Tract-wise microstructural analysis informs on current and future disability in early multiple sclerosis.

OBJECTIVES: Microstructural characterization of patients with multiple sclerosis (MS) has been shown to correlate better with disability compared to conventional radiological biomarkers. Quantitative MRI provides effective means to characterize microstructural brain tissue changes both in lesions and normal-appearing brain tissue. However, the impact of the location of microstructural alterations in terms of neuronal pathways has not been thoroughly explored so far. Here, we study the extent and the location of tissue changes probed using quantitative MRI along white matter (WM) tracts extracted from a connectivity atlas. METHODS: We quantified voxel-wise T1 tissue alterations compared to normative values in a cohort of 99 MS patients. For each WM tract, we extracted metrics reflecting tissue alterations both in lesions and normal-appearing WM and correlated these with cross-sectional disability and disability evolution after 2 years. RESULTS: In early MS patients, T1 alterations in normal-appearing WM correlated better with disability evolution compared to cross-sectional disability. Further, the presence of lesions in supratentorial tracts was more strongly associated with cross-sectional disability, while microstructural alterations in infratentorial pathways yielded higher correlations with disability evolution. In progressive patients, all major WM pathways contributed similarly to explaining disability, and correlations with disability evolution were generally poor. CONCLUSIONS: We showed that microstructural changes evaluated in specific WM pathways contribute to explaining future disability in early MS, hence highlighting the potential of tract-wise analyses in monitoring disease progression. Further, the proposed technique allows to estimate WM tract-specific microstructural characteristics in clinically compatible acquisition times, without the need for advanced diffusion imaging.

Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET-MRI data.

In oncology, intratumoural heterogeneity is closely linked with the efficacy of therapy, and can be partially characterized via tumour biopsies. Here we show that intratumoural heterogeneity can be characterized spatially via phenotype-specific, multi-view learning classifiers trained with data from dynamic positron emission tomography (PET) and multiparametric magnetic resonance imaging (MRI). Classifiers trained with PET-MRI data from mice with subcutaneous colon cancer quantified phenotypic changes resulting from an apoptosis-inducing targeted therapeutic and provided biologically relevant probability maps of tumour-tissue subtypes. When applied to retrospective PET-MRI data of patients with liver metastases from colorectal cancer, the trained classifiers characterized intratumoural tissue subregions in agreement with tumour histology. The spatial characterization of intratumoural heterogeneity in mice and patients via multimodal, multiparametric imaging aided by machine-learning may facilitate applications in precision oncology.

NEMA NU 4-2008 performance evaluation and MR compatibility tests of an APD-based small animal PET-insert for simultaneous PET/MR imaging.

An avalanche photodiode (APD)-based small animal positron emission tomography (PET)-insert was fully evaluated for its PET performance, as well as potential influences on magnetic resonance imaging (MRI) performance. This PET-insert has an extended axial field of view (FOV) compared with the previous design to increase system sensitivity, as well as an updated cooling and temperature regulation to enable stable and reproducible PET acquisitions. The PET performance was evaluated according to the National Electrical Manufacturers Association NU4-2008 protocol. The energy and timing resolution's full width at half maximum were 16.1% and 4.7 ns, respectively. The reconstructed radial spatial resolution of the PET-insert was 1.8 mm full width at half maximum at the center FOV using filtered back projection for reconstruction and sensitivity was 3.68%. The peak noise equivalent count rates were 70 kcps for a rat-like and 350 kcps for a mouse-like phantom, respectively. Image quality phantom values and contrast recovery were comparable to state-of-the art PET-inserts and standalone systems. Regarding MR compatibility, changes in the mean signal-to-noise ratio for turbo spin echo and echo-planar imaging sequences were below 8.6%, for gradient echo sequences below 1%. Degradation of the mean homogeneity was below 2.3% for all tested sequences. The influence of the PET-insert on theB0maps was negligible and no influence on functional MRI sequences was detected. A mouse and rat imaging study demonstrated the feasibility ofin vivosimultaneous PET/MRI.

A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate: Results of a Multireader, Multicase Study.

