Biochemically recurrent prostate cancer: rationalisation of the approach to imaging.

AIM: To assess the utility of magnetic resonance imaging (MRI) in addition to the additive benefit of the conventional imaging techniques, computed tomography (CT) and nuclear medicine (NM) bone scintigraphy, for investigation of biochemical recurrence (BCR) post-prostatectomy where access to prostate specific membrane antigen (PSMA) positron-emission tomography (PET)-CT is challenging. MATERIALS AND METHODS: Relevant imaging over a 5-year period was reviewed. Ethical approval was granted by the internal review board. All patients with suspected BCR, defined as a PSA ≥0.2 ng/ml on two separate occasions, underwent a retrospective imaging review. This was performed on PACS archive search database in a single centre using search terms "PSA" and "prostatectomy" in the three imaging methods; MRI, CT, and NM bone scintigraphy. All PSMA PET CT performed were recorded. RESULTS: One hundred and eighty-five patients were identified. Patients with an MRI pelvis that demonstrated distant metastases (i.e., pelvic bone metastases or lymph node involvement more cranial to the bifurcation of the common iliac arteries) were more likely to have a positive CT and/or NM bone scintigraphy. The Pearson correlation coefficient between the findings of M1 disease at MRI pelvis and the presence of distant metastases at CT thorax, abdomen, pelvis and NM bone scintigraphy was calculated at 0.81 (p<0.01) and 0.91 (p<0.01) respectively. CONCLUSION: An imaging strategy based on risk stratification and technique-specific selection criteria leads to more appropriate use of resources, and in turn, increases the yield of conventional imaging methods. MRI prostate findings can be used to predict the additive value of CT/NM bone scintigraphy allowing a more streamlined approach to their use.

An optimized reference tissue method for quantification of tau protein depositions in diverse neurodegenerative disorders by PET with 18F-PM-PBB3 (18F-APN-1607).

Positron emission tomography (PET) with 18F-PM-PBB3 (18F-APN-1607, 18F-Florzolotau) enables high-contrast detection of tau depositions in various neurodegenerative dementias, including Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD). A simplified method for quantifying radioligand binding in target regions is to employ the cerebellum as a reference (CB-ref) on the assumption that the cerebellum has minimal tau pathologies. This procedure is typically valid in AD, while FTLD disorders exemplified by progressive supranuclear palsy (PSP) are characterized by occasional tau accumulations in the cerebellum, hampering the application of CB-ref. The present study aimed to establish an optimal method for defining reference tissues on 18F-PM-PBB3-PET images of AD and non-AD tauopathy brains. We developed a new algorithm to extract reference voxels with a low likelihood of containing tau deposits from gray matter (GM-ref) or white matter (WM-ref) by a bimodal fit to an individual, voxel-wise histogram of the radioligand retentions and applied it to 18F-PM-PBB3-PET data obtained from age-matched 40 healthy controls (HCs) and 23 CE, 40 PSP, and five other tau-positive FTLD patients. PET images acquired at 90-110 min after injection were averaged and co-registered to corresponding magnetic resonance imaging space. Subsequently, we generated standardized uptake value ratio (SUVR) images estimated by CB-ref, GM-ref and WM-ref, respectively, and then compared the diagnostic performances. GM-ref and WM-ref covered a broad area in HCs and were free of voxels located in regions known to bear high tau burdens in AD and PSP patients. However, radioligand retentions in WM-ref exhibited age-related declines. GM-ref was unaffected by aging and provided SUVR images with higher contrast than CB-ref in FTLD patients with suspected and confirmed corticobasal degeneration. The methodology for determining reference tissues as optimized here improves the accuracy of 18F-PM-PBB3-PET measurements of tau burdens in a wide range of neurodegenerative illnesses.

Source-to-Target Automatic Rotating Estimation (STARE) - A publicly-available, blood-free quantification approach for PET tracers with irreversible kinetics: Theoretical framework and validation for [18F]FDG.

