Impact of Image Reconstruction Sets on Quantitative Analysis of [18F]-Fluorodeoxyglucose Brain Positron Emission TomographyIimages: Insights for Pre-Surgical Evaluation of Epilepsy Patients. A Preliminary Study
Abstract
Purpose: In epilepsy pre-surgical evaluations, semi-automated quantitative analysis of Fluorine-18-fluorodeoxyglucose (¹⁸F-FDG) brain PET complements visual assessment for localizing the seizure onset zone. This study evaluates how adjusting reconstruction parameters enhances quantitative accuracy, aiming to identify optimal configurations for reliable clinical decision-making.
Materials and Methods: 234 reconstruction methods were applied to 18F-FDG brain PET images of a 47-year-old male with focal epilepsy. The parameters encompassed the 3D-Ordered-Subset Expectation Maximization image reconstruction method, both with Resolution Recovery (RR) and without (non-RR), various numbers of iterations×subset (#it×sub), pixel sizes, and Gaussian filters. The accuracy errors were determined by the Relative Difference Percentage (RDP) in measured maximum standardized uptake value SUVmax and absolute Z-scores from all 234 reconstructed images, compared to reference values from the normal database reconstruction set as the benchmark
Results: The study revealed that reconstructed images with 5 mm or 8 mm full width at half maximum (FWHM) Gaussian filters yielded RDP values above 5% for SUVmax and Z-scores, indicating potential inaccuracy with higher values of post-smoothing filters. The recommended reconstruction sets with RDP values below 5% for both RR and non-RR images were those with a 3 mm FWHM Gaussian filter and higher (#it×sub), specifically (5×21, 8×21), (5×21, 6×21), and (7×21, 8×21) for pixel sizes of 1.01 mm, 1.35 mm, and 2.03 mm, respectively.
Conclusion: The findings underscore the significant impact of altering the image reconstruction sets on the SUVmax and Z-scores. Furthermore, the inconsistent fluctuations of Z-scores emphasize the importance of using standardized image reconstruction sets to ensure accurate and reliable quantitative outcomes in epilepsy pre-surgical evaluations.
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7- Jorge G Burneo, Raymond Poon, Sarah Kellett, and O Carter Snead, "The utility of positron emission tomography in epilepsy." Canadian Journal of Neurological Sciences, Vol. 42 (No. 6), pp. 360-71, (2015).
8- Jukka Peltola et al., "Semiautomated classification of nocturnal seizures using video recordings." Epilepsia, (2022).
9- George W Culler IV and Barbara C Jobst, "Surgical Treatments for epilepsy." CONTINUUM: Lifelong Learning in Neurology, Vol. 28 (No. 2), pp. 536-58, (2022).
10- Gregory D Cascino and Benjamin H Brinkmann, "Advances in the Surgical Management of Epilepsy: Drug-Resistant Focal Epilepsy in the Adult Patient." Neurologic clinics, Vol. 39 (No. 1), pp. 181-96, (2021).
11- Giridhar P Kalamangalam and Nitin Tandon, "Stereo-EEG implantation strategy." Journal of Clinical Neurophysiology, Vol. 33 (No. 6), pp. 483-89, (2016).
12- Yun Jeong Lee, "Advanced neuroimaging techniques for evaluating pediatric epilepsy." Clinical and experimental pediatrics, Vol. 63 (No. 3), p. 88, (2020).
13- Andrea Bernasconi et al., "Recommendations for the use of structural magnetic resonance imaging in the care of patients with epilepsy: a consensus report from the International League Against Epilepsy Neuroimaging Task Force." Epilepsia, Vol. 60 (No. 6), pp. 1054-68, (2019).
14- Jonah Peter, Sina Houshmand, Thomas J Werner, Domenico Rubello, and Abass Alavi, "Novel assessment of global metabolism by 18F-FDG-PET for localizing affected lobe in temporal lobe epilepsy." Nuclear medicine communications, Vol. 37 (No. 8), pp. 882-87, (2016).
15- Jonah Peter, Mohsen Khosravi, Thomas J Werner, and Abass Alavi, "Global temporal lobe asymmetry as a semi-quantitative imaging biomarker for temporal lobe epilepsy lateralization: A machine learning classification study." Hell. J. Nucl. Med, Vol. 21pp. 95-101, (2018).
16- Javier Aparicio et al., "Combined 18F-FDG-PET and diffusion tensor imaging in mesial temporal lobe epilepsy with hippocampal sclerosis." NeuroImage: Clinical, Vol. 12pp. 976-89, (2016).
