PET新型Tau蛋白分子探针18F-S16用于阿尔茨海默病患者

目的 观察PET新型Tau蛋白分子探针¹⁸F-S16用于阿尔茨海默病(AD)的价值。方法 前瞻性纳入6例AD患者、2例非AD痴呆患者,以及6名老年和2名年轻健康志愿者,行头部¹⁸F-S16 PET/MR扫描;计算注射药物后40~60 min及70~90 min皮质/小脑标准摄取值比值(SUVR),并观察其与Logan图解分析所得分布体积比(DVR)的相关性,基于ROI和体素分析比较摄取差异。结果 AD患者皮质放射性摄取明显增加,而老年及年轻健康志愿者未见皮质显著放射性浓聚;三者均可见基底节区非特异性摄取。¹⁸F-S16注射后90 min内,AD患者SUVR进行性增加,并随时间延长而与老年和年轻健康志愿者差距增大。与老年健康志愿者相比,AD患者双侧枕叶皮质、舌回及后扣带回皮质/楔前叶¹⁸F-S16放射性滞留明显增加。注射药物后40~60 min及70~90 min,5名老年健康志愿者枕叶SUVR与DVR的相关系数R²分别为0.826 2和0.909 1,其在内侧颞叶皮质为0.989 7和0.983 6,在额叶为0.749 6和0.752 6,顶叶为0.930 6和0.921 7,外侧颞叶为0.934 2和0.936 3,在后扣带回为0.749 6和0.752 6。结论 作为PET Tau蛋白分子探针,¹⁸F-S16在AD患者脑内皮质区存在显著放射性滞留,具有一定临床应用潜力。     

FDDNP binding using MR derived cortical surface maps

ObjectivesTo assess quantitatively the cortical pattern profile of regional FDDNP binding to beta-amyloid and neurofibrillary tangles on MR derived cortical maps, FDDNP PET images were corrected for movement and partial volume (PV), and optimized for kernel size.MethodsFDDNP DVR PET images from 23 subjects (7 with Alzheimer's disease (AD), 6 with mild cognitive impairment and 10 controls) were obtained from Logan analysis using cerebellum as reference. A hemispheric cortical surface model for each subject was extracted from the MRI. The same transformations were applied to the FDDNP DVR PET images to map them into the same space. The cortical map with PV correction was calculated as the ratio of the DVR cortical surface and that of the simulated map, created from the mask derived from MRI and smoothed to the PET resolution. Discriminant analysis was used to order the FDDNP DVR cortical surfaces based on subjects' disease state. Linear regression was used to assess the rate of change of DVR vs. MMSE for each hemispheric cortical surface point.ResultsThe FDDNP DVR cortical surface corrected for movement and PV had less hemispheric asymmetry. Optimal kernel size was determined to be 9 mm. The corrected cortical surface map of FDDNP DVR showed clear spatial pattern that was consistent with the known pathological progression of AD.ConclusionCorrecting for movement, PV as well as optimizing kernel size provide sensitive statistical analysis of FDDNP distribution which confirms in the living brain known pathology patterns earlier observed with cognitive decline with brain specimens.     

Voxel-Based Analysis of Amyloid-Burden Measured with [11C]PiB PET in a Double Transgenic Mouse Model of Alzheimer’s Disease

Purpose

The purpose of this study is to validate the feasibility of a voxel-based analysis of in vivo amyloid-β positron emission tomography (PET) imaging studies in transgenic mouse models of Alzheimer’s disease.

Procedures

We performed [¹¹C]PiB PET imaging in 20 APP/PS1 mice and 16 age-matched controls, and histologically determined the individual amyloid-β plaque load. Using SPM software, we performed a voxel-based group comparison plus a regression analysis between PiB retention and actual plaque load, both thresholded at p _FWE?<?0.05. In addition, we carried out an individual ROI analysis in every animal.

Results

The automated voxel-based group comparison allowed us to identify voxels with significantly increased PiB retention in the cortical and hippocampal regions in transgenic animals compared to controls. The voxel-based regression analysis revealed a significant association between this signal increase and the actual cerebral plaque load. The validity of these results was corroborated by the individual ROI-based analysis.

