Study of multistep Dense U-Net-based automatic segmentation for head MRI scans.

Segmentation of cardiac structures is essential for cardiac function evaluation using cine magnetic resonance imaging (MRI). Deep learning can be used to segment cardiac structures in cine cardiac MRI with high accuracy, but this approach requires fully annotated datasets for training, which are difficult to obtain. Semi-supervised segmentation methods provide a way to alleviate the burden of manual labeling by using labeled and unlabeled data for training. However, these methods generally provide suboptimal segmentationaccuracies. To develop a semi-supervised method that utilizes relatively small training datasets and under-annotations for improved cine cardiac MRIsegmentation. The proposed approach consists of deformable registration, fully and weakly supervised segmentation, and a temporal attention perceiver (TAP). The registration module was trained to warp labeled frames to generate pseudo labels for unlabeled frames. The warped labeled images were used to train the fully supervised segmentation network. The unlabeled images and the pseudo label were used to train the weakly supervised segmentation model, and the segmentation prediction was compared with the input pseudo label as an auxiliary loss to the registration module. The TAP module was employed to generate optimized features for the warped labeled and the original unlabeled images both paired with the original labeled image. Consistency between the resulting features was enforced to refine cross-instance feature alignment to facilitate the registration. One hundred, twenty, and ten subjects from the Automatic Cardiac Diagnosis Challenge (ACDC) and seventy-five, thirty, and fifteen cases from the Multi-Vendor & Multi-Disease (M&Ms) Cardiac Image Segmentation Challenge were used for training, each with random end-systolic (ES)/end-diastolic (ED) frames labeled. The optimized models were used to segment the remaining 50 ACDC and 50 M&Ms subjects. The proposed approach was compared with several commonly used semi-supervised segmentation methods in terms of Dice-similarity-coefficients (DSC), average-symmetric-surface-distance (ASSD), and Hausdorff-distance (HD) for left (LV) and right (RV) ventricular cavity and myocardium (Myo). A Unet trained on the same subjects each with both frames labeled was used as an upper bound (Unet_UB). Using 100 ACDC training subjects, our approach yielded DSC = 0.910 0.063, ASSD = 1.37 0.63 mm, and HD = 6.38 2.99 mm for RV, DSC = 0.894 0.024, ASSD = 1.20 1.12 mm, and HD = 4.67 3.22 mm for Myo, and DSC = 0.934 0.056, ASSD = 1.25 1.63 mm, and HD = 3.97 5.76 mm for LV. A bidirectional copy-paste (BCP) method performed the best among the comparative methods and generated DSC = 0.902 0.060, ASSD = 1.45 0.60 mm, and HD = 7.50 3.20 mm for RV, DSC = 0.885 0.030, ASSD = 1.28 0.80 mm, and HD = 5.80 2.80 mm for Myo, and DSC = 0.920 0.068, ASSD = 1.15 0.40 mm, and HD = 4.20 3.30 mm for LV. For Unet_UB, these were 0.905 0.068, 1.48 0.61 mm, and 6.35 2.85 mm for RV, 0.895 0.030, 1.05 0.45 mm, and 4.40 3.09 mm for Myo, and 0.941 0.044, 1.02 0.34 mm, and 3.17 1.63 mm for LV. Similar trends were observed when using 75 M&Ms training subjects. For all the experiments, our approach outperformed BCP in general and yielded segmentation accuracies comparable to Unet_UB. The proposed approach outperformed several commonly used semi-supervised segmentation methods and yielded segmentation accuracies on par with fully supervised Unet using various relatively small datasets and under annotations fortraining.