17- Manohar Kuruva, Valeria M Moncayo, and Ryan B Peterson, "PET and SPECT imaging of epilepsy: technical considerations, pathologies, and pitfalls." in Seminars in Ultrasound, CT and MRI, (2020), Vol. 41 (No. 6): Elsevier, pp. 551-61.
18- Aldo Moro-AOU Policlinic, "Typical and atypical PET/CT findings in non-cancerous conditions." Hellenic Journal of Nuclear Medicine, (2020).
19- Maria Mayoral et al., "Epileptogenic zone localization with 18FDG PET using a new dynamic parametric analysis." Frontiers in Neurology, Vol. 10p. 380, (2019).
20- John S Duncan, Gavin P Winston, Matthias J Koepp, and Sebastien Ourselin, "Brain imaging in the assessment for epilepsy surgery." The Lancet Neurology, Vol. 15 (No. 4), pp. 420-33, (2016).
21- Jonah Peter, Sina Houshmand, Thomas J Werner, Domenico Rubello, and Abass Alavi, "Applications of global quantitative18F-FDG-PET analysis in temporal lobe epilepsy." Nuclear medicine communications, Vol. 37 (No. 3), pp. 223-30, (2016).
22- Dongyan Wu et al., "Characterizing the hyper‐and hypometabolism in temporal lobe epilepsy using multivariate machine learning." Journal of Neuroscience Research, (2021).
23- Vanessa Cristina Mendes Coelho et al., "Automated Online Quantification Method for 18F-FDG Positron Emission Tomography/CT Improves Detection of the Epileptogenic Zone in Patients with Pharmacoresistant Epilepsy." (in English), Frontiers in Neurology, Original Research Vol. 8 (No. 453), 2017-September-01 (2017).
24- P. Fu et al., "NeuroGam Software Analysis in Epilepsy Diagnosis Using 99mTc-ECD Brain Perfusion SPECT Imaging." Med Sci Monit, Vol. 21pp. 2801-8, (2015).
25- Evangeline Yee, Karteek Popuri, Mirza Faisal Beg, and Alzheimer's Disease Neuroimaging Initiative, "Quantifying brain metabolism from FDG‐PET images into a probability of Alzheimer's dementia score." Human brain mapping, Vol. 41 (No. 1), pp. 5-16, (2020).
26- Bianca De Blasi et al., "Age-specific 18F-FDG image processing pipelines and analysis are essential for individual mapping of seizure foci in pediatric patients with intractable epilepsy." Journal of Nuclear Medicine, Vol. 59 (No. 10), pp. 1590-96, (2018).
27- Li Sze Chow and Raveendran Paramesran, "Review of medical image quality assessment." Biomedical signal processing and control, Vol. 27pp. 145-54, (2016).
28- Jianhua Yan et al., "Impact of image reconstruction settings on texture features in 18F-FDG PET." Journal of Nuclear Medicine, Vol. 56 (No. 11), pp. 1667-73, (2015).
29- Nicolas Aide, Charline Lasnon, Patrick Veit-Haibach, Terez Sera, Bernhard Sattler, and Ronald Boellaard, "EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies." European journal of nuclear medicine and molecular imaging, Vol. 44pp. 17-31, (2017).
30- Andrea Cozzi et al., "Deep Learning-Based Versus Iterative Image Reconstruction for Unenhanced Brain CT: A Quantitative Comparison of Image Quality." Tomography, Vol. 9 (No. 5), pp. 1629-37, (2023).
31- Aziz Makandar and Bhagirathi Halalli, "Image enhancement techniques using highpass and lowpass filters." International Journal of Computer Applications, Vol. 109 (No. 14), pp. 12-15, (2015).
32- Sahar Rezaei et al., "The impact of iterative reconstruction protocol, signal-to-background ratio and background activity on measurement of PET spatial resolution." Japanese journal of radiology, Vol. 38pp. 231-39, (2020).
33- Alexander E Curtis, Tanya A Smith, Bulat A Ziganshin, and John A Elefteriades, "The mystery of the Z-score." aorta, Vol. 4 (No. 04), pp. 124-30, (2016).
34- Haleema Anwar, Qudsia Umaira Khan, Natasha Nadeem, Iqra Pervaiz, Muhammad Ali, and Fatima Fayyaz Cheema, "Epileptic seizures." Discoveries, Vol. 8 (No. 2), (2020).