Conclusions

Voxel-based analysis of in vivo amyloid-β PET imaging studies in mouse models of Alzheimer’s disease is feasible and allows studying the PiB retention patterns in whole brain maps. Furthermore, the selected approach in our study also allowed us to establish a quantitative relation between tracer retention and actual plaque pathology in the brain in a voxel-wise manner.     

Combined motion blur and partial volume correction for computer aided diagnosis of pulmonary nodules in PET/CT

Objective

We present an automated scheme to correct PET max-uptake-values of small to medium-sized pulmonary nodules for motion blur and partial volume averaging. Both effects cause significant underestimation of PET max-uptake-values, particularly in nodules below 25 mm diameter, but nodules up to 75 mm might be affected. This compromises the power of PET for the differential diagnosis of such nodules, in particular benign versus malignant. Thus, correcting PET max-uptake-values has the potential to improve the classification of PET-positive pulmonary nodules.

Methods

The proposed correction algorithm relies on (i) determination of the actual size and shape of the nodule by segmentation of the nodule in the CT image and (ii) estimation of the effective local point-spread-function in the PET image, taking into account not only the inherently limited spatial resolution of the PET scanner, but also respiratory motion effects. Then the expected under-estimation of the PET max-uptake value in the nodule can be computed by simulation, and the correct PET max-uptake is obtained by multiplication with the correction factor (inverse of the under-estimation/recovery factor).

Results

Depending on the estimated nodule shape and blur width, the resulting SUV correction factors ranged from 1.0 to 11, with an average correction factor of 3.0, with higher values for smaller nodules. In comparison to SUV correction using a simplified spherical nodule model, the true-shape SUV correction factors were on average 30% higher. The feasibility of the method presented here is indicated by the high correlation between fitted and observed PET image profiles for clinical cases (average 0.995).

Conclusion

Blur and motion correction factors for standardized PET uptake values may significantly change the differential diagnosis of small pulmonary nodules. Feasibility and stability of the proposed automated combined SUV correction method as well as ease of use of the software tool have been demonstrated by retrospective analysis of real PET/CT patient datasets from clinical routine.     

An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest

In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.     

The impact of physiological noise correction on fMRI at 7 T

Sub-striatal regions of interest (ROIs) are widely used in PET studies to investigate the role of dopamine in the modulation of neural networks implicated in emotion, cognition and motor function. One common approach is that of Mawlawi et al. (2001) and Martinez et al. (2003), where each striatum is divided into five sub-regions. This study focuses on the use of two spatial normalization-based alternatives to manual sub-striatal ROI delineation per subject: manual ROI delineation on a template brain and the production of probabilistic ROIs from a set of subject-specific manually delineated ROIs. Two spatial normalization algorithms were compared: SPM5 unified segmentation and ART. The ability of these methods to quantify sub-striatal regional non-displaceable binding potential (BP(ND)) and BP(ND) % change (following methylphenidate) was tested on 32 subjects (16 controls and 16 ADHD patients) scanned with the dopamine D(2)/D(3) ligand [(18)F]fallypride. Probabilistic ROIs produced by ART provided the best results, with similarity index values against subject-specific manual ROIs of 0.75-0.89 (mean 0.84) compared to 0.70-0.85 (mean 0.79) for template ROIs. Correlations (r) for BP(ND) and BP(ND) % change between subject-specific manual ROIs and these probabilistic ROIs of 0.90-0.98 (mean 0.95) and 0.98-1.00 (mean 0.99) respectively were superior overall to those obtained with template ROIs, although only marginally so for BP(ND) % change. The significance of relationships between BP(ND) measures and both behavioural tasks and methylphenidate plasma levels was preserved with ART combined with both probabilistic and template ROIs. SPM5 virtually matched the performance of ART for BP(ND) % change estimation but was inferior for BP(ND) estimation in caudate sub-regions. ART spatial normalization combined with probabilistic ROIs and to a lesser extent template ROIs provides an efficient and accurate alternative to time-consuming manual sub-striatal ROI delineation per subject, especially when the parameter of interest is BP(ND) % change.     

