Analysis of Functional Brain Images Using Population-Based Probabilistic Atlas

Lee, Jae Sung Lee and Dong Soo

doi:10.2174/1573405052953056

Cited by 36 publications

(22 citation statements)

References 36 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All the PET images were spatially normalized into an in-house 18 F-FP-CIT PET template (5,22). PET counts in the putamen, caudate nucleus, and cerebellum were calculated using statistical probabilistic anatomic mapping (23).…”

Section: Pet Image Analysismentioning

confidence: 99%

Segmentation-Based MR Attenuation Correction Including Bones Also Affects Quantitation in Brain Studies: An Initial Result of ¹⁸F-FP-CIT PET/MR for Patients with Parkinsonism

et al. 2014

View full text Add to dashboard Cite

Attenuation correction (AC) with an ultrashort echo time (UTE) sequence has recently been used in combination with segmentation for cortical bone identification for brain PET/MR studies. The purpose of this study was to evaluate the quantification of 18 F-fluoropropylcarbomethoxyiodophenylnortropane ( 18 F-FP-CIT) binding in brain PET/MR, particularly focusing on effects of UTE-based AC including bone segmentation. Methods: Sixteen patients with initially suspected parkinsonism were prospectively enrolled. An emission scan was acquired 110 min after 18 F-FP-CIT injection on a dedicated PET/MR scanner, immediately followed by another emission scan using a PET/CT scanner 120 min after the injection. A UTE-based attenuation map was used to classify the voxels into 3 tissues: bone, soft tissue, and air. All PET images were spatially normalized, and a specific-to-nonspecific dopamine transporter (DAT) binding ratio (BR) was calculated using statistical probabilistic anatomic mapping. The level of agreement was assessed with intraclass correlation coefficients (ICCs). Voxelwise comparison between PET images acquired from PET/MR and PET/CT was performed. We compared non-attenuation-corrected images to analyze UTE-based AC effects on DAT quantification. Results: BR in the putamen obtained from PET/MR and PET/CT showed low interequipment variability, whereas BR in the caudate nucleus showed significant variability (ICC 5 0.967 and 0.682 for putamen and caudate nucleus, respectively). BR in the caudate nucleus was significantly underestimated by PET/MR, compared with PET/CT (mean difference of BR 5 0.66, P , 0.0001). Voxelwise analysis revealed that PET/MR showed significantly low BR in the periventricular regions, which was caused by a misclassification of the ventricle as air on the attenuation map. We also compared non-AC images, revealing low interequipment variability even in the caudate nucleus (ICC 5 0.937 and 0.832 for putamen and caudate nucleus, respectively). Conclusion: Our data demonstrate spatial bias of the DAT BR on 18 F-FP-CIT PET/MR. Voxelwise analysis and comparison to non-AC images identified the misclassification of ventricle as air to be the cause of bias. To obtain reliable quantification for brain PET/MR studies including 18 F-FP-CIT PET, alternative and more reliable segmentation strategies are required.

show abstract

Section: Pet Image Analysismentioning

confidence: 99%

Segmentation-Based MR Attenuation Correction Including Bones Also Affects Quantitation in Brain Studies: An Initial Result of ¹⁸F-FP-CIT PET/MR for Patients with Parkinsonism

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Once spatial normalization of an individual image is successful, image parameters of target VOIs, predefined structures on a standard template can simply be measured automatically. Thus, SPAM can provide objective regional analysis of functional brain images, with ease and near-complete reproducibility [20].…”

Section: Discussionmentioning

confidence: 99%

Validation of Simple Quantification Methods for 18F-FP-CIT PET Using Automatic Delineation of Volumes of Interest Based on Statistical Probabilistic Anatomical Mapping and Isocontour Margin Setting

Kim

Paeng

et al. 2012

Nucl Med Mol Imaging

View full text Add to dashboard Cite

Purpose 18 F-FP-CIT positron emission tomography (PET) is an effective imaging for dopamine transporters. In usual clinical practice, 18 F-FP-CIT PET is analyzed visually or quantified using manual delineation of a volume of interest (VOI) for the striatum. In this study, we suggested and validated two simple quantitative methods based on automatic VOI delineation using statistical probabilistic anatomical mapping (SPAM) and isocontour margin setting. Methods Seventy-five 18 F-FP-CIT PET images acquired in routine clinical practice were used for this study. A studyspecific image template was made and the subject images were normalized to the template. Afterwards, uptakes in the striatal regions and cerebellum were quantified using probabilistic VOI based on SPAM. A quantitative parameter, Q SPAM , was calculated to simulate binding potential. Additionally, the functional volume of each striatal region and its uptake were measured in automatically delineated VOI using isocontour margin setting. Uptake-volume product (Q UVP ) was calculated for each striatal region. Q SPAM and Q UVP were compared with visual grading and the influence of cerebral atrophy on the measurements was tested. Results Image analyses were successful in all the cases. Both the Q SPAM and Q UVP were significantly different according to visual grading (P<0.001). The agreements of Q UVP or Q SPAM with visual grading were slight to fair for the caudate nucleus (κ00.421 and 0.291, respectively) and good to perfect to the putamen (κ00.663 and 0.607, respectively). Also, Q SPAM and Q UVP had a significant correlation with each other (P<0.001). Cerebral atrophy made a significant difference in Q SPAM and Q UVP of the caudate nuclei regions with decreased 18 F-FP-CIT uptake. Conclusion Simple quantitative measurements of Q SPAM and Q UVP showed acceptable agreement with visual grading. Although Q SPAM in some group may be influenced by cerebral atrophy, these simple methods are expected to be effective in the quantitative analysis of 18 F-FP-CIT PET in usual clinical practice.