INTRODUCTION: Full quantification of positron emission tomography (PET) data requires an input function. This generally means arterial blood sampling, which is invasive, labor-intensive and burdensome. There is no current, standardized method to fully quantify PET radiotracers with irreversible kinetics in the absence of blood data. Here, we present Source-to-Target Automatic Rotating Estimation (STARE), a novel, data-driven approach to quantify the net influx rate (Ki) of irreversible PET radiotracers, that requires only individual-level PET data and no blood data. We validate STARE with human [18F]FDG PET scans and assess its performance using simulations. METHODS: STARE builds upon a source-to-target tissue model, where the tracer time activity curves (TACs) in multiple "target" regions are expressed at once as a function of a "source" region, based on the two-tissue irreversible compartment model, and separates target region Ki from source Ki by fitting the source-to-target model across all target regions simultaneously. To ensure identifiability, data-driven, subject-specific anchoring is used in the STARE minimization, which takes advantage of the PET signal in a vasculature cluster in the field of view (FOV) that is automatically extracted and partial volume-corrected. To avoid the need for any a priori determination of a single source region, each of the considered regions acts in turn as the source, and a final Ki is estimated in each region by averaging the estimates obtained in each source rotation. RESULTS: In a large dataset of human [18F]FDG scans (N = 69), STARE Ki estimates were correlated with corresponding arterial blood-based Ki estimates (r = 0.80), with an overall regression slope of 0.88, and were precisely estimated, as assessed by comparing STARE Ki estimates across several runs of the algorithm (coefficient of variation across runs=6.74 ± 2.48%). In simulations, STARE Ki estimates were largely robust to factors that influence the individualized anchoring used within its algorithm. CONCLUSION: Through simulations and application to [18F]FDG PET data, feasibility is demonstrated for STARE blood-free, data-driven quantification of Ki. Future work will include applying STARE to PET data obtained with a portable PET camera and to other irreversible radiotracers.

18 F-fluorodeoxy-glucose positron emission tomography pattern and prognostic predictors in patients with anti-GABAB receptor encephalitis.

AIMS: To identify the metabolic pattern and prognostic predictors in anti-gamma-aminobutyric-acid B (GABAB) receptor encephalitis using 18 F-fluorodeoxy-glucose positron emission tomography (18 F-FDG-PET). METHODS: Twenty-one patients diagnosed anti-GABAB receptor encephalitis who underwent 18 F-FDG-PET at first hospitalization were retrospectively reviewed. 18 F-FDG-PET images were analyzed in comparison with controls. Further group comparisons of 18 F-FDG-PET data were carried out between prognostic subgroups. RESULTS: 18 F-FDG-PET was abnormal in 81% patients with anti-GABAB receptor encephalitis and was more sensitive than MRI (81% vs. 42.9%, p = 0.025). Alter limbic lobe glucose metabolism (mostly hypermetabolism) was observed in 14 patients (66.7%), of whom 10 (10/14, 71.4%) demonstrated hypermetabolism in the medial temporal lobe (MTL). Group analysis also confirmed MTL hypermetabolism in association with relative frontal and parietal hypometabolism was a general metabolic pattern. After a median follow-up of 33 months, the group comparisons revealed that patients with poor outcome demonstrated increased metabolism in the MTL compared to those with good outcome. CONCLUSION: 18 F-FDG-PET may be more sensitive than MRI in the early diagnosis of anti-GABAB receptor encephalitis. MTL hypermetabolism was associated with relative frontal or parietal hypometabolism and may serve as a prognostic biomarker in anti-GABAB receptor encephalitis.

11C-acetate positron emission tomography is more precise than 18F-fluorodeoxyglucose positron emission tomography in evaluating tumor burden and predicting disease risk of multiple myeloma.

