Reproducible brain PET data analysis: easier said than done.

BackgroundWhile a great deal of recent effort has focused on addressing a perceived reproducibility crisis within brain structural magnetic resonance imaging (MRI) and functional MRI research communities, less attention has been paid to reproducibility of brain positron emission tomography (PET) research.MethodsWe examined the current landscape of factors that contribute to reproducible neuroimaging data analysis, comparing brain MRI to brain PET. The factors included scientific standards, analytic plan pre‐registration, data and code sharing, containerized workflows, and standardized processing pipelines. To demonstrate the positive impact that further developing such reproducibility factors would have on brain PET research, we conducted one case study in which the brain PET processing pipeline developed for one flagship study (Alzheimer's Disease Neuroimaging Initiative, ADNI) for brain 18F‐florbetapir and 18F‐fluorodeoxyglucose (FDG) analysis was reproduced in our laboratory.ResultsCompared to fMRI, PET research encounters substantial challenges in adopting essential reproducibility practices. While 85% of fMRI studies adhere to the standardized reporting practices, PET is in the early stages of establishing reporting practices, lacking a standardized checklist. Pre‐registration is prevalent for fMRI (57.6%) but notably absent in PET, with no pre‐registered PET studies found on platforms such as the Center for Open Science. The number of shared datasets is higher for fMRI compared to PET, with 92 fMRI datasets and only 9 PET datasets on repositories like OpenNeuro. Moreover, there are very few data repositories for newer PET radiotracers. Containerized software availability also favor fMRI, with platforms like (Brain Imaging Data Structure, BIDS) apps hosting 66% dedicated fMRI tools, while no PET‐specific software is available. Standardized processing pipelines follow the same trend, with fMRI boasting optimized workflows for specific protocols, while PET has few dedicated pipelines and lacks validation studies for existing options. Furthermore, while our case study demonstrated excellent correlation results for both the 18F‐FDG and 18F‐florbetapir methods (Figure 2), the replication of the ADNI PET pipeline posed substantial challenges and implementation delays. These obstacles and delays in execution were due to incomplete documentation and ambiguities in application details.ConclusionPET neuroimaging lags behind its MRI‐related counterparts in achieving robust reproducibility.

Purpose: Neurofibromatosis type 1 (NF1), which is caused by mutations of the NF1 gene, is the most frequent single gene disorder to affect the nervous system. Unidentified bright objects (UBOs) are commonly observed on brain magnetic resonance imaging (MRI) in patients with NF1. However, their clinical and pathologic significance is not well understood. The purpose of this study was to investigate the correlation between UBOs and cerebral glucose metabolism measured by F-2Fluoro-2-deoxy-D-glucose (F-FDG) positron emission tomography (PET) in Korean patients with NF1. Materials and Methods: Medical records of 75 patients (34 males and 41 females) with NF1 who underwent brain MRI and PET between 2005 and 2011 were evaluated retrospectively. Clinical data including demographics, neurological symptoms, and brain MRI and PET findings, were reviewed. Results: UBOs were detected in the brain MRI scans of 31 patients (41%). The region most frequently affected by UBOs was the basal ganglia. The most frequent brain PET finding was thalamic glucose hypometabolism (45/75, 60%). Of the 31 patients with UBOs, 26 had thalamic glucose hypometabolism on brain PET, but the other 5 had normal brain PET findings. Conversely, of the 45 patients with thalamic glucose hypometabolism on brain PET, 26 showed UBOs on their brain MRI scans, but 19 had normal findings on brain MRI scans. Conclusion: UBOs on brain MRI scans and thalamic glucose hypometabolism on PET appear to be 2 distinctive features of NF1 rather than correlated symptoms. Because the clinical significance of these abnormal imaging findings remains unclear, a longitudinal follow-up study of changes in clinical manifestations and imaging findings is necessary.