OBJECTIVE: The aim of this study was to evaluate the effect of a deep learning based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans. MATERIALS AND METHODS: We selected 100 consecutive prostate magnetic resonance imaging cases from a publicly available data set (PROSTATEx Challenge) with and without histopathologically confirmed prostate cancer. Seven board-certified radiologists were tasked to read each case twice in 2 reading blocks (with and without the assistance of a DL-CAD), with a separation between the 2 reading sessions of at least 2 weeks. Reading tasks were to localize and classify lesions according to Prostate Imaging Reporting and Data System (PI-RADS) v2.0 and to assign a radiologist's level of suspicion score (scale from 1-5 in 0.5 increments; 1, benign; 5, malignant). Ground truth was established by consensus readings of 3 experienced radiologists. The detection performance (receiver operating characteristic curves), variability (Fleiss κ), and average reading time without DL-CAD assistance were evaluated. RESULTS: The average accuracy of radiologists in terms of area under the curve in detecting clinically significant cases (PI-RADS ≥4) was 0.84 (95% confidence interval [CI], 0.79-0.89), whereas the same using DL-CAD was 0.88 (95% CI, 0.83-0.94) with an improvement of 4.4% (95% CI, 1.1%-7.7%; P = 0.010). Interreader concordance (in terms of Fleiss κ) increased from 0.22 to 0.36 (P = 0.003). Accuracy of radiologists in detecting cases with PI-RADS ≥3 was improved by 2.9% (P = 0.10). The median reading time in the unaided/aided scenario was reduced by 21% from 103 to 81 seconds (P < 0.001). CONCLUSIONS: Using a DL-CAD system increased the diagnostic accuracy in detecting highly suspicious prostate lesions and reduced both the interreader variability and the reading time.

Machine learning identifies stroke features between species.

Identification and localization of ischemic stroke (IS) lesions is routinely performed to confirm diagnosis, assess stroke severity, predict disability and plan rehabilitation strategies using magnetic resonance imaging (MRI). In basic research, stroke lesion segmentation is necessary to study complex peri-infarction tissue changes. Moreover, final stroke volume is a critical outcome evaluated in clinical and preclinical experiments to determine therapy or intervention success. Manual segmentations are performed but they require a specialized skill set, are prone to inter-observer variation, are not entirely objective and are often not supported by histology. The task is even more challenging when dealing with large multi-center datasets, multiple experimenters or large animal cohorts. On the other hand, current automatized segmentation approaches often lack histological validation, are not entirely user independent, are often based on single parameters, or in the case of complex machine learning methods, require vast training datasets and are prone to a lack of model interpretation. Methods: We induced IS using the middle cerebral artery occlusion model on two rat cohorts. We acquired apparent diffusion coefficient (ADC) and T2-weighted (T2W) images at 24 h and 1-week after IS induction. Subsets of the animals at 24 h and 1-week post IS were evaluated using histology and immunohistochemistry. Using a Gaussian mixture model, we segmented voxel-wise interactions between ADC and T2W parameters at 24 h using one of the rat cohorts. We then used these segmentation results to train a random forest classifier, which we applied to the second rat cohort. The algorithms' stroke segmentations were compared to manual stroke delineations, T2W and ADC thresholding methods and the final stroke segmentation at 1-week. Volume correlations to histology were also performed for every segmentation method. Metrics of success were calculated with respect to the final stroke volume. Finally, the trained random forest classifier was tested on a human dataset with a similar temporal stroke on-set. Manual segmentations, ADC and T2W thresholds were again used to evaluate and perform comparisons with the proposed algorithms' output. Results: In preclinical rat data our framework significantly outperformed commonly applied automatized thresholding approaches and segmented stroke regions similarly to manual delineation. The framework predicted the localization of final stroke regions in 1-week post-stroke MRI with a median Dice similarity coefficient of 0.86, Matthew's correlation coefficient of 0.80 and false positive rate of 0.04. The predicted stroke volumes also strongly correlated with final histological stroke regions (Pearson correlation = 0.88, P < 0.0001). Lastly, the stroke region characteristics identified by our framework in rats also identified stroke lesions in human brains, largely outperforming thresholding approaches in stroke volume prediction (P<0.01). Conclusion: Our findings reveal that the segmentation produced by our proposed framework using 24 h MRI rat data strongly correlated with the final stroke volume, denoting a predictive effect. In addition, we show for the first time that the stroke imaging features can be directly translated between species, allowing identification of acute stroke in humans using the model trained on animal data. This discovery reduces the gap between the clinical and preclinical fields, unveiling a novel approach to directly co-analyze clinical and preclinical data. Such methods can provide further biological insights into human stroke and highlight the differences between species in order to help improve the experimental setups and animal models of the disease.