The optimal method of tumor burden evaluation in newly diagnosed multiple myeloma (NDMM) is yet to be determined. This study aimed to compare the value of 11C-acetate positron-emission tomography (PET)/computed tomography (CT) (AC-PET and 18F-fluorodeoxyglucose PET/CT (FDG-PET) in the assessment of tumor burden in NDMM. This study evaluated 64 NDMM patients between February 2015 and July 2018. AC-PET and FDG-PET were used to assess myeloma lesions. The clinical data, imaging results, and their correlations were analyzed. Diffuse bone marrow uptake in AC-PET was significantly correlated with biomarkers for tumor burden, including serum hemoglobin (P = 0.020), M protein (P = 0.054), the percentage of bone marrow plasma cells (P < 0.001), and the Durie-Salmon stage of the disease (P = 0.007). The maximum standard uptake value (SUVmax) of focal lesions and high diffuse bone marrow uptake in AC-PET showed stronger correlations with high-risk disease (P = 0.017, P = 0.013) than those in FDG-PET. Moreover, the presence of diffuse bone marrow uptake, more than ten focal lesions, and an SUVmax of focal lesions of > 6.0 in AC-PET, but not in FDG-PET, predicted a higher probability of disease progression and shorter progression-free survival (P < 0.05). AC-PET outperformed FDG-PET in tumor burden evaluation and disease progression prediction in NDMM.

Modeling [11C]yohimbine PET human brain kinetics with test-retest reliability, competition sensitivity studies and search for a suitable reference region.

Previous work introduced the [11C]yohimbine as a suitable ligand of central α2-adrenoreceptors (α2-ARs) for PET imaging. However, reproducibility of [11C]yohimbine PET measurements in healthy humans estimated with a simplified modeling method with reference region, as well as sensitivity of [11C]yohimbine to noradrenergic competition were not evaluated. The objectives of the present study were therefore to fill this gap. METHODS: Thirteen healthy humans underwent two [11C]yohimbine 90-minute dynamic scans performed on a PET-MRI scanner. Seven had arterial blood sampling with metabolite assessment and plasmatic yohimbine free fraction evaluation at the first scan to have arterial input function and test appropriate kinetic modeling. The second scan was a simple retest for 6 subjects to evaluate the test-retest reproducibility. For the remaining 7 subjects the second scan was a challenge study with the administration of a single oral dose of 150 µg of clonidine 90 min before the PET scan. Parametric images of α2-ARs distribution volume ratios (DVR) were generated with two non-invasive models: Logan graphical analysis with Reference (LREF) and Simplified Reference Tissue Method (SRTM). Three reference regions (cerebellum white matter (CERWM), frontal white matter (FLWM), and corpus callosum (CC)) were tested. RESULTS: We showed high test-retest reproducibility of DVR estimation with LREF and SRTM regardless of reference region (CC, CERWM, FLWM). The best fit was obtained with SRTMCC (r2=0.94). Test-retest showed that the SRTMCC is highly reproducible (mean ICC>0.7), with a slight bias (-1.8%), whereas SRTMCERWM had lower bias (-0.1%), and excellent ICC (mean>0.8). Using SRTMCC, regional changes have been observed after clonidine administration with a significant increase reported in the amygdala and striatum as well as in several posterior cortical areas as revealed with the voxel-based analysis. CONCLUSION: The results add experimental support for the suitability of [11C]yohimbine PET in the quantitative assessment of α2-ARs occupancy in vivo in the human brain. Trial registration EudraCT 2018-000380-82.

Validation technique and improvements introduced in a new dedicated brain positron emission tomograph (CareMiBrain).

The goal of developing a PET dedicated to the brain (CareMiBrain) has evolved from its initial approach to diagnosis and monitoring of dementias, to the more ambitious of creating a revolutionary clinical pathway for the knowledge and personalized treatment of multiple neurological diseases. The main innovative feature of CareMiBrain is the use of detectors with continuous crystals, which allow a high resolution determination of the depth of annihilation photons interaction within the thickness of the scintillation crystal. The technical validation phase of the equipment consisted of a pilot, prospective and observational study whose objective was to obtain the first images (40 patients), analyze them and make adjustments in the acquisition, reconstruction and correction parameters, comparing the image quality of the CareMiBrain equipment with that of the whole-body PET/CT. Thanks to the team meetings and the joint analysis of the images, it was possible to detect its weak points and some of its causes. The calibration, acquisition and processing processes, as well as the reconstruction, were optimized, the number of iterations was set to achieve the best signal-to-noise ratio, the random correction was optimized and a post-processing algorithm was included in the reconstruction algorithm. The main technical improvements implemented in this phase of technical validation carried out through collaboration of the Services of Nuclear Medicine and Neurology of the Hospital Clínico San Carlos with the Spanish company Oncovision will be exposed in a project financed with funds from the European Union (Horizon 2020 innovation program, 713323).