Gray matter deficits in young onset schizophrenia are independent of age of onset

Four electric field modeling methods of Dosing Prefrontal Transcranial Magnetic Stimulation (TMS): Introducing APEX MT dosimetry

Association of white matter volume with brain age classification using deep learning network and region wise analysis

Differentiation of absolute metabolite concentrations between gray and white matter in the occipital region of normal human brain was performed by in vivo localized single-voxel 1H magnetic resonance spectroscopy at 1.5 Tesla with long echo time (136 ms). With the combination of image segmentation between white and gray matter and cerebrospinal fluid, signal compensation of T1 and T2 effects, tissue water signal as the internal concentration reference, as well as compensation by different water contents in gray and white matters, it was determined that the levels of N-acetylaspartate (NAA), creatine and/or phosphocreatine (Cr), and choline-containing compounds (Cho) in gray matter were significantly higher than in white matter. The averaged NAA, Cr, and Cho concentrations in gray matter were 11.0, 9.7, and 1.9 mM/liter, respectively, in comparison with 7.5, 5.2, and 1.6 mM/liter in white matter. These results suggest that precise composition of white and gray matter and cerebrospinal fluid is necessary to avoid partial voluming effect in a single voxel and to accurately quantify the metabolite concentrations.

In several diagnosis and therapy procedures based on electrostimulation effect, the internal physical quantity related to the stimulation is the induced electric field. To estimate the induced electric field in an individual human model, the segmentation of anatomical imaging, such as magnetic resonance image (MRI) scans, of the corresponding body parts into tissues is required. Then, electrical properties associated with different annotated tissues are assigned to the digital model to generate a volume conductor. However, the segmentation of different tissues is a tedious task with several associated challenges specially with tissues appear in limited regions and/or low-contrast in anatomical images. An open question is how segmentation accuracy of different tissues would influence the distribution of the induced electric field. In this study, we applied parametric segmentation of different tissues to exploit the segmentation of available MRI to generate different quality of head models using deep learning neural network architecture, named ForkNet. Then, the induced electric field are compared to assess the effect of model segmentation variations. Computational results indicate that the influence of segmentation error is tissue-dependent. In brain, sensitivity to segmentation accuracy is relatively high in cerebrospinal fluid (CSF), moderate in gray matter (GM) and low in white matter for transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES). A CSF segmentation accuracy reduction of 10% in terms of Dice coefficient (DC) lead to decrease up to 4% in normalized induced electric field in both applications. However, a GM segmentation accuracy reduction of 5.6% DC leads to increase of normalized induced electric field up to 6%. Opposite trend of electric field variation was found between CSF and GM for both TMS and tES. The finding obtained here would be useful to quantify potential uncertainty of computational results.

The brain volume of chronic drinkers is known to partially recover with abstinence from alcohol. To investigate the relative contribution of grey and white brain matter to this process, magnetic resonance imaging and brain tissue segmentation was used to study brain tissue in acute alcohol withdrawal and abstinence in seven alcohol-dependent men. The patients were studied on three occasions; within 48 h after the last drink and approximately one month and two and a half months later. Total brain tissue class volumes [grey matter (GM) and white matter (WM) and cerebrospinal fluid (CSF)] were measured. Eleven healthy volunteers were scanned twice to serve as a control group. The alcohol-dependent patients were investigated with regard to drinking variables, neuropsychological performance and blood biochemistry. In the alcohol-dependent patients, intracranial volume and total GM volume did not change between scan occasions, except in a single patient who demonstrated a GM increase of 4.8% (4.2% relative volume) between scans 2 and 3. For all patients, the increase in total WM volume ranged between 1.9 and 22.4% (absolute volumes) and 2.1 and 21.2% (relative volumes). Between scans 2 and 3, the increase in total WM volume ranged between 0.3 and 13.2% (absolute volumes), and between 1.5 and 14.0% (relative volumes). One patient resumed drinking and was investigated a second time during acute withdrawal. In this patient, the measured decrease of 8.1 and 8.5% of absolute and relative WM volumes corresponded to the size of the volume increase between scans 1 and 2. CSF, GM and WM volumes in the healthy subjects were constant over time. The results demonstrate that changes in brain volume during short-term abstinence in chronic alcohol-dependent patients are confined to the WM. The time limit of WM volume restitution is variable and continues longer than 3 weeks after withdrawal.

