation(s)
- Arman Rahmim
- Department of Radiology and Radiological Science, Johns Hopkins University, JHOC Building Room 3245, 601 N. Caroline St, Baltimore, MD, 21287, USA. .,Departments of Radiology and Physics & Astronomy, University of British Columbia, Vancouver, BC, V5Z 1M9, Canada.
| | - Martin A Lodge
- Department of Radiology and Radiological Science, Johns Hopkins University, JHOC Building Room 3245, 601 N. Caroline St, Baltimore, MD, 21287, USA
| | | | | | - Yun Zhou
- Department of Radiology and Radiological Science, Johns Hopkins University, JHOC Building Room 3245, 601 N. Caroline St, Baltimore, MD, 21287, USA
| | - Alan McMillan
- Department of Radiology, University of Wisconsin, Madison, WI, 53705, USA
| | - Steve Cho
- Department of Radiology, University of Wisconsin, Madison, WI, 53705, USA
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | | | - Richard L Wahl
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| |
Collapse
|
50
|
Zaidi H, Alavi A, Naqa IE. Novel Quantitative PET Techniques for Clinical Decision Support in Oncology. Semin Nucl Med 2018; 48:548-564. [PMID: 30322481 DOI: 10.1053/j.semnuclmed.2018.07.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Quantitative image analysis has deep roots in the usage of positron emission tomography (PET) in clinical and research settings to address a wide variety of diseases. It has been extensively employed to assess molecular and physiological biomarkers in vivo in healthy and disease states, in oncology, cardiology, neurology, and psychiatry. Quantitative PET allows relating the time-varying activity concentration in tissues/organs of interest and the basic functional parameters governing the biological processes being studied. Yet, quantitative PET is challenged by a number of degrading physical factors related to the physics of PET imaging, the limitations of the instrumentation used, and the physiological status of the patient. Moreover, there is no consensus on the most reliable and robust image-derived PET metric(s) that can be used with confidence in clinical oncology owing to the discrepancies between the conclusions reported in the literature. There is also increasing interest in the use of artificial intelligence based techniques, particularly machine learning and deep learning techniques in a variety of applications to extract quantitative features (radiomics) from PET including image segmentation and outcome prediction in clinical oncology. These novel techniques are revolutionizing clinical practice and are now offering unique capabilities to the clinical molecular imaging community and biomedical researchers at large. In this report, we summarize recent developments and future tendencies in quantitative PET imaging and present example applications in clinical decision support to illustrate its potential in the context of clinical oncology.
Collapse
Affiliation(s)
- Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva Neuroscience Centre, University of Geneva, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, Groningen, the Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
| | - Abass Alavi
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| |
Collapse
|