A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets.

PURPOSE: To facilitate the demonstration of the prognostic value of radiomics, multicenter radiomics studies are needed. Pooling radiomic features of such data in a statistical analysis is however challenging, as they are sensitive to the variability in scanner models, acquisition protocols and reconstruction settings, which is often unavoidable in a multicentre retrospective analysis. A statistical harmonization strategy called ComBat was utilized in radiomics studies to deal with the "center-effect". The goal of the present work was to integrate a transfer learning (TL) technique within ComBat-and recently developed alternate versions of ComBat with improved flexibility (M-ComBat) and robustness (B-ComBat)-to allow the use of a previously determined harmonization transform to the radiomic feature values of new patients from an already known center. MATERIAL AND METHODS: The proposed TL approach were incorporated in the four versions of ComBat (standard, B, M, and B-M ComBat). The proposed approach was evaluated using a dataset of 189 locally advanced cervical cancer patients from 3 centers, with magnetic resonance imaging (MRI) and positron emission tomography (PET) images, with the clinical endpoint of predicting local failure. The impact performance of the TL approach was evaluated by comparing the harmonization achieved using only parts of the data to the reference (harmonization achieved using all the available data). It was performed through three different machine learning pipelines. RESULTS: The proposed TL technique was successful in harmonizing features of new patients from a known center in all versions of ComBat, leading to predictive models reaching similar performance as the ones developed using the features harmonized with all the data available. CONCLUSION: The proposed TL approach enables applying a previously determined ComBat transform to new, previously unseen data.

Identifying brain networks in synaptic density PET (11C-UCB-J) with independent component analysis.

BACKGROUND: The human brain is inherently organized into distinct networks, as reported widely by resting-state functional magnetic resonance imaging (rs-fMRI), which are based on blood-oxygen-level-dependent (BOLD) signal fluctuations. 11C-UCB-J PET maps synaptic density via synaptic vesicle protein 2A, which is a more direct structural measure underlying brain networks than BOLD rs-fMRI. METHODS: The aim of this study was to identify maximally independent brain source networks, i.e., "spatial patterns with common covariance across subjects", in 11C-UCB-J data using independent component analysis (ICA), a data-driven analysis method. Using a population of 80 healthy controls, we applied ICA to two 40-sample subsets and compared source network replication across samples. We examined the identified source networks at multiple model orders, as the ideal number of maximally independent components (IC) is unknown. In addition, we investigated the relationship between the strength of the loading weights for each source network and age and sex. RESULTS: Thirteen source networks replicated across both samples. We determined that a model order of 18 components provided stable, replicable components, whereas estimations above 18 were not stable. Effects of sex were found in two ICs. Nine ICs showed age-related change, with 4 remaining significant after correction for multiple comparison. CONCLUSION: This study provides the first evidence that human brain synaptic density can be characterized into organized covariance patterns. Furthermore, we demonstrated that multiple synaptic density source networks are associated with age, which supports the potential utility of ICA to identify biologically relevant synaptic density source networks.

Validation of a combined image derived input function and venous sampling approach for the quantification of [18F]GE-179 PET binding in the brain.