Reproducibility and Comparability of Preclinical PET Imaging Data: A Multicenter Small-Animal PET Study.

The standardization of preclinical imaging is a key factor to ensure the reliability, reproducibility, validity, and translatability of preclinical data. Preclinical standardization has been slowly progressing in recent years and has mainly been performed within a single institution, whereas little has been done in regards to multicenter standardization between facilities. This study aimed to investigate the comparability among preclinical imaging facilities in terms of PET data acquisition and analysis. In the first step, basic PET scans were obtained in 4 different preclinical imaging facilities to compare their standard imaging protocol for 18F-FDG. In the second step, the influence of the personnel performing the experiments and the experimental equipment used in the experiment were compared. In the third step, the influence of the image analysis on the reproducibility and comparability of the acquired data was determined. Distinct differences in the uptake behavior of the 4 standard imaging protocols were determined for the investigated organs (brain, left ventricle, liver, and muscle) due to different animal handling procedures before and during the scans (e.g., fasting vs. nonfasting, glucose levels, temperature regulation vs. constant temperature warming). Significant differences in the uptake behavior in the brain were detected when the same imaging protocol was used but executed by different personnel and using different experimental animal handling equipment. An influence of the person analyzing the data was detected for most of the organs, when the volumes of interest were manually drawn by the investigators. Coregistration of the PET to an MR image and drawing the volume of interest based on anatomic information yielded reproducible results among investigators. It has been demonstrated that there is a huge demand for standardization among multiple institutions.

Oxygen-Enhanced and Dynamic Contrast-Enhanced Optoacoustic Tomography Provide Surrogate Biomarkers of Tumor Vascular Function, Hypoxia, and Necrosis.

Measuring the functional status of tumor vasculature, including blood flow fluctuations and changes in oxygenation, is important in cancer staging and therapy monitoring. Current clinically approved imaging modalities suffer long procedure times and limited spatiotemporal resolution. Optoacoustic tomography (OT) is an emerging clinical imaging modality that may overcome these challenges. By acquiring data at multiple wavelengths, OT can interrogate hemoglobin concentration and oxygenation directly and resolve contributions from injected contrast agents. In this study, we tested whether two dynamic OT techniques, oxygen-enhanced (OE) and dynamic contrast-enhanced (DCE)-OT, could provide surrogate biomarkers of tumor vascular function, hypoxia, and necrosis. We found that vascular maturity led to changes in vascular function that affected tumor perfusion, modulating the DCE-OT signal. Perfusion in turn regulated oxygen availability, driving the OE-OT signal. In particular, we demonstrate for the first time a strong per-tumor and spatial correlation between imaging biomarkers derived from these in vivo techniques and tumor hypoxia quantified ex vivo Our findings indicate that OT may offer a significant advantage for localized imaging of tumor response to vascular-targeted therapies when compared with existing clinical DCE methods.Significance: Imaging biomarkers derived from optoacoustic tomography can be used as surrogate measures of tumor perfusion and hypoxia, potentially yielding rapid, multiparametric, and noninvasive cancer staging and therapeutic response monitoring in the clinic.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/78/20/5980/F1.large.jpg Cancer Res; 78(20); 5980-91. ©2018 AACR.

PET/MRI Hybrid Systems.