35- Christoph Baumgartner, Johannes P Koren, Martha Britto-Arias, Lea Zoche, and Susanne Pirker, "Presurgical epilepsy evaluation and epilepsy surgery." F1000Research, Vol. 8(2019).
36- Nikoletta Pianou and Sofia Chatziioannou, "Imaging with PET/CT in Patients with Epilepsy." in Epilepsy Surgery and Intrinsic Brain Tumor Surgery: Springer, (2019), pp. 45-50.
37- A Niñerola-Baizán et al., "Relevance of quantification in brain PET studies with 18F-FDG." Revista Española de Medicina Nuclear e Imagen Molecular (English Edition), Vol. 39 (No. 3), pp. 184-92, (2020).
38- Benjamin D Wissel et al., "Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery." Epilepsia, Vol. 61 (No. 1), pp. 39-48, (2020).
39- Antonella Matti, Giacomo Maria Lima, Cinzia Pettinato, Francesca Pietrobon, Felice Martinelli, and Stefano Fanti, "How do the more recent reconstruction algorithms affect the interpretation criteria of PET/CT images?" Nuclear Medicine and Molecular Imaging, Vol. 53 (No. 3), pp. 216-22, (2019).
40- Tumelo Carel Godwin Moalosi, "The value of different reconstruction algorithms for quantification of FDG PET brain imaging." Stellenbosch: Stellenbosch University, (2016).
41- Charlotte S van der Vos et al., "Quantification, improvement, and harmonization of small lesion detection with state-of-the-art PET." European journal of nuclear medicine and molecular imaging, Vol. 44pp. 4-16, (2017).
42- Yu-Jung Tsai, "Penalised image reconstruction algorithms for efficient and consistent quantification in emission tomography." UCL (University College London), (2019).
43- Jie Zhao, Yunfeng Song, Qiong Liu, Shijie Chen, and Jyh-Cheng Chen, "Optimization of the Algorithm for the Implementation of Point Spread Function in the 3D-OSEM Reconstruction Algorithm Based on the List-Mode Micro PET Data." Electronics, Vol. 12 (No. 6), p. 1309, (2023).
44- Claudia Kuntner and David Stout, "Quantitative preclinical PET imaging: opportunities and challenges." (in English), Frontiers in Physics, Review Vol. 2 (No. 12), 2014-February-28 (2014).
2- William H Theodore, "Presurgical Focus Localization in Epilepsy: PET and SPECT." in Seminars in nuclear medicine, (2017), Vol. 47 (No. 1): Elsevier, pp. 44-53.
3- David J Thurman et al., "The burden of premature mortality of epilepsy in high‐income countries: a systematic review from the Mortality Task Force of the International League Against Epilepsy." Epilepsia, Vol. 58 (No. 1), pp. 17-26, (2017).
4- Wolfgang Löscher, Heidrun Potschka, Sanjay M Sisodiya, and Annamaria Vezzani, "Drug resistance in epilepsy: clinical impact, potential mechanisms, and new innovative treatment options." Pharmacological reviews, Vol. 72 (No. 3), pp. 606-38, (2020).
5- Johan Bjellvi, Ingrid Olsson, Kristina Malmgren, and Karin Wilbe Ramsay, "Epilepsy duration and seizure outcome in epilepsy surgery: A systematic review and meta-analysis." Neurology, Vol. 93 (No. 2), pp. e159-e66, (2019).
6- Stefan E Poirier et al., "An evaluation of the diagnostic equivalence of 18F-FDG-PET between hybrid PET/MRI and PET/CT in drug-resistant epilepsy: A pilot study." Epilepsy Research, Vol. 172p. 106583, (2021).
7- Jorge G Burneo, Raymond Poon, Sarah Kellett, and O Carter Snead, "The utility of positron emission tomography in epilepsy." Canadian Journal of Neurological Sciences, Vol. 42 (No. 6), pp. 360-71, (2015).
8- Jukka Peltola et al., "Semiautomated classification of nocturnal seizures using video recordings." Epilepsia, (2022).
9- George W Culler IV and Barbara C Jobst, "Surgical Treatments for epilepsy." CONTINUUM: Lifelong Learning in Neurology, Vol. 28 (No. 2), pp. 536-58, (2022).
10- Gregory D Cascino and Benjamin H Brinkmann, "Advances in the Surgical Management of Epilepsy: Drug-Resistant Focal Epilepsy in the Adult Patient." Neurologic clinics, Vol. 39 (No. 1), pp. 181-96, (2021).