BackgroundPartial volume correction with anatomical magnetic resonance (MR) images (MR-PVC) is useful for accurately quantifying tracer uptake on brain positron emission tomography (PET) images. However, MR segmentation processes for MR-PVC are time-consuming and prevent the widespread clinical use of MR-PVC. Here, we aimed to develop a deep learning model to directly predict PV-corrected maps from PET and MR images, ultimately improving the MR-PVC throughput.MethodsWe used MR T1-weighted and [11C]PiB PET images as input data from 192 participants from the Alzheimer’s Disease Neuroimaging Initiative database. We calculated PV-corrected maps as the training target using the region-based voxel-wise PVC method. Two-dimensional U-Net model was trained and validated by sixfold cross-validation with the dataset from the 156 participants, and then tested using MR T1-weighted and [11C]PiB PET images from 36 participants acquired at sites other than the training dataset. We calculated the structural similarity index (SSIM) of the PV-corrected maps and intraclass correlation (ICC) of the PV-corrected standardized uptake value between the region-based voxel-wise (RBV) PVC and deepPVC as indicators for validation and testing.ResultsA high SSIM (0.884 ± 0.021) and ICC (0.921 ± 0.042) were observed in the validation and test data (SSIM, 0.876 ± 0.028; ICC, 0.894 ± 0.051). The computation time required to predict a PV-corrected map for a participant (48 s without a graphics processing unit) was much shorter than that for the RBV PVC and MR segmentation processes.ConclusionThese results suggest that the deepPVC model directly predicts PV-corrected maps from MR and PET images and improves the throughput of MR-PVC by skipping the MR segmentation processes.

Accurate quantification of positron emission tomography (PET) is important for diagnosis and assessment of cancer treatment. The low spatial resolution of PET imaging induces partial volume effect to PET images that biases quantification. A PET partial volume correction method is proposed using high-resolution, anatomical information from magnetic resonance images (MRI). The corrected PET is pursued by removing the convolution of PET point spread function (PSF) and by preserving edges present in PET and the aligned MR images. The correction is implemented in a Bayesian's deconvolution framework that is minimized by a conjugate gradient method. The method is evaluated on simulated phantom and brain PET images. The results show that the method effectively restores 102 ± 7% of the true PET activity with a size of greater than the full-width at half maximum of the point spread function. We also applied the method to synthesized brain PET data. The method does not require prior information about tracer activity within tissue regions. It can offer a partial volume correction method for various PET applications and can be particularly useful for combined PET/MRI studies.

e20079 Background: Over the past several decades, first line therapy recommendations for small cell lung cancer (SCLC) have remained relatively unchanged. Additionally, the National Comprehensive Cancer Network (NCCN) guidelines regarding initial evaluation, staging, response assessment, and surveillance of SCLC have also remained relatively static. To evaluate radiologic imaging utilization for SCLC, we reviewed patients imaging data and assessed their association with staging, metastatic sites at presentation, overall survival, and other clinical factors. Methods: Retrospective analysis of patients with a diagnosis of SCLC that received treatment and follow up from 2003 through 2018 was completed. Cross-sectional radiology imaging data including computed tomography of the chest abdomen and pelvis (CT CAP), magnetic resonance imaging (MRI), and positron-emission tomography (PET) was obtained from the picture archiving and communication system (PACS) and clinical data including age, sex, race, smoking history, extensive/limited stage, and metastatic sites at presentation was obtained from the electronic medical record. Results: The average age at diagnosis for all SCLC patients (n = 518) from 2003 to 2018 was 66 with a male to female ratio of 0.89. The mean overall survival of all SCLC patients was 410.2 days with extensive (n = 371) stage = 312.5 days and limited (n = 147) stage = 718.8 days. We found a significantly increased number of patients less than age 66 presented with brain metastasis compared to those greater than age 66 ( P< 0.01). The absolute total number of CT CAP and PET studies was significantly increased in limited stage patients compared to extensive stage ( P< 0.001). However, when accounting for mean overall survival, extensive stage patients received a significantly increased number of CT Brain ( P< 0.05), PET ( P< 0.01), CT CAP, and MRI Brain ( P< 0.001) studies compared to limited stage per year. Conclusions: Overall radiology imaging utilization is significantly different between limited and extensive stage SCLC patients over the past 15 years.

