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Alae Eddine EB, Scheiber C, Grenier T, Janier M, Flaus A. CT-guided spatial normalization of nuclear hybrid imaging adapted to enlarged ventricles: Impact on striatal uptake quantification. Neuroimage 2024; 294:120631. [PMID: 38701993 DOI: 10.1016/j.neuroimage.2024.120631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 04/25/2024] [Accepted: 04/30/2024] [Indexed: 05/06/2024] Open
Abstract
INTRODUCTION Spatial normalization is a prerequisite step for the quantitative analysis of SPECT or PET brain images using volume-of-interest (VOI) template or voxel-based analysis. MRI-guided spatial normalization is the gold standard, but the wide use of PET/CT or SPECT/CT in routine clinical practice makes CT-guided spatial normalization a necessary alternative. Ventricular enlargement is observed with aging, and it hampers the spatial normalization of the lateral ventricles and striatal regions, limiting their analysis. The aim of the present study was to propose a robust spatial normalization method based on CT scans that takes into account features of the aging brain to reduce bias in the CT-guided striatal analysis of SPECT images. METHODS We propose an enhanced CT-guided spatial normalization pipeline based on SPM12. Performance of the proposed pipeline was assessed on visually normal [123I]-FP-CIT SPECT/CT images. SPM12 default CT-guided spatial normalization was used as reference method. The metrics assessed were the overlap between the spatially normalized lateral ventricles and caudate/putamen VOIs, and the computation of caudate and putamen specific binding ratios (SBR). RESULTS In total 231 subjects (mean age ± SD = 61.9 ± 15.5 years) were included in the statistical analysis. The mean overlap between the spatially normalized lateral ventricles of subjects and the caudate VOI and the mean SBR of caudate were respectively 38.40 % (± SD = 19.48 %) of the VOI and 1.77 (± 0.79) when performing SPM12 default spatial normalization. The mean overlap decreased to 9.13 % (± SD = 1.41 %, P < 0.001) of the VOI and the SBR of caudate increased to 2.38 (± 0.51, P < 0.0001) when performing the proposed pipeline. Spatially normalized lateral ventricles did not overlap with putamen VOI using either method. The mean putamen SBR value derived from the proposed spatial normalization (2.75 ± 0.54) was not significantly different from that derived from the default SPM12 spatial normalization (2.83 ± 0.52, P > 0.05). CONCLUSION The automatic CT-guided spatial normalization used herein led to a less biased spatial normalization of SPECT images, hence an improved semi-quantitative analysis. The proposed pipeline could be implemented in clinical routine to perform a more robust SBR computation using hybrid imaging.
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Affiliation(s)
- El Barkaoui Alae Eddine
- Département de médecine nucléaire, Groupement Hospitalier Est, Hospices Civils de Lyon, Bron, France; INSA-Lyon, Universite Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69100, LYON, France
| | - Christian Scheiber
- Département de médecine nucléaire, Groupement Hospitalier Est, Hospices Civils de Lyon, Bron, France; Institut des Sciences Cognitives Marc Jeannerod, UMR 5229, CNRS, CRNL, Université Claude Bernard Lyon 1, Lyon, France
| | - Thomas Grenier
- INSA-Lyon, Universite Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69100, LYON, France
| | - Marc Janier
- Département de médecine nucléaire, Groupement Hospitalier Est, Hospices Civils de Lyon, Bron, France; Faculté de Médecine Lyon Est, Université Claude Bernard Lyon 1, Lyon, France; Laboratoire d'Automatique, de génie des procédés et de génie pharmaceutique, LAGEPP, UMR 5007 UCBL1 - CNRS, Lyon, France
| | - Anthime Flaus
- Département de médecine nucléaire, Groupement Hospitalier Est, Hospices Civils de Lyon, Bron, France; Faculté de Médecine Lyon Est, Université Claude Bernard Lyon 1, Lyon, France; Centre de Recherche en Neurosciences de Lyon, INSERM U1028/CNRS UMR5292, Lyon, France.
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Shang C, Sakurai K, Nihashi T, Arahata Y, Takeda A, Ishii K, Ishii K, Matsuda H, Ito K, Kato T, Toyama H, Nakamura A. Comparison of consistency in centiloid scale among different analytical methods in amyloid PET: the CapAIBL, VIZCalc, and Amyquant methods. Ann Nucl Med 2024; 38:460-467. [PMID: 38512444 PMCID: PMC11108942 DOI: 10.1007/s12149-024-01919-3] [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: 11/08/2023] [Accepted: 02/28/2024] [Indexed: 03/23/2024]
Abstract
OBJECTIVE The Centiloid (CL) scale is a standardized measure for quantifying amyloid deposition in amyloid positron emission tomography (PET) imaging. We aimed to assess the agreement among 3 CL calculation methods: CapAIBL, VIZCalc, and Amyquant. METHODS This study included 192 participants (mean age: 71.5 years, range: 50-87 years), comprising 55 with Alzheimer's disease, 65 with mild cognitive impairment, 13 with non-Alzheimer's dementia, and 59 cognitively normal participants. All the participants were assessed using the three CL calculation methods. Spearman's rank correlation, linear regression, Friedman tests, Wilcoxon signed-rank tests, and Bland-Altman analysis were employed to assess data correlations, linear associations, method differences, and systematic bias, respectively. RESULTS Strong correlations (rho = 0.99, p < .001) were observed among the CL values calculated using the three methods. Scatter plots and regression lines visually confirmed these strong correlations and met the validation criteria. Despite the robust correlations, a significant difference in CL value between CapAIBL and Amyquant was observed (36.1 ± 39.7 vs. 34.9 ± 39.4; p < .001). In contrast, no significant differences were found between CapAIBL and VIZCalc or between VIZCalc and Amyquant. The Bland-Altman analysis showed no observable systematic bias between the methods. CONCLUSIONS The study demonstrated strong agreement among the three methods for calculating CL values. Despite minor variations in the absolute values of the Centiloid scores obtained using these methods, the overall agreement suggests that they are interchangeable.