11- Giridhar P Kalamangalam and Nitin Tandon, "Stereo-EEG implantation strategy." Journal of Clinical Neurophysiology, Vol. 33 (No. 6), pp. 483-89, (2016).
12- Yun Jeong Lee, "Advanced neuroimaging techniques for evaluating pediatric epilepsy." Clinical and experimental pediatrics, Vol. 63 (No. 3), p. 88, (2020).
13- Andrea Bernasconi et al., "Recommendations for the use of structural magnetic resonance imaging in the care of patients with epilepsy: a consensus report from the International League Against Epilepsy Neuroimaging Task Force." Epilepsia, Vol. 60 (No. 6), pp. 1054-68, (2019).
14- Jonah Peter, Sina Houshmand, Thomas J Werner, Domenico Rubello, and Abass Alavi, "Novel assessment of global metabolism by 18F-FDG-PET for localizing affected lobe in temporal lobe epilepsy." Nuclear medicine communications, Vol. 37 (No. 8), pp. 882-87, (2016).
15- Jonah Peter, Mohsen Khosravi, Thomas J Werner, and Abass Alavi, "Global temporal lobe asymmetry as a semi-quantitative imaging biomarker for temporal lobe epilepsy lateralization: A machine learning classification study." Hell. J. Nucl. Med, Vol. 21pp. 95-101, (2018).
16- Javier Aparicio et al., "Combined 18F-FDG-PET and diffusion tensor imaging in mesial temporal lobe epilepsy with hippocampal sclerosis." NeuroImage: Clinical, Vol. 12pp. 976-89, (2016).
17- Manohar Kuruva, Valeria M Moncayo, and Ryan B Peterson, "PET and SPECT imaging of epilepsy: technical considerations, pathologies, and pitfalls." in Seminars in Ultrasound, CT and MRI, (2020), Vol. 41 (No. 6): Elsevier, pp. 551-61.
18- Aldo Moro-AOU Policlinic, "Typical and atypical PET/CT findings in non-cancerous conditions." Hellenic Journal of Nuclear Medicine, (2020).
19- Maria Mayoral et al., "Epileptogenic zone localization with 18FDG PET using a new dynamic parametric analysis." Frontiers in Neurology, Vol. 10p. 380, (2019).
20- John S Duncan, Gavin P Winston, Matthias J Koepp, and Sebastien Ourselin, "Brain imaging in the assessment for epilepsy surgery." The Lancet Neurology, Vol. 15 (No. 4), pp. 420-33, (2016).
21- Jonah Peter, Sina Houshmand, Thomas J Werner, Domenico Rubello, and Abass Alavi, "Applications of global quantitative18F-FDG-PET analysis in temporal lobe epilepsy." Nuclear medicine communications, Vol. 37 (No. 3), pp. 223-30, (2016).
22- Dongyan Wu et al., "Characterizing the hyper‐and hypometabolism in temporal lobe epilepsy using multivariate machine learning." Journal of Neuroscience Research, (2021).
23- Vanessa Cristina Mendes Coelho et al., "Automated Online Quantification Method for 18F-FDG Positron Emission Tomography/CT Improves Detection of the Epileptogenic Zone in Patients with Pharmacoresistant Epilepsy." (in English), Frontiers in Neurology, Original Research Vol. 8 (No. 453), 2017-September-01 (2017).
24- P. Fu et al., "NeuroGam Software Analysis in Epilepsy Diagnosis Using 99mTc-ECD Brain Perfusion SPECT Imaging." Med Sci Monit, Vol. 21pp. 2801-8, (2015).
25- Evangeline Yee, Karteek Popuri, Mirza Faisal Beg, and Alzheimer's Disease Neuroimaging Initiative, "Quantifying brain metabolism from FDG‐PET images into a probability of Alzheimer's dementia score." Human brain mapping, Vol. 41 (No. 1), pp. 5-16, (2020).
26- Bianca De Blasi et al., "Age-specific 18F-FDG image processing pipelines and analysis are essential for individual mapping of seizure foci in pediatric patients with intractable epilepsy." Journal of Nuclear Medicine, Vol. 59 (No. 10), pp. 1590-96, (2018).
27- Li Sze Chow and Raveendran Paramesran, "Review of medical image quality assessment." Biomedical signal processing and control, Vol. 27pp. 145-54, (2016).
28- Jianhua Yan et al., "Impact of image reconstruction settings on texture features in 18F-FDG PET." Journal of Nuclear Medicine, Vol. 56 (No. 11), pp. 1667-73, (2015).