Hippocampal atrophy and hypometabolism are key neuroimaging biomarkers for early diagnosis of Alzheimer's Disease (AD). Artificial intelligence methods for quantitative automated volumetric analysis of brain magnetic resonance imaging (MRI) are enabling objective assessment of brain atrophy in a fast and accessible way. Thus, the objective of this work is to compare hippocampal volume derived from MRI with the hypometabolism observed in 18F-FDG positron emission tomography (PET) in the context of early diagnosis of AD. Twenty-two patients with AD (11 women, [51-84yo], mean age±SD: 69.91±9.51) and twenty-six normal individuals (16 women, age range [51-80yo], mean age±SD: 66.23±6.87) were enrolled in this study. Structural T1-weighted and 18F-FDG scans were acquired on a 3T MR-PET system (Biograph mMR, Siemens Healthineers, Forchheim, Germany). A deep-learning-based research application (MorphoBox) was used to derive brain volume normalized by the total intracranial volume from MRI (Figure 1), and p-mod software was applied to calculate standardized uptake value ratio (SUVR) from PET imaging. Pearson correlations between normalized brain volumes and SUVR, as well as visual interpretations from diagnostic radiologists, were assessed. Receiver operating characteristics (ROC) analyses were conducted to investigate the discrimination ability of hippocampus relative volume on one hand and SUVR on the other hand. A significant difference in SUVR normalized by cerebellar SUV was observed between the AD and control groups in the following brain regions: hippocampus (p<0.001), frontal lobe (p<0.001), parietal lobe (p<0.001), temporal lobe (p<0.001), occipital lobe (p=0.001), cingulate (p<0.001), caudate (p=0.002) and thalamus (p<0.001). There was a strong correlation between SUVR and normalized volume, particularly in hippocampus, frontal, parietal, and temporal regions (ρ=0.60, 0.64, 0.67, and 0.73, respectively). The ROC curve for hippocampal relative volume yielded an AUC of 0.79 indicating good discrimination abilities (Figure 2). Our study demonstrates a strong correlation between hippocampal volumetry from MRI and FDG PET brain imaging. The MorphoBox research application under study could serve as a valuable screening tool in centers with restricted access to PET imaging. Additionally, our findings indicate that hippocampal volumetry exhibits superior discrimination power in the sample under study. We recommend further evaluation with a larger sample size in future research.

This study used 11C‐PBR28 positron emission tomography (PET) imaging to determine whether levels of 18‐kDa translocator protein (TSPO), an inflammation‐specific biomarker, are increased in frontotemporal lobar degeneration (FTLD) patients. 11C‐PBR28, 18F‐FDG, and 11C‐PIB brain PET scans, as well as magnetic resonance imaging (MRI), were conducted in four FTLD patients and 22 healthy controls. 11C‐PBR28 scans revealed that all FTLD patients showed increased TSPO binding versus controls. Significantly greater increases in TSPO were observed in the frontal, lateral temporal, parietal, and occipital cortices, topographically consistent with individual clinical phenotypes and with brain MRI and 18F‐FDG PET. Amyloid burden was not increased.