Although broadly utilized, computed tomography (CT) has been superseded by magnetic resonance imaging (MRI) for the volumetric assessment of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) in the aging human brain. Nevertheless, many scenarios remain where MRI is unavailable or discouraged; furthermore, in developing countries, CT can often be the only accessible imaging method for assessing brain structure in older patients with mild cognitive impairment or Alzheimer’s disease. Thus, there is merit in developing effective approaches for the estimation of brain volumetrics using CT. Here, MRI and CT scans were acquired from 10 older adults [mean (µ) ± standard deviation (σ) of age = 65 ± 7 yrs; 5 females]. To demarcate WM, GM and CSF from head CT, we developed a brain segmentation method based upon probabilistic, atlas-dependent classification. MRI-only segmentation was compared to CT-only segmentation; similarity was calculated through the Dice coefficient (DC). A normal distribution of DCs was found after contrasting the methods [µ ± σ across participants: 85.5% ± 4.6% (WM), 86.7% ± 5.6% (GM) and 91.3% ± 2.8% (CSF)], suggesting a satisfactory capacity of CT to assess brain volumetrics. Sensitivity was adequate: WM, GM and CSF volumes were estimated within ~5%, ~4% and ~3% of their MRI-based values. There was no indication of volume over- or under-estimation with CT [t (9) = 0.89, p > 0.80]. These results facilitate the integration of CT-based brain volumetrics with MRI, thereby offering a wider range of methods for quantifying macroscale brain changes in neurodegenerative diseases.

Introduction: Advancing age is associated with global brain atrophy. Cross-sectional studies have found sex differences in neuroanatomy; however, longitudinal studies assessing sex differences in neurodegeneration are currently scarce. The effects of age and sex on brain atrophy may not be uniform across the whole brain and may partially explain the sex differences observed in dementia. The current study aimed to examine sex differences in longitudinal atrophy patterns in gray and white matter regions in older adults. Methods: The study sample included 1,480 individuals from the Age, Gene/Environment Susceptibility (AGES)-Reykjavik Study, a population-based cohort study, that underwent two magnetic resonance imaging scans within an average of 5 years between assessments. Individuals were also followed-up for incident dementia diagnosis. Linear regression models were used to assess sex differences between mean differences in gray and white matter regions, correcting for age, education, baseline intracranial volume, baseline regional volumes, hypertension, body mass index, and apolipoprotein (APOE) e4 allele status. Results: Men showed increased longitudinal atrophy in the total gray matter, as well as in the parietal cortex, cingulate cortex, caudate nucleus, brainstem, left cerebellum, precentral gyrus, putamen, globus pallidus, and orbitofrontal cortex. Whereas women exhibited greater atrophy over time in total white matter, but not in specific regions. No moderation was found between sex differences in incident dementia regarding atrophy patterns. Conclusions: While men show larger gray matter volumes cross-sectionally, their rates of atrophy over time are steeper compared to women. Sex differences in brain atrophy seem to be specifically detrimental in men in regions related to executive functioning, motor control, and emotion processing.