Blood-based kinetic analysis of PET data relies on an accurate estimate of the arterial plasma input function (PIF). An alternative to invasive measurements from arterial sampling is an image-derived input function (IDIF). However, an IDIF provides the whole blood radioactivity concentration, rather than the required free tracer radioactivity concentration in plasma. To estimate the tracer PIF, we corrected an IDIF from the carotid artery with estimates of plasma parent fraction (PF) and plasma-to-whole blood (PWB) ratio obtained from five venous samples. We compared the combined IDIF+venous approach to gold standard data from arterial sampling in 10 healthy volunteers undergoing [18F]GE-179 brain PET imaging of the NMDA receptor. Arterial and venous PF and PWB ratio estimates determined from 7 patients with traumatic brain injury (TBI) were also compared to assess the potential effect of medication. There was high agreement between areas under the curves of the estimates of PF (r = 0.99, p<0.001), PWB ratio (r = 0.93, p<0.001), and the PIF (r = 0.92, p<0.001) as well as total distribution volume (VT) in 11 regions across the brain (r = 0.95, p<0.001). IDIF+venous VT had a mean bias of -1.7% and a comparable regional coefficient of variation (arterial: 21.3 ± 2.5%, IDIF+venous: 21.5 ± 2.0%). Simplification of the IDIF+venous method to use only one venous sample provided less accurate VT estimates (mean bias 9.9%; r = 0.71, p<0.001). A version of the method that avoids the need for blood sampling by combining the IDIF with population-based PF and PWB ratio estimates systematically underestimated VT (mean bias -20.9%), and produced VT estimates with a poor correlation to those obtained using arterial data (r = 0.45, p<0.001). Arterial and venous blood data from 7 TBI patients showed high correlations for PF (r = 0.92, p = 0.003) and PWB ratio (r = 0.93, p = 0.003). In conclusion, the IDIF+venous method with five venous samples provides a viable alternative to arterial sampling for quantification of [18F]GE-179 VT.

AMYQ: An index to standardize quantitative amyloid load across PET tracers.

INTRODUCTION: Positron emission tomography (PET) amyloid quantification methods require magnetic resonance imaging (MRI) for spatial registration and a priori reference region to scale the images. Furthermore, different tracers have distinct thresholds for positivity. We propose the AMYQ index, a new measure of amyloid burden, to overcome these limitations. METHODS: We selected 18F-amyloid scans from ADNI and Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) with the corresponding T1-MRI. A subset also had neuropathological data. PET images were normalized, and the AMYQ was calculated based on an adaptive template. We compared AMYQ with the Centiloid scale on clinical and neuropathological diagnostic performance. RESULTS: AMYQ was related with amyloid neuropathological burden and had excellent diagnostic performance to discriminate controls from patients with Alzheimer's disease (AD) (area under the curve [AUC] = 0.86). AMYQ had a high agreement with the Centiloid scale (intraclass correlation coefficient [ICC] = 0.88) and AUC between 0.94 and 0.99 to discriminate PET positivity when using different Centiloid cutoffs. DISCUSSION: AMYQ is a new MRI-independent index for standardizing and quantifying amyloid load across tracers.

PET Molecular Imaging: A Holistic Review of Current Practice and Emerging Perspectives for Diagnosis, Therapeutic Evaluation and Prognosis in Clinical Oncology.

PET/CT molecular imaging has been imposed in clinical oncological practice over the past 20 years, driven by its two well-grounded foundations: quantification and radiolabeled molecular probe vectorization. From basic visual interpretation to more sophisticated full kinetic modeling, PET technology provides a unique opportunity to characterize various biological processes with different levels of analysis. In clinical practice, many efforts have been made during the last two decades to standardize image analyses at the international level, but advanced metrics are still under use in practice. In parallel, the integration of PET imaging with radionuclide therapy, also known as radiolabeled theranostics, has paved the way towards highly sensitive radionuclide-based precision medicine, with major breakthroughs emerging in neuroendocrine tumors and prostate cancer. PET imaging of tumor immunity and beyond is also emerging, emphasizing the unique capabilities of PET molecular imaging to constantly adapt to emerging oncological challenges. However, these new horizons face the growing complexity of multidimensional data. In the era of precision medicine, statistical and computer sciences are currently revolutionizing image-based decision making, paving the way for more holistic cancer molecular imaging analyses at the whole-body level.

Metabolic connectivity-based single subject classification by multi-regional linear approximation in the rat.