29- Nicolas Aide, Charline Lasnon, Patrick Veit-Haibach, Terez Sera, Bernhard Sattler, and Ronald Boellaard, "EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies." European journal of nuclear medicine and molecular imaging, Vol. 44pp. 17-31, (2017).
30- Andrea Cozzi et al., "Deep Learning-Based Versus Iterative Image Reconstruction for Unenhanced Brain CT: A Quantitative Comparison of Image Quality." Tomography, Vol. 9 (No. 5), pp. 1629-37, (2023).
31- Aziz Makandar and Bhagirathi Halalli, "Image enhancement techniques using highpass and lowpass filters." International Journal of Computer Applications, Vol. 109 (No. 14), pp. 12-15, (2015).
32- Sahar Rezaei et al., "The impact of iterative reconstruction protocol, signal-to-background ratio and background activity on measurement of PET spatial resolution." Japanese journal of radiology, Vol. 38pp. 231-39, (2020).
33- Alexander E Curtis, Tanya A Smith, Bulat A Ziganshin, and John A Elefteriades, "The mystery of the Z-score." aorta, Vol. 4 (No. 04), pp. 124-30, (2016).
34- Haleema Anwar, Qudsia Umaira Khan, Natasha Nadeem, Iqra Pervaiz, Muhammad Ali, and Fatima Fayyaz Cheema, "Epileptic seizures." Discoveries, Vol. 8 (No. 2), (2020).
35- Christoph Baumgartner, Johannes P Koren, Martha Britto-Arias, Lea Zoche, and Susanne Pirker, "Presurgical epilepsy evaluation and epilepsy surgery." F1000Research, Vol. 8(2019).
36- Nikoletta Pianou and Sofia Chatziioannou, "Imaging with PET/CT in Patients with Epilepsy." in Epilepsy Surgery and Intrinsic Brain Tumor Surgery: Springer, (2019), pp. 45-50.
37- A Niñerola-Baizán et al., "Relevance of quantification in brain PET studies with 18F-FDG." Revista Española de Medicina Nuclear e Imagen Molecular (English Edition), Vol. 39 (No. 3), pp. 184-92, (2020).
38- Benjamin D Wissel et al., "Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery." Epilepsia, Vol. 61 (No. 1), pp. 39-48, (2020).
39- Antonella Matti, Giacomo Maria Lima, Cinzia Pettinato, Francesca Pietrobon, Felice Martinelli, and Stefano Fanti, "How do the more recent reconstruction algorithms affect the interpretation criteria of PET/CT images?" Nuclear Medicine and Molecular Imaging, Vol. 53 (No. 3), pp. 216-22, (2019).
40- Tumelo Carel Godwin Moalosi, "The value of different reconstruction algorithms for quantification of FDG PET brain imaging." Stellenbosch: Stellenbosch University, (2016).
41- Charlotte S van der Vos et al., "Quantification, improvement, and harmonization of small lesion detection with state-of-the-art PET." European journal of nuclear medicine and molecular imaging, Vol. 44pp. 4-16, (2017).
42- Yu-Jung Tsai, "Penalised image reconstruction algorithms for efficient and consistent quantification in emission tomography." UCL (University College London), (2019).
43- Jie Zhao, Yunfeng Song, Qiong Liu, Shijie Chen, and Jyh-Cheng Chen, "Optimization of the Algorithm for the Implementation of Point Spread Function in the 3D-OSEM Reconstruction Algorithm Based on the List-Mode Micro PET Data." Electronics, Vol. 12 (No. 6), p. 1309, (2023).
44- Claudia Kuntner and David Stout, "Quantitative preclinical PET imaging: opportunities and challenges." (in English), Frontiers in Physics, Review Vol. 2 (No. 12), 2014-February-28 (2014).
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| Issue | Vol 13 No 2 (2026) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v13i2.21944 | |
| Keywords | ||
| Positron Emission Tomography Fluorine-18-fluorodeoxyglucose Drug-Resistant Epilepsy Image Reconstruction. | ||
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How to Cite
1.
Firouzi N, Parach AA, Tanha K, Rostami M, Geramifar P. Impact of Image Reconstruction Sets on Quantitative Analysis of [18F]-Fluorodeoxyglucose Brain Positron Emission TomographyIimages: Insights for Pre-Surgical Evaluation of Epilepsy Patients. A Preliminary Study. Frontiers Biomed Technol. 2026;13(2):458-471.