Appearance modeling of 11C PiB PET images: characterizing amyloid deposition in Alzheimer's disease, mild cognitive impairment and healthy aging

Beta-amyloid (Abeta) deposition is one of the neuropathological hallmarks of Alzheimer's disease (AD), Abeta burden can be quantified using (11)C PiB PET. Neuropathological studies have shown that the initial plaques are located in the temporal and orbitofrontal cortices, extending later to the cingulate, frontal and parietal cortices (Braak and Braak, 1997). Previous studies have shown an overlap in (11)C PiB PET retention between AD, mild cognitive impairment (MCI) patients and normal elderly control (NC) participants. It has also been shown that there is a relationship between Abeta deposition and memory impairment in MCI patients. In this paper we explored the variability seen in 15 AD, 15 MCI and 18 NC by modeling the voxel data from spatially and uptake normalized PiB images using principal component analysis. The first two principal components accounted for 80% of the variability seen in the data, providing a clear separation between AD and NC, and allowing subsequent classification. The MCI cases were distributed along an apparent axis between the AD and NC group, closely aligned with the first principal component axis. The NC cases that were PiB(+) formed a distinct cluster that was between, but separated from the AD and PiB(-) NC clusters. The PiB(+) MCI were found to cluster with the AD cases, and exhibited a similar deposition pattern. The primary principal component score was found to correlate with episodic memory scores and mini mental status examination and it was observed that by varying the first principal component, a change in amyloid deposition could be derived that is similar to the expected progression of amyloid deposition observed from post mortem studies.     

Measurement of GABAA receptor binding in vivo with [11C]flumazenil: a test-retest study in healthy subjects

[(11)C]Flumazenil is widely used in positron emission tomography (PET) studies to measure GABA(A) receptors in vivo in humans. Although several different methods have been applied for the quantification of [(11)C]flumazenil binding, the reproducibility of these methods has not been previously examined. The reproducibility of a single bolus [(11)C]flumazenil measurements was studied by scanning eight healthy volunteers twice during the same day. Grey matter regions were analyzed using both regions-of-interest (ROI) and voxel-based analysis methods. Compartmental kinetic modelling using both arterial and reference region input function were applied to derive the total tissue distribution volume (V(T)) and the binding potential (BP) (BP(P) and BP(ND)) of [(11)C]flumazenil. To measure the reproducibility and reliability of each [(11)C]flumazenil binding parameter, absolute variability values (VAR) and intraclass correlation coefficients (ICC) were calculated. Tissue radioactivity concentration over time was best modelled with a 2-tissue compartmental model. V(T) showed with all methods good to excellent reproducibility and reliability with low VARs (mean of all brain regions) (5.57%-6.26%) and high ICCs (mean of all brain regions) (0.83-0.88) when using conventional ROI analysis. Also voxel-based analysis methods yielded excellent reproducibility (VAR 5.75% and ICC 0.81). In contrast, the BP estimates using pons as the reference tissue yielded higher VARs (8.08%-9.08%) and lower ICCs (0.35-0.80). In conclusion, the reproducibility of [(11)C]flumazenil measurements is considerably better with outcome measures based on arterial input function than those using pons as the reference tissue. The voxel-based analysis methods are proper alternative as the reliability is preserved and analysis automated.     

