Is MR-guided Attenuation Correction a Viable Option for Dual-Modality PET/MR Imaging?

ObjectiveWe developed a new computed tomography (CT)-based spatial normalization method and CT template to demonstrate its usefulness in spatial normalization of positron emission tomography (PET) images with [18F] fluorodeoxyglucose (FDG) PET studies in healthy controls.Materials and MethodsSeventy healthy controls underwent brain CT scan (120 KeV, 180 mAs, and 3 mm of thickness) and [18F] FDG PET scans using a PET/CT scanner. T1-weighted magnetic resonance (MR) images were acquired for all subjects. By averaging skull-stripped and spatially-normalized MR and CT images, we created skull-stripped MR and CT templates for spatial normalization. The skull-stripped MR and CT images were spatially normalized to each structural template. PET images were spatially normalized by applying spatial transformation parameters to normalize skull-stripped MR and CT images. A conventional perfusion PET template was used for PET-based spatial normalization. Regional standardized uptake values (SUV) measured by overlaying the template volume of interest (VOI) were compared to those measured with FreeSurfer-generated VOI (FSVOI).ResultsAll three spatial normalization methods underestimated regional SUV values by 0.3-20% compared to those measured with FSVOI. The CT-based method showed slightly greater underestimation bias. Regional SUV values derived from all three spatial normalization methods were correlated significantly (p < 0.0001) with those measured with FSVOI.ConclusionCT-based spatial normalization may be an alternative method for structure-based spatial normalization of [18F] FDG PET when MR imaging is unavailable. Therefore, it is useful for PET/CT studies with various radiotracers whose uptake is expected to be limited to specific brain regions or highly variable within study population.

Small-animal positron emission tomography as a tool for neuropharmacology

In the management of brainstem lesions, the place of stereotactic biopsy sampling remains debatable. The authors compared the results of magnetic resonance (MR) imaging, positron emission tomography (PET) scanning, and histological studies obtained in 30 patients who underwent MR imaging- and PET-guided stereotactic biopsy procedures for a brainstem mass lesion. Between July 1991 and December 1998, 30 patients harboring brainstem mass lesions underwent a stereotactic procedure in which combined MR imaging and PET scanning guidance were used. Positron emission tomography scanning was performed using [18F]fluorodeoxyglucose in 16 patients, methionine in two patients, and both tracers in 12 patients. Definite diagnosis was established on histological examination of the biopsy samples. Interpretation of MR imaging findings only or PET findings only was in agreement with the histological diagnosis in 63% and 73% of cases, respectively. Magnetic resonance imaging and PET findings were concordant in 19 of the 30 cases; in those cases, imaging data correlated with histological findings in 79%. Treatment based on information derived from MR imaging was concordant with therapy based on histological findings in only 17 patients (57%). Combining MR imaging and PET scanning data, the concordance between the neuroimaging-based treatment and treatments based on histological findings increased to 19 patients (63%). In seven patients who underwent biopsy procedures with one PET-defined and one MR imaging-defined trajectory, at histological examination the PET-guided samples were more representative of the tumor's nature and grade than the MR imaging-guided samples in four cases (57%). In 18 patients PET scanning was used to define a biopsy target and provided a diagnostic yield in 100% of the cases. Although the use of combined PET and MR imaging improves radiological interpretation of a mass lesion in the brainstem, it does not accurately replace histological diagnosis that is provided by a stereotactically obtained biopsy sample. Combining information provided by MR imaging and PET scanning in stereotactic conditions improves the accuracy of targeting and the diagnostic yield of the biopsy sample; an MR imaging- and PET-guided stereotactic biopsy procedure is a safe and efficient modality for the management of mass lesions of the brainstem.

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.

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.

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.

To compare quantitative values of cerebral blood flow (CBF) derived from dynamic susceptibility contrast (DSC) magnetic resonance (MR) imaging with reference standard positron emission tomography (PET) in patients with confirmed cerebrovascular occlusive disease. Local institutional review board approval and informed consent were obtained for a prospective study of 18 patients (six men, 12 women; age range, 28-71 years; mean age, 45 years ± 10.4 [standard deviation]) with angiographically confirmed Moyamoya (n = 8) or internal carotid artery occlusions (n = 10). DSC MR images and oxygen 15-labeled water (H(2)[(15)O]) PET images were acquired on the same day. DSC images were postprocessed to yield parametric images of CBF (in mL/100 g/min), coregistered, and analyzed using grid-based regions of interest. Mean values of CBF in each region of interest from MR imaging and PET data sets were compared. Correlations for each patient were determined and overall agreement between pooled MR imaging and PET CBF was reported using linear regression analysis and Bland-Altman plots. Strong correlations (r(2) ≥ 0.55) were found between MR imaging and PET CBF values in all patients. Use of the bookend approach was found to underestimate CBF predictably across the patient cohort (mean slope, 0.82; standard deviation, 0.18; slope of aggregated data, 0.75). This allowed for a simple rescaling of MR imaging values producing strong agreement with PET values in the aggregated data (r(2) = 0.66; slope = 1.00; intercept = 0.00). The data show that the bookend MR imaging technique produces similar results for quantitative CBF between DSC MR imaging and H(2)[(15)O] PET. Although MR-derived CBF underestimated PET-derived CBF, the patient-to-patient variability in the slopes of the linear MR and PET relationships was significantly smaller than a competing quantitation technique. As a result, the bookend technique appears to more predictably measure quantitative CBF in a clinical setting.