Metabolic connectivity patterns on the basis of [18F]-FDG positron emission tomography (PET) are used to depict complex cerebral network alterations in different neurological disorders and therefore may have the potential to support diagnostic decisions. In this study, we established a novel statistical classification method taking advantage of differential time-dependent states of whole-brain metabolic connectivity following unilateral labyrinthectomy (UL) in the rat and explored its classification accuracy. The dataset consisted of repeated [18F]-FDG PET measurements at baseline and 1, 3, 7, and 15 days (= maximum of 5 classes) after UL with 17 rats per measurement day. Classification in different stages after UL was performed by determining connectivity patterns for the different classes by Pearson's correlation between uptake values in atlas-based segmented brain regions. Connections were fitted with a linear function, with which different thresholds on the correlation coefficient (r = [0.5, 0.85]) were investigated. Rats were classified by determining the congruence of their PET uptake pattern with the fitted connectivity patterns in the classes. Overall, the classification accuracy with this method was 84.3% for 3 classes, 75.0% for 4 classes, and 54.1% for 5 classes and outperformed random classification as well as machine learning classification on the same dataset. The optimal classification thresholds of the correlation coefficient and distance-to-fit were found to be |r| > 0.65 and d = 4 when using Siegel's slope estimator for fitting. This connectivity-based classification method can compete with machine learning classification and may have methodological advantages when applied to support PET-based diagnostic decisions in neurological network disorders (such as neurodegenerative syndromes).

The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders.

Imaging biomarkers play a wide-ranging role in clinical trials for neurological disorders. This includes selecting the appropriate trial participants, establishing target engagement and mechanism-related pharmacodynamic effect, monitoring safety, and providing evidence of disease modification. In the early stages of clinical drug development, evidence of target engagement and/or downstream pharmacodynamic effect-especially with a clear relationship to dose-can provide confidence that the therapeutic candidate should be advanced to larger and more expensive trials, and can inform the selection of the dose(s) to be further tested, i.e., to "de-risk" the drug development program. In these later-phase trials, evidence that the therapeutic candidate is altering disease-related biomarkers can provide important evidence that the clinical benefit of the compound (if observed) is grounded in meaningful biological changes. The interpretation of disease-related imaging markers, and comparability across different trials and imaging tools, is greatly improved when standardized outcome measures are defined. This standardization should not impinge on scientific advances in the imaging tools per se but provides a common language in which the results generated by these tools are expressed. PET markers of pathological protein aggregates and structural imaging of brain atrophy are common disease-related elements across many neurological disorders. However, PET tracers for pathologies beyond amyloid ß and tau are needed, and the interpretability of structural imaging can be enhanced by some simple considerations to guard against the possible confound of pseudo-atrophy. Learnings from much-studied conditions such as Alzheimer's disease and multiple sclerosis will be beneficial as the field embraces rarer diseases.

Cerebrovascular reactivity measurements using simultaneous 15O-water PET and ASL MRI: Impacts of arterial transit time, labeling efficiency, and hematocrit.

Cerebrovascular reactivity (CVR) reflects the capacity of the brain to meet changing physiological demands and can predict the risk of cerebrovascular diseases. CVR can be obtained by measuring the change in cerebral blood flow (CBF) during a brain stress test where CBF is altered by a vasodilator such as acetazolamide. Although the gold standard to quantify CBF is PET imaging, the procedure is invasive and inaccessible to most patients. Arterial spin labeling (ASL) is a non-invasive and quantitative MRI method to measure CBF, and a consensus guideline has been published for the clinical application of ASL. Despite single post labeling delay (PLD) pseudo-continuous ASL (PCASL) being the recommended ASL technique for CBF quantification, it is sensitive to variations to the arterial transit time (ATT) and labeling efficiency induced by the vasodilator in CVR studies. Multi-PLD ASL controls for the changes in ATT, and velocity selective ASL is in theory insensitive to both ATT and labeling efficiency. Here we investigate CVR using simultaneous 15O-water PET and ASL MRI data from 19 healthy subjects. CVR and CBF measured by the ASL techniques were compared using PET as the reference technique. The impacts of blood T1 and labeling efficiency on ASL were assessed using individual measurements of hematocrit and flow velocity data of the carotid and vertebral arteries measured using phase-contrast MRI. We found that multi-PLD PCASL is the ASL technique most consistent with PET for CVR quantification (group mean CVR of the whole brain = 42±19% and 40±18% respectively). Single-PLD ASL underestimated the CVR of the whole brain significantly by 15±10% compared with PET (p<0.01, paired t-test). Changes in ATT pre- and post-acetazolamide was the principal factor affecting ASL-based CVR quantification. Variations in labeling efficiency and blood T1 had negligible effects.