不同密度口服阳性对比剂对PET/CT图像质量及标准摄取值的影响

目的探讨不同密度口服阳性对比剂对PET/CT图像质量和标准摄取值(SUV)的影响。方法60例PET/CT显像患者,显像前常规口服浓度为2%的泛影葡胺1000ml,其中14例患者肠道内发现多处高密度残留钡剂。所有患者均同时采用CT及137Cs两种衰减校正方法,分别重建CT衰减校正(CTAC)、137Cs衰减校正(CsAC)图像。选择所有患者胃肠道内泛影葡胺充盈区、无对比剂充盈区、臀部软组织区以及14例患者高密度钡剂充盈区勾划感兴趣区(ROI)。比较各感兴趣区CTAC和CsAC的平均SUV差异,以及CTAC、CsAC的图像差异。结果在所有正常软组织区、无对比剂充盈区以及泛影葡胺充盈区,CTAC和CsAC的平均SUV无显著性差异。在高密度残留钡剂充盈区,CTAC的平均SUV明显高于CsAC(t=3.451,P=0.018),其差异与CT值成正相关(r=0.445,P=0.021)。比较所有图像,正常软组织、无对比剂充盈区、泛影葡胺充盈区CTAC和CsAC图像质量无明显差异。在高密度残留钡剂充盈区,CTAC图像上可见FDG高摄取伪影,而CsAC图像为正常摄取。结论低密度口服泛影葡胺对PET/CT显像图像质量和标准摄取值无明显影响,高密度钡剂可使PET/CT的SUV明显高估,且可出现不同程度的FDG高摄取伪影。     

An In Vivo Evaluation of Cerebral Cortical Amyloid with [18F]Flutemetamol Using Positron Emission Tomography Compared with Parietal Biopsy Samples in Living Normal Pressure Hydrocephalus Patients

Purpose

The primary objectives of this study were to assess the safety of [¹⁸F]flutemetamol injection and determine the level of association between the quantitative estimates of brain uptake of [¹⁸F]flutemetamol and the quantitative immunohistochemical (IHC) estimates of amyloid levels in cerebral cortex biopsies obtained during shunt placement in patients with normal pressure hydrocephalus (NPH).

Procedures

Parietal lobe biopsies were obtained from 12 subjects (mean (SD), 71 (8.1) years), during shunt placement for NPH. Shunt procedures and biopsies were performed within 8 weeks after the positron emission tomography (PET) imaging, and followed by a computed tomography scan. The quantitative estimates of the brain uptake of [¹⁸F]flutemetamol (standard uptake value ratios (SUVRs)) from the biopsy site, contralateral to the biopsy site, and composite were made from the analysis of PET images. The quantitative IHC levels of amyloid load were estimated using a monoclonal antiamyloid β antibody, 4 G8 (in percent area), as the standard of truth (N?=?8, of which 5 had full histopathology staining). The primary analysis determined the level of association between the SUVR (with cerebellum as the reference region) from the biopsy site, and the level of amyloid was determined from IHC estimates of amyloid in the biopsy sample.

Results

[¹⁸F]Flutemetamol injection was found to be well tolerated. The biopsied area well represented the amyloid deposition throughout the cortex in this small sample. The biopsy site SUVR was significantly correlated with the biopsy specimen amyloid β level (expressed as percent of biopsy specimen area staining with 4 G8). The full model was significant (p?=?0.0174). In the secondary efficacy analyses, contralateral (to biopsy site) and composite SUVR values correlated significantly with the percent of biopsy specimen staining for amyloid β based on 4 G8. Blinded visual [¹⁸F]flutemetamol image interpretations showed a sensitivity of 100 % and a specificity of 100 % with pathology reads staining for amyloid plaque with Bielschowsky and thioflavin S and overall pathology read. The results of the blinded reader agreement for [¹⁸F]flutemetamol PET showed full agreement among three readers.

Conclusions

PET imaging of NPH patients following the administration of [¹⁸F]flutemetamol injection was highly correlated with the presence of fibrillar amyloid β in subsequent cortical biopsy samples in this small sample. Administration of [¹⁸F]flutemetamol injection was well tolerated.     