Over the last decade, the combination of PET and MRI in one system has proven to be highly successful in basic preclinical research, as well as in clinical research. Nowadays, PET/MRI systems are well established in preclinical imaging and are progressing into clinical applications to provide further insights into specific diseases, therapeutic assessments, and biological pathways. Certain challenges in terms of hardware had to be resolved concurrently with the development of new techniques to be able to reach the full potential of both combined techniques. This review provides an overview of these challenges and describes the opportunities that simultaneous PET/MRI systems can exploit in comparison with stand-alone or other combined hybrid systems. New approaches were developed for simultaneous PET/MRI systems to correct for attenuation of 511 keV photons because MRI does not provide direct information on gamma photon attenuation properties. Furthermore, new algorithms to correct for motion were developed, because MRI can accurately detect motion with high temporal resolution. The additional information gained by the MRI can be employed to correct for partial volume effects as well. The development of new detector designs in combination with fast-decaying scintillator crystal materials enabled time-of-flight detection and incorporation in the reconstruction algorithms. Furthermore, this review lists the currently commercially available systems both for preclinical and clinical imaging and provides an overview of applications in both fields. In this regard, special emphasis has been placed on data analysis and the potential for both modalities to evolve with advanced image analysis tools, such as cluster analysis and machine learning.

Linking imaging to omics utilizing image-guided tissue extraction.

Phenotypic heterogeneity is commonly observed in diseased tissue, specifically in tumors. Multimodal imaging technologies can reveal tissue heterogeneity noninvasively in vivo, enabling imaging-based profiling of receptors, metabolism, morphology, or function on a macroscopic scale. In contrast, in vitro multiomics, immunohistochemistry, or histology techniques accurately characterize these heterogeneities in the cellular and subcellular scales in a more comprehensive but ex vivo manner. The complementary in vivo and ex vivo information would provide an enormous potential to better characterize a disease. However, this requires spatially accurate coregistration of these data by image-driven sampling as well as fast sample-preparation methods. Here, a unique image-guided milling machine and workflow for precise extraction of tissue samples from small laboratory animals or excised organs has been developed and evaluated. The samples can be delineated on tomographic images as volumes of interest and can be extracted with a spatial accuracy better than 0.25 mm. The samples remain cooled throughout the procedure to ensure metabolic stability, a precondition for accurate in vitro analysis.

A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation.

PURPOSE: We aimed to precisely estimate intra-tumoral heterogeneity using spatially regularized spectral clustering (SRSC) on multiparametric MRI data and compare the efficacy of SRSC with the previously reported segmentation techniques in MRI studies. PROCEDURES: Six NMRI nu/nu mice bearing subcutaneous human glioblastoma U87 MG tumors were scanned using a dedicated small animal 7T magnetic resonance imaging (MRI) scanner. The data consisted of T2 weighted images, apparent diffusion coefficient maps, and pre- and post-contrast T2 and T2* maps. Following each scan, the tumors were excised into 2-3-mm thin slices parallel to the axial field of view and processed for histological staining. The MRI data were segmented using SRSC, K-means, fuzzy C-means, and Gaussian mixture modeling to estimate the fractional population of necrotic, peri-necrotic, and viable regions and validated with the fractional population obtained from histology. RESULTS: While the aforementioned methods overestimated peri-necrotic and underestimated viable fractions, SRSC accurately predicted the fractional population of all three tumor tissue types and exhibited strong correlations (rnecrotic = 0.92, rperi-necrotic = 0.82 and rviable = 0.98) with the histology. CONCLUSIONS: The precise identification of necrotic, peri-necrotic and viable areas using SRSC may greatly assist in cancer treatment planning and add a new dimension to MRI-guided tumor biopsy procedures.

Spectral Clustering Predicts Tumor Tissue Heterogeneity Using Dynamic 18F-FDG PET: A Complement to the Standard Compartmental Modeling Approach.