Positron emission tomography (PET) of the brain is an important problem-solving tool in pediatric neuroimaging, neurology, and neurosurgery. Fluorine 18 fluorodeoxyglucose (FDG) PET or dual-modality PET and computed tomographic (CT) imaging (PET/CT), with magnetic resonance (MR) imaging correlation, can be used to evaluate childhood epilepsy and pediatric brain tumors, areas in which PET adds value in patient management. FDG PET has been widely used in pediatric temporal lobe epilepsy, most commonly manifesting as mesial temporal sclerosis, which demonstrates hypometabolism at interictal PET and hypermetabolism during seizures. Recently, FDG PET has shown added value for patients with extratemporal epilepsy, in whom FDG PET can help identify cortical foci of interictal hypometabolism that are undetectable or difficult to detect with MR imaging. These findings can then guide additional investigations and surgery. FDG PET also enhances medical decision making in children with brain tumors, in whom FDG PET can be used to (a) improve the diagnostic yield of stereotactic biopsies by detecting metabolically active areas of tumor, (b) help guide the surgeon in achieving total tumor resection, and (c) increase detection of residual or recurrent tumor. Technologic advances in the past decade have allowed fusion of PET and MR images, combining the high resolution of MR imaging with the low-resolution functional capability of PET. As dual-modality integrated PET/MR imaging systems become available, CT coregistration for PET can be eliminated, thus reducing patient radiation exposure. Increasing familiarity with normal and abnormal appearances of FDG PET brain images correlated with MR images can enhance diagnostic yield and improve the care of children with epilepsy and brain tumors.

In medical imaging, positron emission tomography (PET) shows metabolic changes of an organism in pseudo color and magnetic resonance (MR) imaging presents anatomical structures in gray level. The PET and MR brain medical image fusion produces one composite image rendering the anatomical structures with metabolic changes to help doctors effectively diagnose a possible disease. For this purpose, a new fusion method based on Intensity-Hue-Saturation model and log-Gabor wavelet transform is proposed. First, MR image and the intensity component of PET image are decomposed by log-Gabor wavelet transform with suitable decomposition scale. Then, maximum selection’ fusion rule for the high-frequency sub-band and two-stage fusion rule based on weighted-averaging scheme and visibility measure for the low-frequency sub-band are employed. Finally, a new intensity component, obtained by applying reverse log-Gabor wavelet transform to the fused high-and low-frequency sub-bands, along with original hue and saturation components of PET image are converted to obtain our fused color image. In our fused images, both anatomical structures and color changes are rendered with effectively-reduced color distortion. Experimental results on twelve sets, including normal axial, normal coronal, and Alzheimer’s disease brain images, demonstrate that our fusion method outpaces framelet-transform-based method both visually and quantitatively.

Positron emission tomography (PET) has been widely used in various clinical applications. PET is a type of emission computed tomography and operates by positron annihilation radiation. With magnetic resonance imaging (MRI) providing anatomical information, joint PET/MRI reduces the radiation exposure risk of patients. Improved hardware and imaging algorithms have been proposed to further decrease the dose from radioactive tracers or the bed duration, but few methods focus on denoising low-count PET with MRI input. The existing methods are based on fixed conventional convolution and local attention, which do not sufficiently extract and fuse contextual and complementary information from multimodal input. There is still much room for improvement. Therefore, we propose a novel deep learning method for low-count PET/MRI denoising called the spatial-adaptive and transformer fusion network (STFNet), which consists of a Siamese encoder with a spatial-adaptive block (SA-block) and the transformer fusion encoder (TFE). Our proposed STFNet consists of a Siamese encoder with an SA-block, TFE, and two branches of the decoder. First, in the encoder, we adapt the SA-block in the Siamese encoder. The SA-block comprises deformable convolution with fusion modulation (DCFM) and two convolutional operations, which can promote network extraction of more relative and long-range contextual features. Second, the pixel-to-pixel TFE helps the network establish a local and global relationship between high-level feature maps of PET and MRI. In the decoder part, we design two branches for PET denoising and MRI translation, and predictions are obtained by trainable weighted summation. This proposed algorithm is implemented to predict synthetic standard-dose neck PET images from low-count neck PET images and MRI. Additionally, this method is compared with the existing U-Net and residual U-Net methods with and without MRIinput. To demonstrate the advantages of our method, we introduce configuration studies about TFE, ablation studies, and empirical comparative studies. Quantitative analyses are based on root mean square error (RSME), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and Pearson correlation coefficient (PCC). Additionally, qualitative results show the comparisons between our proposed method and other existing methods. All experimental results and visualizations show that our method achieves state-of-the-art performance in quantification andqualification. Based on our experiments, STFNet performs better than existing methods in measurement and visualization. However, our proposed method may still be suboptimal because we apply only the L1 loss to train our data set, and the data set includes corrupted PET with different low counts. In the future, we may exploit a generative adversarial network (GAN)-based paradigm in our STFNet to further improve the visualquality.