Automated brain tissue assessment in the elderly and demented population: construction and validation of a sub-volume probabilistic brain atlas

OBJECTIVES: To develop an automated imaging assessment tool that accommodates the anatomic variability of the elderly and demented population as well as the registration errors occurring during spatial normalization. METHODS: 20 subjects with Alzheimer's disease (AD), mild cognitive impairment, or normal cognition underwent MRI brain imaging and had their 3D volumetric datasets manually partitioned into 68 regions of interest (ROI) termed sub-volumes. Gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) voxel counts were then made in the subject's native space for comparison against automated volumetric measures within three sub-volume probabilistic atlas (SVPA) models. The three SVPAs were constructed using 12 parameter affine (12 p), 2nd order (2nd), and 6th order (6th) transforms derived from registering the manually partitioned scans into a Talairach compatible AD population-based target. The three SVPA automated measures were compared to the manually derived measures in the 20 subjects' native space with a "jack-knife" procedure in which each subject was assessed by an SVPA they did not contribute toward constructing. RESULTS: The mean left and right GM ratio (GM ratio = [GM + CSF] / CSF) "r values" for the 3 SVPAs compared to the manually derived ratios across the 68 ROIs were 0.85 for the 12p SVPA, 0.88 for the 2nd SVPA, and 0.89 for the 6th SVPA. The mean left and right WM ratio (WM ratio = [WM + CSF] / CSF) "r values" for the 3 SVPAs being 0.84 for the 12p SVPA, 0.86 for the 2nd SVPA, and 0.88 for the 6th SVPA. CONCLUSION: We have constructed, from an elderly and demented cohort, an automated brain volumetric tool that has excellent accuracy compared to a manual gold standard and is capable of regional hypothesis testing and individual patient assessment compared to a population.     

Using a reference tissue model with spatial constraint to quantify [11C]Pittsburgh compound B PET for early diagnosis of Alzheimer's disease

INTRODUCTION: Reference tissue model (RTM) is a compartmental modeling approach that uses reference tissue time activity curve (TAC) as input for quantification of ligand-receptor dynamic PET without blood sampling. There are limitations in applying the RTM for kinetic analysis of PET studies using [11C]Pittsburgh compound B ([11C]PIB). For region of interest (ROI) based kinetic modeling, the low specific binding of [11C]PIB in a target ROI can result in a high linear relationship between the output and input. This condition may result in amplification of errors in estimates using RTM. For pixel-wise quantification, due to the high noise level of pixel kinetics, the parametric images generated by RTM with conventional linear or nonlinear regression may be too noisy for use in clinical studies. METHODS: We applied RTM with parameter coupling and a simultaneous fitting method as a spatial constraint for ROI kinetic analysis. Three RTMs with parameter coupling were derived from a classical compartment model with plasma input: an RTM of 4 parameters (R(1), k'(2R), k(4), BP) (RTM4P); an RTM of 5 parameters (R(1), k(2R), NS, k(6), BP) (RTM5P); and a simplified RTM (SRTM) of 3 parameters (R(1), k'(2R), BP) (RTM3P). The parameter sets [k'(2R), k(4)], [k(2R), NS, k(6)], and k'(2R) are coupled among ROIs for RTM4P, RTM5P, and RTM3P, respectively. A linear regression with spatial constraint (LRSC) algorithm was applied to the SRTM for parametric imaging. Logan plots were used to estimate the distribution volume ratio (DVR) (=1+BP (binding potential)) in ROI and pixel levels. Ninety-minute [11C]PIB dynamic PET was performed in 28 controls and 6 individuals with mild cognitive impairment (MCI) on a GE Advance scanner. ROIs of cerebellum (reference tissue) and 15 other regions were defined on coregistered MRIs. RESULTS: The coefficients of variation of DVR estimates from RTM3P obtained by the simultaneous fitting method were lower by 77-89% (in striatum, frontal, occipital, parietal, and cingulate cortex) as compared to that by conventional single ROI TAC fitting method. There were no significant differences in both TAC fitting and DVR estimates between the RTM3P and the RTM4P or RTM5P. The DVR in striatum, lateral temporal, frontal and cingulate cortex for MCI group was 25% to 38% higher compared to the control group (p < or = 0.05), even in this group of individuals with generally low PIB retention. The DVR images generated by the SRTM with LRSC algorithm had high linear correlations with those from the Logan plot (R2 = 0.99). CONCLUSION: In conclusion, the RTM3P with simultaneous fitting method is shown to be a robust compartmental modeling approach that may be useful in [11C]PIB PET studies to detect early markers of Alzheimer's disease where specific ROIs have been hypothesized. In addition, the SRTM with LRSC algorithm may be useful in generating R(1) and DVR images for pixel-wise quantification of [11C]PIB dynamic PET.     