In this study, we described and validated an unsupervised segmentation algorithm for the assessment of tumor heterogeneity using dynamic 18F-FDG PET. The aim of our study was to objectively evaluate the proposed method and make comparisons with compartmental modeling parametric maps and SUV segmentations using simulations of clinically relevant tumor tissue types. Methods: An irreversible 2-tissue-compartmental model was implemented to simulate clinical and preclinical 18F-FDG PET time-activity curves using population-based arterial input functions (80 clinical and 12 preclinical) and the kinetic parameter values of 3 tumor tissue types. The simulated time-activity curves were corrupted with different levels of noise and used to calculate the tissue-type misclassification errors of spectral clustering (SC), parametric maps, and SUV segmentation. The utility of the inverse noise variance- and Laplacian score-derived frame weighting schemes before SC was also investigated. Finally, the SC scheme with the best results was tested on a dynamic 18F-FDG measurement of a mouse bearing subcutaneous colon cancer and validated using histology. Results: In the preclinical setup, the inverse noise variance-weighted SC exhibited the lowest misclassification errors (8.09%-28.53%) at all noise levels in contrast to the Laplacian score-weighted SC (16.12%-31.23%), unweighted SC (25.73%-40.03%), parametric maps (28.02%-61.45%), and SUV (45.49%-45.63%) segmentation. The classification efficacy of both weighted SC schemes in the clinical case was comparable to the unweighted SC. When applied to the dynamic 18F-FDG measurement of colon cancer, the proposed algorithm accurately identified densely vascularized regions from the rest of the tumor. In addition, the segmented regions and clusterwise average time-activity curves showed excellent correlation with the tumor histology. Conclusion: The promising results of SC mark its position as a robust tool for quantification of tumor heterogeneity using dynamic PET studies. Because SC tumor segmentation is based on the intrinsic structure of the underlying data, it can be easily applied to other cancer types as well.

Comparison of Tumor Uptake Heterogeneity Characterization Between Static and Parametric 18F-FDG PET Images in Non-Small Cell Lung Cancer.

UNLABELLED: (18)F-FDG PET is well established in the field of oncology for diagnosis and staging purposes and is increasingly being used to assess therapeutic response and prognosis. Many quantitative indices can be used to characterize tumors on (18)F-FDG PET images, such as SUVmax, metabolically active tumor volume (MATV), total lesion glycolysis, and, more recently, the proposed intratumor uptake heterogeneity features. Although most PET data considered within this context concern the analysis of activity distribution using images obtained from a single static acquisition, parametric images generated from dynamic acquisitions and reflecting radiotracer kinetics may provide additional information. The purpose of this study was to quantify differences between volumetry, uptake, and heterogeneity features extracted from static and parametric PET images of non-small cell lung carcinoma (NSCLC) in order to provide insight on the potential added value of parametric images. METHODS: Dynamic (18)F-FDG PET/CT was performed on 20 therapy-naive NSCLC patients for whom primary surgical resection was planned. Both static and parametric PET images were analyzed, with quantitative parameters (MATV, SUVmax, SUVmean, heterogeneity) being extracted from the segmented tumors. Differences were investigated using Spearman rank correlation and Bland-Altman analysis. RESULTS: MATV was slightly smaller on static images (-2% ± 7%), but the difference was not significant (P = 0.14). All derived parameters, including those characterizing tumor functional heterogeneity, correlated strongly between static and parametric images (r = 0.70-0.98, P ≤ 0.0006), exhibiting differences of less than ±25%. CONCLUSION: In NSCLC primary tumors, parametric and static baseline (18)F-FDG PET images provided strongly correlated quantitative features for both standard (MATV, SUVmax, SUVmean) and heterogeneity quantification. Consequently, heterogeneity quantification on parametric images does not seem to provide significant complementary information compared with static SUV images.

A Population-Based Gaussian Mixture Model Incorporating 18F-FDG PET and Diffusion-Weighted MRI Quantifies Tumor Tissue Classes.