Hallucinations are considered one of the primary symptoms of psychosis; however, their neural basis remains unclear. Voxel-based morphometry (VBM)1 studies have associated hallucinations with gray matter (GM) volume reduction in the superior temporal gyrus in schizophrenia.2 Although hallucinations are associated with brain connectivity,3 few studies have investigated their relationship with white matter (WM) volume. Moreover, these studies have shown inconsistent results.4, 5 The chronicity of illness and long-term medications for schizophrenia may limit the interpretation of previous findings.2 Thus, hallucination studies in clinical high risk for psychosis (CHR) and first-episode psychosis (FEP) are required, with minimal effects of chronicity of illness and medication. However, no VBM study has examined the relationship between hallucinations and WM volume in CHR and FEP. Therefore, we aimed to investigate the regional GM and WM volumes associated with hallucinations in CHR and FEP. We enrolled 57 individuals with CHR, 50 individuals with FEP, and 33 healthy controls (HCs). The Comprehensive Assessment of At-Risk Mental States-Japanese version6 was used to confirm whether ultra-high risk criteria7 were met. Table S1 summarizes the demographic and clinical data of the patients. Hallucination severity was assessed using the Positive and Negative Syndrome Scale (PANSS)8 P3 score. Fig. S1 shows the distribution of the severity of hallucinations. Some of those with a PANSS P3 score of 3 or higher were classified as having CHR because of the low frequency and duration of hallucinations. Magnetic resonance imaging (MRI) data were acquired with a 1.5-T Philips scanner. We examined the association of GM and WM volume with hallucination severity in the group that combined patients with CHR and FEP (CHR/FEP combined group) and performed group comparisons of GM and WM volume between the CHR/FEP combined and HC groups. Age, sex, and intracranial volume were entered as nuisance covariates for each analysis. VBM analysis was conducted using DARTEL9 in SPM12 (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/) with standard smoothing of 8-mm full width at half maximum (FWHM). The initial statistical threshold was set at P < 0.001, uncorrected at the voxel level. The extent threshold for cluster size was set based on the expected number of voxels per cluster provided by SPM. Familywise error (FWE) correction at the cluster level (P < 0.05) was applied to identify significant clusters. Appendix S1 provides detailed information on these methods. This study was performed in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Tohoku University Graduate School of Medicine and Tohoku University Hospital. Written informed consent was obtained from participants aged 18 years or older and from the parents of those under 18 years of age, with written assent from the participants. The anonymity of participants was preserved. WM volume was not significantly correlated with hallucination severity in the CHR/FEP combined group after FWE correction at the cluster level. Reanalysis with 15-mm or 20-mm FWHM filtering did not show significant associations. Additionally, there was no significant difference in the GM and WM volumes between the CHR/FEP combined and HC groups. No regional GM volume abnormalities were significantly correlated with hallucination severity in the CHR/FEP combined group. Appendix S1, Figs. S2–S5, and Tables S2–S5 provide uncorrected results and detailed information regarding these results. To the best of our knowledge, this is the first VBM study to investigate WM volume related to hallucinations in CHR and FEP. We found no significant correlation between WM volume and hallucination severity. In contrast to previous studies in schizophrenia,2, 4, 5 this study examined the brain volume association of hallucinations in CHR and FEP. CHR and FEP are heterogeneous groups in the early stages of mental illness and do not necessarily convert to schizophrenia. Our results indicate that brain disturbances involved in hallucinations in CHR and FEP may be more subtle and/or inconsistent with those in schizophrenia. Diffusion tensor imaging, a neuroimaging method applied to CHR,10 may be suitable for examining structural abnormalities in the development of hallucinations. This study has some limitations. First, information regarding hallucinations is not detailed. Second, the cross-sectional design limited the interpretation of our findings. Third, we used an MRI scanner with a lower field strength than that of recent studies.4 Fourth, there were significant age differences between the groups. Fifth, automatic quality assurance for MRI scans was not performed. In conclusion, advanced neuroimaging studies are needed to reveal the mechanism of hallucinations. This work was supported by JSPS KAKENHI (grant number 18K07586). We thank the participants, staff of the SAFE clinic, and radiological technologists. The authors declare no conflicts of interest. Appendix S1. Supporting information Figure S1. Distribution of hallucination severity. CHR, clinical high risk for psychosis; FEP, first-episode psychosis; PANSS, Positive and Negative Syndrome Scale. Figure S2. Brain regions with lower gray matter volume in the CHR/FEP group compared to the HC group. Statistical threshold was set at P < 0.001, uncorrected at the voxel level. The threshold extent for cluster size was set to k = 117 based on the expected number of voxels per cluster provided by SPM. The full width at half maximum was set to 8-mm. CHR, clinical high risk for psychosis; FEP, first-episode psychosis; HC, healthy controls; SPM, Statistical Parametric Mapping Figure S3. Brain regions with lower white matter volume in the CHR/FEP group compared to the HC group. Statistical threshold was set at P < 0.001, uncorrected at the voxel level. The threshold extent for cluster size was set to k = 129 based on the expected number of voxels per cluster provided