Voxel-based morphometry and automated lobar volumetry: The trade-off between spatial scale and statistical correction

Voxel-based morphometry (VBM) and automated lobar region of interest (ROI) volumetry are comprehensive and fast methods to detect differences in overall brain anatomy on magnetic resonance images. However, VBM and automated lobar ROI volumetry have detected dissimilar gray matter differences within identical image sets in our own experience and in previous reports. To gain more insight into how diverging results arise and to attempt to establish whether one method is superior to the other, we investigated how differences in spatial scale and in the need to statistically correct for multiple spatial comparisons influence the relative sensitivity of either technique to group differences in gray matter volumes. We assessed the performance of both techniques on a small dataset containing simulated gray matter deficits and additionally on a dataset of 22q11-deletion syndrome patients with schizophrenia (22q11DS-SZ) vs. matched controls. VBM was more sensitive to simulated focal deficits compared to automated ROI volumetry, and could detect global cortical deficits equally well. Moreover, theoretical calculations of VBM and ROI detection sensitivities to focal deficits showed that at increasing ROI size, ROI volumetry suffers more from loss in sensitivity than VBM. Furthermore, VBM and automated ROI found corresponding GM deficits in 22q11DS-SZ patients, except in the parietal lobe. Here, automated lobar ROI volumetry found a significant deficit only after a smaller sub-region of interest was employed. Thus, sensitivity to focal differences is impaired relatively more by averaging over larger volumes in automated ROI methods than by the correction for multiple comparisons in VBM. These findings indicate that VBM is to be preferred over automated lobar-scale ROI volumetry for assessing gray matter volume differences between groups.     

头颈部肿瘤分子生物纹理分析与生物靶区自适应勾画

目的探讨头颈部肿瘤分子生物纹理分析和自适应生物靶区(BTV)勾画方法。方法根据肿瘤PET图像的标准摄取值(SUV)及其分子生物"方差"(VAR)纹理特征,提出一种改进的两阶段自适应三维体生长肿瘤BTV勾画方法:首先根据PET图像灰度共生矩阵,提取肿瘤分子生物VAR纹理特征;之后联合肿瘤生物VAR纹理特征,改进先前提出的两阶段自适应三维体生长方法,并进行头颈部肿瘤BTV的自适应勾画。结果鼻咽癌临床PET影像试验和应用结果表明,改进的两阶段自适应三维体生长方法能自适应地勾画鼻咽癌BTV,结果正确合理。结论改进的两阶段自适应三维体生长方法能够更精确地自适应勾画头颈部肿瘤。     

Detecting hippocampal hypometabolism in Mild Cognitive Impairment using automatic voxel-based approaches

While the hippocampus is constantly reported as the site of earliest and highest structural alteration in Alzheimer's disease (AD), findings regarding the metabolic status of this region are rather heterogeneous. It has been proposed that only a time-consuming individual region-of-interest (ROI) approach would allow the detection of hypometabolism in this complex and small area. Our main goal with this study is to assess whether more automatic and clinically useful methods would be sensitive enough when considering other methodological confounds. From a single PET data set collected in 28 patients with amnestic Mild Cognitive Impairment (aMCI) and 19 controls, we assessed the effects of partial volume effect (PVE) correction, scaling (using vermis or global means), and analysis method (individual ROI versus more automatic template-based ROI or voxel-based approaches) on hippocampal hypometabolism detection in aMCI. PVE correction and scaling both showed a significant effect on group comparison, while the analysis method (individual versus template-based ROI) surprisingly did not. Hippocampal metabolic decrease was significant in all vermis-scaled conditions, and more so after PVE correction. Our findings highlight the crucial relevance of using reference-region-based (instead of global) scaling, and the higher sensitivity of PVE-corrected PET measures, to detect hippocampal hypometabolism in aMCI. They also show that hippocampal metabolic decline can be detected using template-based ROI as well as voxel-based methods. These findings have clinical relevance since they support the validity of more automatic and time-saving approaches to assess hippocampal metabolism changes in aMCI and early AD.     