Validation of kinetic modeling of [15O]H2O PET using an image derived input function on hybrid PET/MRI.

In present study we aimed to validate the use of image-derived input functions (IDIF) in the kinetic modeling of cerebral blood flow (CBF) measured by [15O]H2O PET by comparing with the accepted reference standard arterial input function (AIF). Additional comparisons were made to mean cohort AIF and CBF values acquired by methodologically independent phase-contrast mapping (PCM) MRI. Using hybrid PET/MRI an IDIF was generated by measuring the radiotracer concentration in the internal carotid arteries and correcting for partial volume effects using the intravascular volume measured from MRI-angiograms. Seven patients with carotid steno-occlusive disease and twelve healthy controls were examined at rest, after administration of acetazolamide, and, in the control group, during hyperventilation. Agreement between the techniques was examined by linear regression and Bland-Altman analysis. Global CBF values modeled using IDIF correlated with values from AIF across perfusion states in both patients (p<10-6, R2=0.82, 95% limits of agreement (LoA)=[-11.3-9.9] ml/100 g/min) and controls (p<10-6, R2=0.87, 95% LoA=[-17.1-13.7] ml/100 g/min). The reproducibility of gCBF using IDIF was identical to AIF (15.8%). Values from IDIF and AIF had equally good correlation to measurements by PCM MRI, R2=0.86 and R2=0.84, (p<10-6), respectively. Mean cohort AIF performed substantially worse than individual IDIFs (p<10-6, R2=0.63, LoA=[-12.8-25.3] ml/100 g/min). In the patient group, use of IDIF provided similar reactivity maps compared to AIF. In conclusion, global CBF values modeled using IDIF correlated with values modeled by AIF and similar perfusion deficits could be established in a patient group.

Postoperative Surveillance in Older Adults With T1N0M0 Low-risk Papillary Thyroid Cancer.

BACKGROUND: The frequency and cost of postoperative surveillance for older adults (>65 y) with T1N0M0 low-risk papillary thyroid cancer (PTC) have not been well studied. METHODS: Using the SEER-Medicare (2006-2013) database, frequency and cost of surveillance concordant with American Thyroid Association (ATA) guidelines (defined as an office visit, ≥1 thyroglobulin measurement, and ultrasound 6- to 24-month postoperatively) were analyzed for the overall cohort of single-surgery T1N0M0 low-risk PTC, stratified by lobectomy versus total thyroidectomy. RESULTS: Majority of 2097 patients in the study were white (86.7%) and female (77.5%). Median age and tumor size were 72 y (interquartile range 68-76) and 0.6 cm (interquartile range 0.3-1.1 cm), respectively; 72.9% of patients underwent total thyroidectomy. Approximately 77.5% of patients had a postoperative surveillance visit; however, only 15.9% of patients received ATA-concordant surveillance. Patients who underwent total thyroidectomy as compared with lobectomy were more likely to undergo surveillance testing, thyroglobulin (61.7% versus 24.8%) and ultrasound (37.5% versus 29.2%) (all P < 0.01), and receive ATA-concordant surveillance (18.5% versus 9.0%, P < 0.001). Total surveillance cost during the study period was $621,099. Diagnostic radioactive iodine, ablation, and advanced imaging (such as positron emission tomography scans) accounted for 55.5% of costs ($344,692), whereas ATA-concordant care accounted for 44.5% of costs. After multivariate adjustment, patients who underwent total thyroidectomy as compared with lobectomy were twice as likely to receive ATA-concordant surveillance (adjusted odds ratio 2.0, 95% confidence interval: 1.5-2.8, P < 0.001). CONCLUSIONS: Majority of older adults with T1N0M0 low-risk PTC do not receive ATA-concordant surveillance; discordant care was costly. Total thyroidectomy was the strongest predictor of receiving ATA-concordant care.

A non-invasive reference-based method for imaging the cerebral metabolic rate of oxygen by PET/MR: theory and error analysis.