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Affiliation(s)
- Cong Shang
- Department of Radiology, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu, Aichi, 474-8511, Japan
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Keita Sakurai
- Department of Radiology, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu, Aichi, 474-8511, Japan
| | - Takashi Nihashi
- Department of Radiology, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu, Aichi, 474-8511, Japan
| | - Yutaka Arahata
- Department of Neurology, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Akinori Takeda
- Department of Neurology, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Kazunari Ishii
- Department of Radiology, Faculty of Medicine, Kindai University, Osakasayama, Japan
| | - Kenji Ishii
- Team for Neuroimaging Research, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Hiroshi Matsuda
- Department of Biofunctional Imaging, Fukushima Medical University, Fukushima, Japan
- Drug Discovery and Cyclotron Research Center, Southern Tohoku Research Institute for Neuroscience, Koriyama, Japan
| | - Kengo Ito
- Department of Radiology, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu, Aichi, 474-8511, Japan
- Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Takashi Kato
- Department of Radiology, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu, Aichi, 474-8511, Japan.
- Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, Obu, Japan.
| | - Hiroshi Toyama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Akinori Nakamura
- Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, Obu, Japan
- Department of Biomarker Research, National Center for Geriatrics and Gerontology, Obu, Japan
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Yamao T, Miwa K, Kaneko Y, Takahashi N, Miyaji N, Hasegawa K, Wagatsuma K, Kamitaka Y, Ito H, Matsuda H. Deep Learning-Driven Estimation of Centiloid Scales from Amyloid PET Images with 11C-PiB and 18F-Labeled Tracers in Alzheimer's Disease. Brain Sci 2024; 14:406. [PMID: 38672055 PMCID: PMC11048447 DOI: 10.3390/brainsci14040406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Standard methods for deriving Centiloid scales from amyloid PET images are time-consuming and require considerable expert knowledge. We aimed to develop a deep learning method of automating Centiloid scale calculations from amyloid PET images with 11C-Pittsburgh Compound-B (PiB) tracer and assess its applicability to 18F-labeled tracers without retraining. METHODS We trained models on 231 11C-PiB amyloid PET images using a 50-layer 3D ResNet architecture. The models predicted the Centiloid scale, and accuracy was assessed using mean absolute error (MAE), linear regression analysis, and Bland-Altman plots. RESULTS The MAEs for Alzheimer's disease (AD) and young controls (YC) were 8.54 and 2.61, respectively, using 11C-PiB, and 8.66 and 3.56, respectively, using 18F-NAV4694. The MAEs for AD and YC were higher with 18F-florbetaben (39.8 and 7.13, respectively) and 18F-florbetapir (40.5 and 12.4, respectively), and the error rate was moderate for 18F-flutemetamol (21.3 and 4.03, respectively). Linear regression yielded a slope of 1.00, intercept of 1.26, and R2 of 0.956, with a mean bias of -1.31 in the Centiloid scale prediction. CONCLUSIONS We propose a deep learning means of directly predicting the Centiloid scale from amyloid PET images in a native space. Transferring the model trained on 11C-PiB directly to 18F-NAV4694 without retraining was feasible.
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Affiliation(s)
- Tensho Yamao
- Department of Radiological Sciences, School of Health Science, Fukushima Medical University, Fukushima 960-8516, Japan; (T.Y.)
| | - Kenta Miwa
- Department of Radiological Sciences, School of Health Science, Fukushima Medical University, Fukushima 960-8516, Japan; (T.Y.)
| | - Yuta Kaneko
- Department of Radiology, Fukushima Medical University Hospital, Fukushima 960-1295, Japan
| | - Noriyuki Takahashi
- Department of Radiological Sciences, School of Health Science, Fukushima Medical University, Fukushima 960-8516, Japan; (T.Y.)
| | - Noriaki Miyaji
- Department of Radiological Sciences, School of Health Science, Fukushima Medical University, Fukushima 960-8516, Japan; (T.Y.)
| | - Koki Hasegawa
- Department of Radiological Sciences, School of Health Science, Fukushima Medical University, Fukushima 960-8516, Japan; (T.Y.)
| | - Kei Wagatsuma
- School of Allied Health Sciences, Kitasato University, Tokyo 252-0373, Japan
| | - Yuto Kamitaka
- Research Team for Neuroimaging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo 173-0015, Japan
| | - Hiroshi Ito
- Department of Radiology and Nuclear Medicine, Fukushima Medical University, Fukushima 960-1295, Japan
| | - Hiroshi Matsuda
- Department of Biofunctional Imaging, Fukushima Medical University, Fukushima 960-1295, Japan
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