Semiautomatic brain region extraction: a method of parcellating brain regions from structural magnetic resonance images

Structural MR imaging has become essential to the evaluation of regional brain changes in both healthy aging and disease-related processes. Several methods have been developed to measure structure size and regional brain volumes, but many of these methods involve substantial manual tracing and/or landmark identification. We present a new technique, semiautomatic brain region extraction (SABRE), for the rapid and reliable parcellation of cortical and subcortical brain regions. We combine the SABRE parcellation with tissue compartment segmentation [NeuroImage 17 (2002) 1087] to produce measures of gray matter (GM), white matter (WM), ventricular CSF, and sulcal CSF for 26 brain regions. Because SABRE restricts user input to a few easily identified landmarks, inter-rater reliability is high for all volumes, with all coefficients between 0.91 and 0.99. To assess construct validity, we contrasted SABRE-derived volumetric data from healthy young and older adults. Results from the SABRE parcellation and tissue segmentation showed significant differences in multiple brain regions in keeping with regional atrophy described in the literature by researchers using lengthy manual tracing methods. Our findings show that SABRE is a reliable semiautomatic method for assessing regional tissue volumes that provides significant timesavings over purely manual methods, yet maintains information about individual cortical landmarks.     

Synthesis,Radiolabeling, and In Vivo Pharmacokinetic Evaluation of the Amyloid Beta Radioligand [11C]AZD4694 in Nonhuman Primates

Purpose

[¹⁸F]AZD4694 (2-(2-¹⁸F-fluoro-6-(methylamino)-3-pyridyl)benzofuran-5-ol) is a radioligand suitable for imaging of amyloid beta deposits in the living human brain using positron emission tomography (PET). Here, we report the preparation and pharmacokinetic profile of its carbon-11 (t _1/2 ?=?20.4 min) labeled isotopolog [¹¹C]AZD4694 and compare [¹¹C]AZD4694 with the hitherto most widely applied amyloid PET radioligand [¹¹C]Pittsburgh Compound B (PiB).

Procedures

The immediate unlabeled precursor to [¹¹C]AZD4694 was prepared in a four-step convergent synthesis. Subsequent N-¹¹C-methylation of this precursor with [¹¹C]methyl iodide yielded [¹¹C]AZD4694, which after isolation and formulation was injected into cynomolgus monkeys. The radioactivity in nonhuman primate brain following injection of [¹¹C]AZD4694 and [¹¹C]PiB was measured using PET.

Results

[¹¹C]AZD4694 was prepared in a 60 % incorporation yield. In a head to head comparison with [¹¹C]PiB, it appeared that [¹¹C]AZD4694 displayed slightly lower nonspecific binding in white matter than [¹¹C]PiB as well as more rapid pharmacokinetics in the brain.

Conclusions

The advantageous pharmacokinetic profile and low nonspecific binding render [¹¹C]AZD4694 a promising PET radioligand for imaging of amyloid beta in the human brain with PET.     

FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping

Fully-automated brain segmentation methods have not been widely adopted for clinical use because of issues related to reliability, accuracy, and limitations of delineation protocol. By combining the probabilistic-based FreeSurfer (FS) method with the Large Deformation Diffeomorphic Metric Mapping (LDDMM)-based label-propagation method, we are able to increase reliability and accuracy, and allow for flexibility in template choice. Our method uses the automated FreeSurfer subcortical labeling to provide a coarse-to-fine introduction of information in the LDDMM template-based segmentation resulting in a fully-automated subcortical brain segmentation method (FS+LDDMM). One major advantage of the FS+LDDMM-based approach is that the automatically generated segmentations generated are inherently smooth, thus subsequent steps in shape analysis can directly follow without manual post-processing or loss of detail. We have evaluated our new FS+LDDMM method on several databases containing a total of 50 subjects with different pathologies, scan sequences and manual delineation protocols for labeling the basal ganglia, thalamus, and hippocampus. In healthy controls we report Dice overlap measures of 0.81, 0.83, 0.74, 0.86 and 0.75 for the right caudate nucleus, putamen, pallidum, thalamus and hippocampus respectively. We also find statistically significant improvement of accuracy in FS+LDDMM over FreeSurfer for the caudate nucleus and putamen of Huntington's disease and Tourette's syndrome subjects, and the right hippocampus of Schizophrenia subjects.     