The medical image can be divided into two major classes that are anatomical image and functional image. Magnetic resonance image (MRI) and computed tomography (CT) belong to the anatomical image, which can show an outline of an organ and a tissue clearly. The functional image can show the image of the organ and the tissue metabolism situation. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) are also part of the functional image. In this paper, we use 4-[18F]-ADAM to measure serotonin transporter changes and find the relation with major depressive disorder in CT / PET three-dimensional image. In clinical diagnosis, based on the video imaging methods can be divided into the anatomy and function of these two categories. Therefore, the combination of anatomical and functional imaging can provide more useful information. In the study, we use CT image to get the reference position, and combine with isotope metabolic situation from PET. We use the integrated image to do 3D image and count serotonin transporter with different points in time, and measure the variance about serotonin transporter changes in the organization of the metabolism and distribution. We use the variance to find the relation and compare the score using Hamilton Depression Rating Scale by experienced psychiatrists. After the test case, measuring the size distribution of serotonin transporter to depression patients can do a preliminary classification.

Optimal Imaging Protocols for Lung Cancer Staging: CT, PET, MR Imaging, and the Role of Imaging

MR and CT imaging of intraabdominal metastatic spread of colorectal cancer

The purpose was to assess the clinical utility of diagnostic tests for identifying malignancy within a solitary pulmonary nodule (SPN), and to create a nomogram or "look-up" table using clinical data and non-invasive radiology (positive) test results to estimate post-test probability of malignancy. Studies that examined computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) and single photon emission computed tomography (SPECT) for the evaluation of SPN. Two reviewers independently abstracted data and assessed study quality. Study-specific and overall positive likelihood ratios (LRs) for each diagnostic test confirming a diagnosis of malignancy and negative LR for each diagnostic test excluding a diagnosis of malignancy within an SPN were calculated. Forty-four of 242 articles were included. Positive LRs for diagnostic tests were: CT 3.91 (95% confidence interval 2.42, 5.40), MRI 4.57 (3.03, 6.1), PET 5.44 (3.56, 7.32) and SPECT 5.16 (4.03, 6.30). Negative LRs were: CT 0.10 (0.03, 0.16), MRI 0.08 (0.03, 0.12), PET 0.06 (0.02, 0.09) and SPECT 0.06 (0.04, 0.08). Differences in performance for all tests were negligible; therefore, the clinician may confidently use any of the four tests presented in further evaluating an SPN. Given the low cost and prevalence of the technology, SPECT appears to be the leading choice for additional testing in SPN evaluation.

By differentiating potentially treatable causes, structural imaging with either computed tomography (CT) or magnetic resonance imaging (MRI) influences patient management during the initial evaluation of dementia (strong evidence).No evidence exists on the choice of either CT or MRI for the initial evaluation of dementia (insufficient evidence).Diagnostic accuracy of positron emission tomography (PET) and single photon emission computed tomography (SPECT) to distinguish patients with Alzheimer disease (AD) from normal is not higher than that for clinical evaluation (moderate evidence).Hippocampal atrophy on MRI-based volumetry and regional decrease in cerebral perfusion on SPECT correlates with the pathologic stage in AD (moderate evidence).PET, SPECT, and dynamic susceptibility contrast-enhanced MRI are not cost-effective for the diagnostic workup of AD with the assumed minimal effectiveness of the drug donepezil hydrochloride (moderate evidence).Use of PET in early dementia can increase the accuracy of clinical diagnosis without adding to the overall costs of the evaluation (moderate evidence).Longitudinal decrease in MRI-based hippocampal volumes, N-acetylaspartate (NAA) levels on 1H magnetic resonance spectroscopy (MRS), glucose metabolism on PET, and cerebral blood flow on SPECT is associated with the rate of cognitive decline in patients with AD (moderate evidence).The validity of imaging techniques as surrogate markers for therapeutic efficacy in AD has not been tested in a positive disease-modifying drug trial (insufficient evidence).