show abstract

“…More recently this SPM approach to metabolic connectivity has been refined by Lee et al [21], who systematically explored metabolic connectivity based on voxel-wise interregional correlation analysis of SPM in normal healthy adults, thus establishing normative data of interregional metabolic connectivity. In this study, PET images were spatially normalized to the Korean standard PET template of young male adult subjects; then, FDG mean counts were extracted using a structural probabilistic map for 70 brain volumetric regions (volumes of interest, VOI) [34,35] and 28 cytoarchitectonically defined VOI [36]. Probability-weighted mean counts for each seed VOI were globally normalized with respect to individual gray matter mean counts.…”

Section: Resting Metabolic Connectivity: Evolution Of the Methodsmentioning

confidence: 99%

Resting metabolic connectivity in Alzheimer’s disease

Morbelli

Arnaldi

Capitanio

et al. 2013

Clin Transl Imaging

View full text Add to dashboard Cite

Metabolic connectivity analysis of resting 18 F-FDG PET is based on the assumption that brain regions whose metabolism is significantly correlated at rest are functionally associated and that the strength of the association is proportional to the magnitude of the correlation coefficient. Therefore, this method could be used to evaluate connectivity networks independently on the basis of performance in specific tasks. Published studies have provided evidence that metabolic connectivity substantially overlaps underlying anatomical pathways and depends on the location of the analyzed regions, but is not influenced by their size. The present review focuses on the methods and meaning of resting inter-regional correlation analysis of cerebral metabolic rate of glucose consumption in Alzheimer's disease. Accordingly, we describe the evolution of analytical tools from the correlation with a single region of interest to a voxel-based statistical parametric mappingbased approach. We also discuss the pathophysiological implications of metabolic connectivity studies both for Alzheimer-related disconnection syndrome and for default-mode network impairment and compensation mechanisms.

show abstract

Analysis of Functional Brain Images Using Population-Based Probabilistic Atlas

Cited by 36 publications

References 36 publications

Segmentation-Based MR Attenuation Correction Including Bones Also Affects Quantitation in Brain Studies: An Initial Result of ¹⁸F-FP-CIT PET/MR for Patients with Parkinsonism

Segmentation-Based MR Attenuation Correction Including Bones Also Affects Quantitation in Brain Studies: An Initial Result of ¹⁸F-FP-CIT PET/MR for Patients with Parkinsonism

Validation of Simple Quantification Methods for 18F-FP-CIT PET Using Automatic Delineation of Volumes of Interest Based on Statistical Probabilistic Anatomical Mapping and Isocontour Margin Setting

Resting metabolic connectivity in Alzheimer’s disease

Contact Info

Product

Resources

About

Analysis of Functional Brain Images Using Population-Based Probabilistic Atlas

Cited by 36 publications

References 36 publications

Segmentation-Based MR Attenuation Correction Including Bones Also Affects Quantitation in Brain Studies: An Initial Result of 18F-FP-CIT PET/MR for Patients with Parkinsonism

Segmentation-Based MR Attenuation Correction Including Bones Also Affects Quantitation in Brain Studies: An Initial Result of 18F-FP-CIT PET/MR for Patients with Parkinsonism

Validation of Simple Quantification Methods for 18F-FP-CIT PET Using Automatic Delineation of Volumes of Interest Based on Statistical Probabilistic Anatomical Mapping and Isocontour Margin Setting

Resting metabolic connectivity in Alzheimer’s disease

Contact Info

Product

Resources

About

Segmentation-Based MR Attenuation Correction Including Bones Also Affects Quantitation in Brain Studies: An Initial Result of ¹⁸F-FP-CIT PET/MR for Patients with Parkinsonism

Segmentation-Based MR Attenuation Correction Including Bones Also Affects Quantitation in Brain Studies: An Initial Result of ¹⁸F-FP-CIT PET/MR for Patients with Parkinsonism