by SPM. The full width at half maximum was set to 8-mm. CHR, clinical high risk for psychosis; FEP, first-episode psychosis; HC, healthy controls; SPM, Statistical Parametric Mapping Figure S4. Positive correlation between gray matter volume and hallucination severity in the patients at clinical high risk for psychosis. Statistical threshold was set at P < 0.001, uncorrected at the voxel level. The threshold extent for cluster size was set to k = 101 based on the expected number of voxels per cluster provided by the SPM. The full width at half maximum was set to 8-mm. SPM, Statistical Parametric Mapping Figure S5. Negative correlation between white matter volume and hallucination severity in the patients at clinical high risk for psychosis. Statistical threshold was set at P < 0.001, uncorrected at the voxel level. The threshold extent for cluster size was set to k = 121 based on the expected number of voxels per cluster provided by the SPM. The full width at half maximum was set to 8-mm. SPM, Statistical Parametric Mapping Table S1. Demographic and clinical characteristics of the participants. †Educational level, CP-equivalent dose on MRI scan, PANSS positive, PANSS negative, PANSS general, PANSS total, PANSS P3: hallucinatory behavior, and days from enrollment to MRI scan were analyzed using the t-test. Age and intracranial volume were analyzed using one-way analysis of variance. Sex and antipsychotic use were analyzed using the chi-squared test. Significant results are shown in bold. CHR, clinical high risk for psychosis; CP-equivalent, chlorpromazine equivalent; FEP, first-episode psychosis; HC, healthy controls; PANSS, Positive and Negative Syndrome Scale. Table S2. Between-group comparison of regional gray matter volume. CHR/FEP < HC (extent threshold: 117 voxels). † Uncorrected p-value at voxel level. ‡ familywise error-corrected p-value at the cluster level. k, cluster size at P < 0.001 (uncorrected); CHR, clinical high risk for psychosis; FEP, first-episode psychosis; HC, healthy controls; L/R, left/right hemisphere; MNI, Montreal Neurological Institute. The extent threshold for cluster size was set based on the expected number of voxels per cluster provided by the SPM. The full width at half maximum was set to 8-mm. Table S3. Between group comparison of regional white matter volume. CHR/FEP < HC (extent threshold: 129 voxels). † Uncorrected P-value at voxel level. ‡ familywise error-corrected p-value at the cluster level. k, cluster size at P < 0.001 (uncorrected); CHR, clinical high risk for psychosis; FEP, first-episode psychosis; HC, healthy controls; L/R, left/right hemisphere; MNI, Montreal Neurological Institute. The extent threshold for cluster size was set based on the expected number of voxels per cluster provided by the SPM. The full width at half maximum was set to 8-mm. Table S4. Correlation between gray matter volume and hallucination severity in the patients with CHR. Positive correlation (extent threshold: 101 voxels). † Uncorrected p-value at voxel level. ‡ familywise error-corrected p-value at the cluster level. k, cluster size at P < 0.001 (uncorrected); CHR, clinical high risk for psychosis; L/R, left/right hemisphere; MNI, Montreal Neurological Institute. The extent threshold for cluster size was set based on the expected number of voxels per cluster provided by the SPM. The full width at half maximum was set to 8-mm. Table S5. Correlation between white matter volume and hallucination severity in the patients with CHR. Negative correlation (extent threshold: 121 voxels). † Uncorrected p-value at voxel level. ‡ familywise error-corrected p-value at the cluster level. k, cluster size at P < 0.001 (uncorrected); CHR, clinical high risk for psychosis; L/R, left/right hemisphere; MNI, Montreal Neurological Institute. The extent threshold for cluster size was set based on the expected number of voxels per cluster provided by the SPM. The full width at half maximum was set to 8-mm. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Backgroundand Purpose:This study aimed to develop and evaluate an efficient method to automatically segment T1- and T2-weighted brain magnetic resonance imaging (MRI) images. We specifically compared the segmentation performance of individual convolutional neural network (CNN) models against an ensemble approach to advance the accuracy of MRI-guided radiotherapy (RT) planning.Materials and Methods. The evaluation was conducted on a private clinical dataset and a publicly available dataset (HaN-Seg). Anonymized MRI data from 55 brain cancer patients, including T1-weighted, T1-weighted with contrast, and T2-weighted images, were used in the clinical dataset. We employed an EDL strategy that integrated five independently trained 2D neural networks, each tailored for precise segmentation of tumors and organs at risk (OARs) in the MRI scans. Class probabilities were obtained by averaging the final layer activations (Softmax outputs) from the five networks using a weighted-average method, which were then converted into discrete labels. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and Hausdorff distance at 95% (HD95). The EDL model was also tested on the HaN-Seg public dataset for comparison.Results. The EDL model demonstrated superior segmentation performance on both the clinical and public datasets. For the clinical dataset, the ensemble approach achieved an average DSC of 0.7 ± 0.2 and HD95 of 4.5 ± 2.5 mm across all segmentations, significantly outperforming individual networks which yielded DSC values ≤0.6 and HD95 values ≥14 mm. Similar improvements were observed in the HaN-Seg public dataset.Conclusions. Our study shows that the EDL model consistently outperforms individual CNN networks in both clinical and public datasets, demonstrating the potential of ensemble learning to enhance segmentation accuracy. These findings underscore the value of the EDL approach for clinical applications, particularly in MRI-guided RT planning.