The purpose of this study was to assess the clinical potential of methyl-11C-choline (11C-choline) in the diagnosis of brain tumours. To this end, the results of 11C-choline positron emission tomography (PET) in 22 patients suspected of having brain tumours were compared with the findings of contrast-enhanced magnetic resonance (MR) imaging and fluorine-18 fluorodeoxyglucose PET. A histopathological diagnosis was made for each patient during open surgery. The standardised uptake values of brain tumours and the tumour-to-white matter count (T/W) ratios were determined. The degree of 11C-choline accumulation noted in PET images was compared with the gadolinium-enhanced areas of MR images. The mean T/W ratio of 11C-choline in high-grade gliomas was found to be higher than that in low-grade gliomas. This difference was statistically significant (mean+/-SD: 8.7+/-6.2, n=9 versus 1.5+/-0.7, n=5, P<0.03) when data pertaining to the prominent uptake of 11C-choline in a patient with a pilocytic astrocytoma were excluded. 11C-choline PET failed to detect non-neoplastic lesions in two patients. Areas of 11C-choline accumulation in PET scans were larger than areas enhanced on MR images in five cases involving high-grade gliomas. 11C-choline PET differentiated between low-grade gliomas and high-grade gliomas, but did not differentiate between low-grade gliomas and non-neoplastic lesions. The combination of 11C-choline PET and MR imaging may provide investigators with an accurate means by which to identify high-grade gliomas.