UNLABELLED: The aim of our study was to create a novel Gaussian mixture modeling (GMM) pipeline to model the complementary information derived from(18)F-FDG PET and diffusion-weighted MRI (DW-MRI) to separate the tumor microenvironment into relevant tissue compartments and follow the development of these compartments longitudinally. METHODS: Serial (18)F-FDG PET and apparent diffusion coefficient (ADC) maps derived from DW-MR images of NCI-H460 xenograft tumors were coregistered, and a population-based GMM was implemented on the complementary imaging data. The tumor microenvironment was segmented into 3 distinct regions and correlated with histology. ANCOVA was applied to gauge how well the total tumor volume was a predictor for the ADC and (18)F-FDG, or if ADC was a good predictor of (18)F-FDG for average values in the whole tumor or average necrotic and viable tissues. RESULTS: The coregistered PET/MR images were in excellent agreement with histology, both visually and quantitatively, and allowed for validation of the last-time-point measurements. Strong correlations were found for the necrotic (r = 0.88) and viable fractions (r = 0.87) between histology and clustering. The GMM provided probabilities for each compartment with uncertainties expressed as a mixture of tissues in which the resolution of scans was inadequate to accurately separate tissues. The ANCOVA suggested that both ADC and (18)F-FDG in the whole tumor (P = 0.0009, P = 0.02) as well as necrotic (P = 0.008, P = 0.02) and viable (P = 0.003, P = 0.01) tissues were a positive, linear function of total tumor volume. ADC proved to be a positive predictor of (18)F-FDG in the whole tumor (P = 0.001) and necrotic (P = 0.02) and viable (P = 0.0001) tissues. CONCLUSION: The complementary information of (18)F-FDG and ADC longitudinal measurements in xenograft tumors allows for segmentation into distinct tissues when using the novel GMM pipeline. Leveraging the power of multiparametric PET/MRI in this manner has the potential to take the assessment of disease outcome beyond RECIST and could provide an important impact to the field of precision medicine.

Principles of PET/MR Imaging.

Hybrid PET/MR systems have rapidly progressed from the prototype stage to systems that are increasingly being used in the clinics. This review provides an overview of developments in hybrid PET/MR systems and summarizes the current state of the art in PET/MR instrumentation, correction techniques, and data analysis. The strong magnetic field requires considerable changes in the manner by which PET images are acquired and has led, among others, to the development of new PET detectors, such as silicon photomultipliers. During more than a decade of active PET/MR development, several system designs have been described. The technical background of combined PET/MR systems is explained and related challenges are discussed. The necessity for PET attenuation correction required new methods based on MR data. Therefore, an overview of recent developments in this field is provided. Furthermore, MR-based motion correction techniques for PET are discussed, as integrated PET/MR systems provide a platform for measuring motion with high temporal resolution without additional instrumentation. The MR component in PET/MR systems can provide functional information about disease processes or brain function alongside anatomic images. Against this background, we point out new opportunities for data analysis in this new field of multimodal molecular imaging.

Quantitative assessment of heterogeneity in tumor metabolism using FDG-PET.

PURPOSE: [(18)F]-fluorodeoxyglucose-positron emission tomography (FDG-PET) images are usually quantitatively analyzed in "whole-tumor" volumes of interest. Also parameters determined with dynamic PET acquisitions, such as the Patlak glucose metabolic rate (MR(glc)) and pharmacokinetic rate constants of two-tissue compartment modeling, are most often derived per lesion. We propose segmentation of tumors to determine tumor heterogeneity, potentially useful for dose-painting in radiotherapy and elucidating mechanisms of FDG uptake. METHODS AND MATERIALS: In 41 patients with 104 lesions, dynamic FDG-PET was performed. On MR(glc) images, tumors were segmented in quartiles of background subtracted maximum MR(glc) (0%-25%, 25%-50%, 50%-75%, and 75%-100%). Pharmacokinetic analysis was performed using an irreversible two-tissue compartment model in the three segments with highest MR(glc) to determine the rate constants of FDG metabolism. RESULTS: From the highest to the lowest quartile, significant decreases of uptake (K(1)), washout (k(2)), and phosphorylation (k(3)) rate constants were seen with significant increases in tissue blood volume fraction (V(b)). CONCLUSIONS: Tumor regions with highest MR(glc) are characterized by high cellular uptake and phosphorylation rate constants with relatively low blood volume fractions. In regions with less metabolic activity, the blood volume fraction increases and cellular uptake, washout, and phosphorylation rate constants decrease. These results support the hypothesis that regional tumor glucose phosphorylation rate is not dependent on the transport of nutrients (i.e., FDG) to the tumor.

Using the NEMA NU 4 PET image quality phantom in multipinhole small-animal SPECT.