Introduction: Magnetic resonance imaging (MRI) is the most common tool to examine glioblastoma. Preoperative MRI can be used to initial diagnosis, and surgery planning, while follow-up MRIs can be used to evaluate treatment responses, identify recurrency, and detect side effects. The follow-up MRIs are usually taken after the first-line therapy, such as maximal safe resection. In the past, radiologists manually segment the tumor regions from normal brain tissue on follow-up MRIs, which is time-consuming, error-prone, and challenging. Several deep-learning (DL) models have been developed utilizing preoperative images, but their performance has yet to be evaluated on follow-up MRIs. In this research, we built the largest follow-up MRI cohort (311 patients) to assess these DL models and their generalizability and performance on independent preoperative and follow-up images. We also made the first follow-up-based deep learning models for this specific task. Methods: All evaluation deep learning models (10 models) were trained by the Brain Tumor Segmentation challenge 2020 (BraTS’20) and evaluated by fifty pairs of preoperative and follow-up scans from our institution. The segmentation form our institution is evaluated by board certified radiologist. MRIs in the BraTS’20 dataset were all preoperative scans. After the evaluation, we randomly assigned 264 patients’ scans from our institution to the training dataset and 47 patients’ scans to the testing dataset. We compared three types of models in our follow-up deep learning model, including 1) UNet-3D, 2) UNet-3D+transfer-learning, and 3) UNet-3D+transfer-learning+baysian-learning. The benchmark for all models was the Dice similarity coefficient (DSC). DSC can measure the spatial overlap between model prediction and ground truth. The value of DSC is between zero to one. Zero means no overlap and one indicates complete overlap. Results: Our study demonstrates that the BraTS'20 trained models' performance decreased by 13.05% in independent preoperative MRI scans and 19.04% in follow-up MRI scans. The most significant mismatch regions were FLAIR hyperintense regions (3.68% drop in independent preoperative scans and 10.61% drop in independent follow-up scans) and Non-enhancing core (5.20% drop in independent preoperative scans and 11.99% drop in independent follow-up scans). Our best model can achieve the best DSC among three tumor regions compared to all evaluation models (FLAIR hyperintense regions: DSC 0.77 V.S. DSC 0.58; Enhancing tumor region: DSC 0.87 V.S. DSC 0.68; Non-enhancing tumor region: DSC 0.92 V.S. DSC 0.55). Conclusion: Maximal safe resection induced brain structure change, decreasing the performance of the preoperative-based DL model. Implementing a follow-up MRI-based segmentation model is essential to make accurate and generalizable results to address structural changes after maximal safe resection. Our follow-up DL model demonstrates the DSC score can be recovered. We commit to further developing the tool to assist radiologists in handling follow-up MRIs for glioblastoma patients. Citation Format: Kang Lin Hsieh, Tanjida Kabir, Luis Nunez-Rubiano, Yu-Chun Hsu, Yu Cai, Juan Rodriguez Quintero, Octavio Arevalo, Kangyi Zhao, Jackie Zhang, Jiguang Zhu Zhu, Roy Riascos, Xioaqian Jiang, Shayan Shams. A confident and operator-independent deep segmentation model to measure residual tumor volume in the follow-up MRIs for glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB067.