Small-animal positron emission tomography as a tool for neuropharmacology

Short stature is a common concern that necessitates pediatric endocrinology evaluation. Growth hormone (GH) deficiency is often included as an etiology. Brain and pituitary Magnetic Resonance Imaging (MRI) with gadolinium-based contrast agents (GBCAs) is the imaging modality of choice in assessing patients with GH deficiency. Given the significant strides made in MRI technology that allow improved spatial and contrast resolution, the necessity of using contrast material when obtaining brain and pituitary MRI in cases of short stature and isolated GH deficiency should be reassessed. We preformed a retrospective review of otherwise healthy patients with short stature and/or GH deficiency who underwent brain and pituitary MRI without and with contrast, to assess the benefit of contrast administration. Introduction: Short stature is a common concern that necessitates pediatric endocrinology evaluation. The etiologies of short stature are diverse. GH deficiency is often included as an etiology although it accounts for only 1-2% of short stature cases. The prevalence of GH deficiency is reported to be ~ 1:3500. The vast majority of GH deficiency cases are idiopathic in nature with only 20% due to organic causes. The organic causes of GH deficiency include congenital central nervous system (CNS) anomalies, tumors and other pathologic conditions that involve the pituitary-hypothalamic region. As a result, the radiological assessment of the hypothalamic-pituitary region is considered standard of care for evaluating patients with GH deficiency. Although brain and pituitary MRI is the imaging modality of choice in assessing patients with GH deficiency, its yield in cases of isolated GH deficiency is very low. In a study of 40 otherwise normal patients with isolated GH deficiency, 35 (87.5%) had normal brain MRIs. The abnormal findings of brain MRI in the minority of isolated GH deficiency cases included pituitary hypoplasia, pituitary stalk agenesis, lack of the normal T1-weighted pituitary hyperintensity in the posterior part of the sella turcica, and the presence of a high-intensity signal at the infundibular level representing ectopic neurohypophysis. Traditionally, these brain and pituitary MRI images are obtained with the use of contrast material (gadolinium). The main purpose of using contrast material is for the evaluation of pituitary microadenomas. Given the fact that significant strides made in MRI technology and pituitary microadenomas are not appeared to be associated with GH deficiency, the necessity of using contrast material when obtaining brain and pituitary MRI in cases of short stature and isolated GH deficiency should be reassessed. GBCAs have been shown to deposit in different tissues including the kidneys and the brain. The risk increases with repeated doses. The clinical significance of this deposition is unclear at this time but warrants caution especially in pediatric population who have a longer expected lifespan to manifest any delayed effects. Allergic reactions and gastrointestinal symptoms in pediatric patients can occur with GBCA administration, although the incidence is low. Using contrast material also increases the total cost of the MRI study and prolongs the time needed to complete it. Moreover, in order to use contrast material, intravenous venous (IV) access is required which causes discomfort and additional stress to children and their families. Therefore, we performed a retrospective review of otherwise healthy patients with short stature and/or growth hormone deficiency who underwent brain and pituitary MRI without and with contrast, to assess whether contrast administration led to diagnoses that would have otherwise been missed and/or impacted the patient’s clinical course.Objectives: - To compare the diagnostic yield of non-contrast MRI with pre and post-contrast MRI of the brain and pituitary in evaluation of pediatric patients with short stature and/or growth hormone deficiency.- A secondary objective is to measure the size of the pituitary gland and correlates it with peak growth hormone levels (using insulin/argenine). Methodology: We included patients who underwent brain/pituitary MRI with/without contrast performed at our institution between Jan 2013-Dec 2018 who have short stature/GH deficiency. We excluded patients with known diagnosis of other pituitary hormone deficiencies prior to obtaining MRI studies, genetic and neurological disorders, known tumors/malignancies of any type, or renal failure. Two pediatric neuroradiologists independently reviewed the brain and pituitary MRI of these patients (each read 50% of the cohort) blinded to the clinical data and diagnoses. Each radiologist initially reviewed only the non-contrast portions of the studies, and subsequently, the same radiologist reviewed the entire study, including pre- and post-contrast portions in a separate session. The two sessions were 6 weeks apart to avoid recall bias. Several imaging findings including size and morphology of pituitary gland, presence of congenital anomalies or focal lesions and any associated intracranial findings systematically recorded, and subsequently analyzed. Hypotheses: 1.The incidence of finding congenital pituitary cysts is the same when obtaining brain/pituitary MRI imaging using gadolinium contrast versus when not using contrast in patients with short stature and or isolate GH deficiency. 2.The incidence of discovering abnormal infundibulum is the same when obtaining brain/pituitary MRI imaging using gadolinium contrast versus when not using contrast in patients with short stature and or isolate GH deficiency. 3.Small pituitary size correlate with GH deficiency. Results: -We identified 327 patients with short stature/GH deficiency from Jan 2013-Dec 2018-224 (68.5%) are males and 103 (31.5%) are females. -The mean age at the time of imaging is 10 years and the median is 11 years. -161 (49.24%) have height z-score &amp;lt; -2.25 and 166 (50.76%) have height z-score &amp;gt; -2.25.-82 (25.07%) have IGF1 z-score for age &amp;lt; -2, 102 (31.19%) have z-score ≥-2 to ≤ -1, 141 (43.12%) have z-score &amp;gt; -1 and 2 (0.62%) have no level done.-63 (19.27%) have GH peak &amp;lt;5, 87 (26.61%) have GH peak 5-7.99, 53 (16.21%) have GH peak 8-9.99, 30 (9.17%) have GH peak &amp;gt; 10 and 94 (28.75%) did not undergo GH provocative testing. -The kappa coefficient for pars intermedia cyst on pre vs. post contrast imaging is 0.74 and 0.55 for the infundibulum on pre vs. post contrast imaging. -The mean pituitary height for patients with IGF z-score &amp;lt; -2 is 3.9 mm, 4 mm for z-score ≥- 2 to ≤ -1 and 4.3 mm for z-score &amp;gt; -1-The mean pituitary height for patients with peak GH &amp;lt; 5 is 3.8 mm, 4.2 mm for peak 5-7.99, 4.3 mm for peak 8-9.99 and 4.4 mm for peak &amp;gt; 10. Conclusion: This question has not been answered or even raised in the literature. Our findings suggest that the there is no added benefit to use gadolinium when obtaining brain/pituitary MRI for the evaluation of GH deficiency/short stature. Furthermore, it seems that there is an association between the pituitary height and the GH status of the cohort which is in line with previous published studies.