UNLABELLED: Several commercial small-animal SPECT scanners using multipinhole collimation are presently available. However, generally accepted standards to characterize the performance of these scanners do not exist. Whereas for small-animal PET, the National Electrical Manufacturers Association (NEMA) NU 4 standards have been defined in 2008, such standards are still lacking for small-animal SPECT. In this study, the image quality parameters associated with the NEMA NU 4 image quality phantom were determined for a small-animal multipinhole SPECT scanner. METHODS: Multiple whole-body scans of the NEMA NU 4 image quality phantom of 1-h duration were performed in a U-SPECT-II scanner using (99m)Tc with activities ranging between 8.4 and 78.2 MBq. The collimator contained 75 pinholes of 1.0-mm diameter and had a bore diameter of 98 mm. Image quality parameters were determined as a function of average phantom activity, number of iterations, postreconstruction spatial filter, and scatter correction. In addition, a mouse was injected with (99m)Tc-hydroxymethylene diphosphonate and was euthanized 6.5 h after injection. Multiple whole-body scans of this mouse of 1-h duration were acquired for activities ranging between 3.29 and 52.7 MBq. RESULTS: An increase in the number of iterations was accompanied by an increase in the recovery coefficients for the small rods (RC(rod)), an increase in the noise in the uniform phantom region, and a decrease in spillover ratios for the cold-air- and water-filled scatter compartments (SOR(air) and SOR(wat)). Application of spatial filtering reduced image noise but lowered RC(rod). Filtering did not influence SOR(air) and SOR(wat). Scatter correction reduced SOR(air) and SOR(wat). The effect of total phantom activity was primarily seen in a reduction of image noise with increasing activity. RC(rod), SOR(air), and SOR(wat) were more or less constant as a function of phantom activity. The relation between acquisition and reconstruction settings and image quality was confirmed in the (99m)Tc-hydroxymethylene diphosphonate mouse scans. CONCLUSION: Although developed for small-animal PET, the NEMA NU 4 image quality phantom was found to be useful for small-animal SPECT as well, allowing for objective determination of image quality parameters and showing the trade-offs between several of these parameters on variation of acquisition and reconstruction settings.

Endothelial shear stress: a critical determinant of arterial remodeling and arterial stiffness in humans--a carotid 3.0-T MRI study.

BACKGROUND: Low endothelial shear stress (ESS) elicits endothelial dysfunction. However, the relationship between ESS and arterial remodeling and arterial stiffness is unknown in humans. We developed a 3.0-T MRI protocol to evaluate the contribution of ESS to arterial remodeling and stiffness. METHODS AND RESULTS: Fifteen young (aged 26 ± 3 years) and 15 older (aged 57 ± 3 years) healthy volunteers as well as 15 patients with cardiovascular disease (aged 63 ± 10 years) were enrolled. Phase-contrast MRI of the common carotid arteries was used to derive ESS data from the spatial velocity gradients close to the arterial wall. ESS measurements were performed on 3 occasions and showed excellent reproducibility (intraclass correlation coefficient, 0.79). Multiple linear regression analysis accounting for age and blood pressure revealed that ESS was an independent predictor of the following response variables: carotid wall thickness (regression coefficient [b], -0.19 mm(2) per N/m(2); P=0.02), lumen area (b, -15.5 mm(2) per N/m(2); P<0.001), and vessel size (b, -24.0 mm(2) per N/m(2); P<0.001). Segments of the artery wall exposed to lower ESS were significantly thicker than segments exposed to higher ESS within the same artery (P=0.009). Furthermore, ESS was associated with arterial compliance, accounting for age, blood pressure, and wall thickness (b, -0.003 mm(2)/mm Hg per N/m(2); P=0.04). CONCLUSIONS: Our carotid MRI data show that ESS is an important determinant of arterial remodeling and arterial stiffness in humans. The data warrant further studies to evaluate use of carotid ESS as a noninvasive tool to improve the understanding of individual cardiovascular disease risk and to assess novel drug therapies in cardiovascular disease prevention.

Pretargeted immuno-positron emission tomography imaging of carcinoembryonic antigen-expressing tumors with a bispecific antibody and a 68Ga- and 18F-labeled hapten peptide in mice with human tumor xenografts.