Sysmex XN全自动血细胞分析仪在体液细胞计数及分类中的应用

目的评价Sysmex XN全自动血细胞分析仪体液模式在脑脊液和胸腹水标本细胞计数及分类计数中的应用价值。方法收集2019年7-8月该院住院患者体液标本341例,其中脑脊液标本138例及胸腹水标本203例,应用Sysmex XN全自动血细胞分析仪(仪器法)和改良牛鲍计数板(手工法)分别分类计数,对两种方法所测结果进行比较。结果仪器法与手工法对脑脊液标本有核细胞计数及红细胞计数一致性较好(ICC=0.985、0.994,均P<0.05),仪器法与手工法对脑脊液标本有核细胞分类计数中单个核细胞及多个核细胞计数一致性较好(ICC=0.917、0.946,均P<0.05);仪器法与手工法对胸腹水标本中有核细胞计数及红细胞计数一致性较好(ICC=0.960、0.996,均P<0.05);仪器法与手工法对胸腹水标本有核细胞分类计数中单个核细胞、多个核细胞及间皮细胞计数一致性较好(ICC=0.894、0.937、0.758,均P<0.05)。结论Sysmex XN全自动血细胞分析仪体液检测模式与手工法结果有较好的一致性,其操作简单、迅速,结果准确、可靠,重复性好,在体液细胞检测中具有较高的临床应用价值。     

Deep learning-guided estimation of attenuation correction factors from time-of-flight PET emission data

PurposeAttenuation correction (AC) is essential for quantitative PET imaging. In the absence of concurrent CT scanning, for instance on hybrid PET/MRI systems or dedicated brain PET scanners, an accurate approach for synthetic CT generation is highly desired. In this work, a novel framework is proposed wherein attenuation correction factors (ACF) are estimated from time-of-flight (TOF) PET emission data using deep learning.MethodsIn this approach, referred to as called DL-EM), the different TOF sinogram bins pertinent to the same slice are fed into a multi-input channel deep convolutional network to estimate a single ACF sinogram associated with the same slice. The clinical evaluation of the proposed DL-EM approach consisted of 68 clinical brain TOF PET/CT studies, where CT-based attenuation correction (CTAC) served as reference. A two-tissue class consisting of background-air and soft-tissue segmentation of the TOF PET non-AC images (SEG) as a proxy of the technique used in the clinic was also included in the comparative evaluation. Qualitative and quantitative PET analysis was performed through SUV bias maps quantification in 63 different brain regions.ResultsThe DL-EM approach resulted in 6.1 ± 9.7% relative mean absolute error (RMAE) in bony structures compared to SEG AC method with RMAE of 16.1 ± 8.2% (p-value <0.001). Considering the entire head region, DL-EM led to a root mean square error (RMSE) of 0.3 ± 0.01 outperforming the SEG method with RMSE of 0.8 ± 0.02 SUV (p-value <0.001). The region-wise analysis of brain PET studies revealed less than 7% absolute SUV bias for the DL-EM approach, whereas the SEG method resulted in more than 14% absolute SUV bias (p-value <0.05).ConclusionsQualitative assessment and quantitative PET analysis demonstrated the superior performance of the DL-EM approach over the segmentation-based technique with clinically acceptable SUV bias. The results obtained using the DL-EM approach are comparable to state-of-the-art MRI-guided AC methods. Yet, this approach enables the extraction of interesting features about patient-specific attenuation which could be employed not only as a stand-alone AC approach but also as complementary/prior information in other AC algorithms.