The combination of Positron Emission Tomography (PET) with magnetic resonance imaging (MRI) in hybrid PET/MRI scanners offers a number of advantages in investigating brain structure and function. A critical step of PET data reconstruction is attenuation correction (AC). Accounting for bone in attenuation maps (μ-map) was shown to be important in brain PET studies. While there are a number of MRI-based AC methods, no systematic comparison between them has been performed so far. The aim of this work was to study the different performance obtained by some of the recent methods presented in the literature. To perform such a comparison, we focused on [18F]-Fluorodeoxyglucose-PET/MRI neurodegenerative dementing disorders, which are known to exhibit reduced levels of glucose metabolism in certain brain regions. Four novel methods were used to calculate μ-maps from MRI data of 15 patients with Alzheimer's dementia (AD). The methods cover two atlas-based methods, a segmentation method, and a hybrid template/segmentation method. Additionally, the Dixon-based and a UTE-based method, offered by a vendor, were included in the comparison. Performance was assessed at three levels: tissue identification accuracy in the μ-map, quantitative accuracy of reconstructed PET data in specific brain regions, and precision in diagnostic images at identifying hypometabolic areas. Quantitative regional errors of -20--10% were obtained using the vendor's AC methods, whereas the novel methods produced errors in a margin of ±5%. The obtained precision at identifying areas with abnormally low levels of glucose uptake, potentially regions affected by AD, were 62.9 and 79.5% for the two vendor AC methods, the former ignoring bone and the latter including bone information. The precision increased to 87.5-93.3% in average for the four new methods, exhibiting similar performances. We confirm that the AC methods based on the Dixon and UTE sequences provided by the vendor are inferior to alternative techniques. As a novel finding, there was no substantial difference between the recently proposed atlas-based, template-based and segmentation-based methods.

Since cerebral infarction results from a reduction of cerebral blood flow (CBF) by the occlusion or stenosis of carotid or intracranial arteries, CBF is a primary parameter to predict of ischemic brain injury. Single-photon emission tomography (SPECT) and positron emission tomography (PET) contributed to evaluate loss of cerebral autoregulation, uncoupling state between CBF and brain metabolism, and ischemic penumbra. Measurement of CBF and oxygen metabolism by 15O PET revealed the process of infarct growth in hyperacute stage of cerebral infarction and areas with depressed oxygen metabolism, but normal water diffusion in magnetic resonance imaging (MRI) was termed as “metabolic penumbra.” Recently, some researchers shed light on the role of glial cells in the energy metabolism of the brain and 11C-acetate PET and demonstrated that astrocytic energy metabolism in TCA cycle was protective against ischemia. SPECT and PET studies for secondary reaction after ischemia (i.e., selective neuronal loss by 123I-iomazenil SPECT and 11C-flumazenil PET, tissue hypoxia by 18F-FMISO PET, and neuroinflammation by TSPO-PET) are expected as new biomarkers. Combining these imaging biomarkers with classical CBF measurement may contribute to develop innovative drugs for pharmacological neuroprotection in the therapy of cerebral infarction.