Electrical brain imaging (EBI) refers to a set of techniques that exploit either the spontaneous electrical activity of the central nervous system, as in electroencephalographic (EEG) source reconstruction, or make use of external current injections, as in electrical impedance tomography (EIT) , to image the structure or function of the brain. When compared to other brain imaging methods used in research or in the clinical setting, such as computed tomography (CT), magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and single photon emission computed tomography (SPECT), EIT and EEG source localization instrumentation offer the advantages of portability, low cost, high temporal resolution [ms] and quick setup. The downsides are a low spatial resolution [cm], high computational cost of the image reconstruction process and high sensitivity to imperfections of the electrical model of the head.&#13;\nIn this work, a new special purpose reconstruction algorithm for EIT is presented and validated wth experimental measurements performed on a cylindrical phantom and on a simulated human head. The algorithm focuses on the quick detection of compact conductivity contrasts in imperfectly known in 3D domains. The performance of the proposed algorithm is then compared to the one of a benchmark reconstruction method in the EIT field, Tikhonov regularized reconstruction, with stroke detection and classification as a case study. Moreover, the possible application of EIT imaging to the detection of epileptic foci with intracranial deep electrodes (stereoelectroencephalography or SEEG) is explored.&#13;\nFinally, EEG source reconstruction algorithms are implemented on a heterogeneous multi-CPU and multi-GPU computing system to significantly reduce the reconstruction time.

Neuroimaging examines the relationship between abnormalities of brain function in epilepsy patients (seizures, impaired cognitive function, psychiatric co-morbidity etc.) and focal or more widespread brain pathology. Since the mid-1980s, the introduction of magnetic resonance imaging (MRI) into clinical neurology has had an impact on the diagnosis, treatment, and research of epilepsy only comparable with the advent of the electroencephalography (EEG) fifty years ago. MRI plays the important role of identifying single or multiple structural lesions responsible for the epileptic seizures. Thus, visual assessment of MRI plays an important role in the differentiation between symptomatic, cryptogenic, and idiopathic epilepsies. This diagnostic step leads to therapeutic decisions (medical treatment vs. surgery) and prognostic evaluations. If a structural lesion identified with MRI correlates with seizure-type, EEG and other clinical data, the likelihood of rendering the patient seizure free with epilepsy surgery is increased. Clinical research into epilepsy uses quantitative MRI (volumetry, T2-relaxometry, magnetic resonance spectroscopy [MRS], voxel-based morphometry) to reduce those cases initially labeled as cryptogenic. Quantitative MRI questions the belief that there is epilepsy without structural brain abnormality at all. Functional MRI (fMRI), positron emission tomography (PET) and single photon emission computed tomography (SPECT) demonstrate changes associated with epileptic seizures and pathology, and changes associated with EEG abnormalities and their cessation. Functional neuroimaging is also used for the identification of functional brain tissue before surgery. Physiologically or pathologically active neuronal tissue is believed to be identified by glucose or oxygen consumption (PET), cerebral blood flow (PET, SPECT, perfusion MRI), and cerebral blood oxygenation (blood oxygenation level dependent [BOLD] fMRI). PET also offers the opportunity to visualize the in-vivo distribution of neuronal receptors which are implicated in the generation, the spread, and the cessation of seizures.

Establishing the in vivo diagnosis of Alzheimer’s disease (AD) or other dementias relies on clinical criteria; however, the accuracy of these criteria can be limited. The diagnostic accuracy is 77% for a clinical diagnosis of AD, even among experts. We performed a review through PubMed of articles related to specific diagnostic modalities, including APOE genotyping, cerebrospinal fluid (CSF) testing, fludeoxyglucose F 18 positron emission tomography (PET), amyloid PET, tau PET, computed tomography (CT), single-photon emission CT, magnetic resonance imaging (MRI), and B12 and thyroid-stimulating hormone screening, to determine the specificity and sensitivity of each test used in the clinical diagnosis of AD. We added a novel immunomagnetic reduction assay that provides ultrasensitivity for analyzing the levels of plasma tau and beta amyloid 42 (Aβ42). The sensitivity and specificity of the current diagnostic approach (structural CT or MRI with screening labs) remain low for clinical detection of AD and are primarily used to exclude other conditions. Because of limited diagnostic capabilities, physicians do not feel comfortable or skilled in rendering a clinical diagnosis of AD. Compounding this problem is the fact that inexpensive, minimally invasive diagnostic tests do not yet exist. Biomarkers (obtained through CSF testing or PET imaging), which are not routinely incorporated in clinical practice, correlate well with pathologic changes. While PET is particularly costly and difficult to assess, CSF measures of tau and beta amyloid are not costly, and these tests may be worthwhile when the tiered approach proposed here warrants further testing. There is a need for developing bloodborne biomarkers that can aid in the clinical diagnosis of AD. Here we present a streamlined questionnaire-enriched, biomarker-enriched approach that is more cost-effective than the current diagnosis of exclusion and is designed to increase clinical confidence for a diagnosis of dementia due to AD.