Cranial Ultrasonography

The correct interpretation of clinical positron emission tomography (PET) data depends largely on the physical limits of the PET scanner. The partial volume effect (PVE) is related to the size of the studied object compared to the spatial resolution. It represents one of the most important limiting factors in quantitative data analysis. This effect is increased in the case of atrophy, as in patients with Alzheimer disease (AD), and it influences measurement of the metabolic reduction generally seen in cerebral degeneration. In this case, interpretation can be biased, because cortical activity will be underestimated due to the atrophy. In general, anatomical images of AD patients have shown diffuse atrophy, while PET studies have found widespread hypometabolism affecting the parietal and temporal lobes. Although hypometabolic areas usually correspond to atrophic regions, they also occur without such changes. Thus, the aim is to differentiate authentic hypometabolism (decrease of glucose consumption per unit volume of gray matter) from that due to PVE from atrophy (cell loss). Consequently, we are using a method for three-dimensional (3D) correction of human PET data with 3D magnetic resonance imaging (MRI). We measured atrophy and metabolism by using both T1-weighted MR images and high and medium resolution PET scans. We injected 12 patients and controls with [18F]fluorodeoxyglucose for glucose consumption measurements. Atrophy was estimated in the following way. We isolated the cerebral structures, using a segmentation technique on the MRI scans, into gray matter (GM), white matter, and cerebrospinal fluid. We superimposed the PET images onto the MR images to obtain anatomo-functional correlations. We degraded the segmented MR images to the resolution of the PET images by a convolution process to create a PET image correction map. We corrected the metabolic PET data for the PVE. We studied the cerebral metabolic rate of glucose in the GM where metabolic variation is the most relevant to AD. By dealing with problems relating to the sensitivity to the segmentation and to the PET-MRI coregistration, computation of MRI convolution processes provided the degree of PVE on a pixel-by-pixel basis, allowing correction of hypometabolisms contained in GM PET values. Global cortical metabolism increased after correction for PVE by, on average, 29 and 24% for tomographs acquired with medium (TTV03 LETI) and high (ECAT 953B CTI/Siemens) resolution, respectively, whereas the cortical metabolism increased by 75 and 65% for the respective tomographs in AD patients. The difference of metabolism between scans after correction for PVE was less than before correction, decreasing from 31 to 17%. This difference was most marked in the frontal and temporal lobes. Fusion imaging allowed correction for PVE in metabolic data using 3D MRI and determination of whether a change in the apparent radiotracer concentration in PET data reflected an alteration in GM volume, a change in radiotracer concentration per unit volume of GM, or both.

Effects of low-level sarin and cyclosarin exposure and Gulf War Illness on Brain Structure and Function: A study at 4 T