Positron emission tomography (PET) remains the gold standard for quantitative imaging of the cerebral metabolic rate of oxygen (CMRO2); however, it is an invasive and complex procedure that requires accounting for recirculating [15O]H2O (RW) and the cerebral blood volume (CBV). This study presents a non-invasive reference-based technique for imaging CMRO2 that was developed for PET/magnetic resonance imaging (MRI) with the goal of simplifying the PET procedure while maintaining its ability to quantify metabolism. The approach is to use whole-brain (WB) measurements of oxygen extraction fraction (OEF) and cerebral blood flow (CBF) to calibrate [15O]O2-PET data, thereby avoiding the need for invasive arterial sampling. Here we present the theoretical framework, along with error analyses, sensitivity to PET noise and inaccuracies in input parameters, and initial assessment on PET data acquired from healthy participants. Simulations showed that neglecting RW and CBV corrections caused errors in CMRO2 of less than ±10% for changes in regional OEF of ±25%. These predictions were supported by applying the reference-based approach to PET data, which resulted in remarkably similar CMRO2 images to those generated by analyzing the same data using a modeling approach that incorporated the arterial input functions and corrected for CBV contributions. Significant correlations were observed between regional CMRO2 values from the two techniques (slope = 1.00 ± 0.04, R 2 > 0.98) with no significant differences found for integration times of 3 and 5 min. In summary, results demonstrate the feasibility of producing quantitative CMRO2 images by PET/MRI without the need for invasive blood sampling.

A high-resolution in vivo atlas of the human brain's benzodiazepine binding site of GABAA receptors.

Gamma-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the human brain and plays a key role in several brain functions and neuropsychiatric disorders such as anxiety, epilepsy, and depression. For decades, several in vivo and ex vivo techniques have been used to highlight the mechanisms of the GABA system, however, no studies have currently combined the techniques to create a high-resolution multimodal view of the GABA system. Here, we present a quantitative high-resolution in vivo atlas of the human brain benzodiazepine receptor sites (BZR) located on postsynaptic ionotropic GABAA receptors (GABAARs), generated on the basis of in vivo [11C]flumazenil Positron Emission Tomography (PET) data. Next, based on ex vivo autoradiography data, we transform the PET-generated atlas from binding values into BZR protein density. Finally, we examine the brain regional association between BZR protein density and ex vivo mRNA expression for the 19 subunits in the GABAAR, including an estimation of the minimally required expression of mRNA levels for each subunit to translate into BZR protein. This represents the first publicly available quantitative high-resolution in vivo atlas of the spatial distribution of BZR densities in the healthy human brain. The atlas provides a unique neuroscientific tool as well as novel insights into the association between mRNA expression for individual subunits in the GABAAR and the BZR density at each location in the brain.

Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging.

Accurate regional brain quantitative PET measurements, particularly when using partial volume correction, rely on robust image registration between PET and MR images. We argue here that the precision, and hence the uncertainty, of MR-PET image registration is mainly driven by the registration implementation and the quality of PET images due to their lower resolution and higher noise compared to the structural MR images. We propose a dedicated uncertainty analysis for quantifying the precision of MR-PET registration, centred around the bootstrap resampling of PET list-mode events to generate multiple PET image realisations with different noise (count) levels. The effects of PET image reconstruction parameters, such as the use of attenuation and scatter corrections and different number of iterations, on the precision and accuracy of MR-PET registration were investigated. In addition, the performance of four software packages with their default settings for rigid inter-modality image registration were considered: NiftyReg, Vinci, FSL and SPM. Four distinct PET image distributions made of two early time frames (similar to cortical FDG) and two late frames using two amyloid PET dynamic acquisitions of one amyloid positive and one amyloid negative participants were investigated. For the investigated four PET frames, the biggest impact on the uncertainty was observed between registration software packages (up to 10-fold difference in precision) followed by the reconstruction parameters. On average, the lowest uncertainty for different PET frames and brain regions was observed with SPM and two iterations of fully quantitative image reconstruction. The observed uncertainty for the varying PET count-level (from 5% to 60%) was slightly lower than for the reconstruction parameters. We also observed that the registration uncertainty in quantitative PET analysis depends on amyloid status of the considered PET frames, with increased uncertainty (up to three times) when using post-reconstruction partial volume correction. This analysis is applicable for PET data obtained from either PET/MR or PET/CT scanners.