(18)F-Fluorodeoxyglucose ((18)F-FDG) is the most common molecular imaging agent in oncology, with a high sensitivity and specificity for detecting several cancers. Antibodies could enhance specificity; therefore, procedures were developed for radiolabeling a small ( approximately 1451 Da) hapten peptide with (68)Ga or (18)F to compare their specificity with (18)F-FDG for detecting tumors using a pretargeting procedure. Mice were implanted with carcinoembryonic antigen (CEA; CEACAM5)-expressing LS174T human colonic tumors and a CEA-negative tumor, or an inflammation was induced in thigh muscle. A bispecific monoclonal anti-CEA x anti-hapten antibody was given to mice, and 16 hours later, 5 MBq of (68)Ga- or (18)F-labeled hapten peptides were administered intravenously. Within 1 hour, tissues showed high and specific targeting of (68)Ga-IMP-288, with 10.7 +/- 3.6% ID/g uptake in the tumor and very low uptake in normal tissues (e.g., tumor-to-blood ratio of 69.9 +/- 32.3), in a CEA-negative tumor (0.35 +/- 0.35% ID/g), and inflamed muscle (0.72 +/- 0.20% ID/g). (18)F-FDG localized efficiently in the tumor (7.42 +/- 0.20% ID/g) but also in the inflamed muscle (4.07 +/- 1.13% ID/g) and in several normal tissues; thus, pretargeted (68)Ga-IMP-288 provided better specificity and sensitivity. Positron emission tomography (PET)/computed tomography images reinforced the improved specificity of the pretargeting method. (18)F-labeled IMP-449 distributed similarly in the tumor and normal tissues as the (68)Ga-labeled IMP-288, indicating that either radiolabeled hapten peptide could be used. Thus, pretargeted immuno-PET does exceptionally well with short-lived radionuclides and is a highly sensitive procedure that is more specific than (18)F-FDG-PET. Mol Cancer Ther; 9(4); 1019-27. (c)2010 AACR.

Image-quality assessment for several positron emitters using the NEMA NU 4-2008 standards in the Siemens Inveon small-animal PET scanner.

UNLABELLED: The positron emitters (18)F, (68)Ga, (124)I, and (89)Zr are all relevant in small-animal PET. Each of these radionuclides has different positron energies and ranges and a different fraction of single photons emitted. Average positron ranges larger than the intrinsic spatial resolution of the scanner (for (124)I and (68)Ga) will deteriorate the effective spatial resolution and activity recovery coefficient (RC) for small lesions or phantom structures. The presence of single photons (for (124)I and (89)Zr) could increase image noise and spillover ratios (SORs). METHODS: Image noise, expressed as percentage SD in a uniform region (%SD), RC, and SOR (in air and water) were determined using the NEMA NU 4 small-animal image-quality phantom filled with 3.7 MBq of total activity of (18)F, (68)Ga, (124)I, or (89)Zr. Filtered backprojection (FBP), ordered-subset expectation maximization in 2 dimensions, and maximum a posteriori (MAP) reconstructions were compared. In addition to the NEMA NU 4 image-quality parameters, spatial resolutions were determined using small glass capillaries filled with these radionuclides in a water environment. RESULTS: The %SD for (18)F, (68)Ga, (124)I, and (89)Zr using FBP was 6.27, 6.40, 6.74, and 5.83, respectively. The respective RCs were 0.21, 0.11, 0.12, and 0.19 for the 1-mm-diameter rod and 0.97, 0.65, 0.64, and 0.88 for the 5-mm-diameter rod. SORs in air were 0.01, 0.03, 0.04, and 0.01, respectively, and in water 0.02, 0.10, 0.13, and 0.02. Other reconstruction algorithms gave similar differences between the radionuclides. MAP produced the highest RCs. For the glass capillaries using FBP, the full widths at half maximum for (18)F, (68)Ga, (124)I, and (89)Zr were 1.81, 2.46, 2.38, and 1.99 mm, respectively. The corresponding full widths at tenth maximum were 3.57, 6.52, 5.87, and 4.01 mm. CONCLUSION: With the intrinsic spatial resolution (approximately 1.5 mm) of this latest-generation small-animal PET scanner, the finite positron range has become the limiting factor for the overall spatial resolution and activity recovery in small structures imaged with (124)I and (68)Ga. The presence of single photons had only a limited effect on the image noise. MAP, as compared with the other reconstruction algorithms, increased RC and decreased %SD and SOR.