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Molecular Radiation Therapy (MRT) is a valid therapeutic option for a wide range of malignancies, such as neuroendocrine tumors and liver cancers. In its practice, it is generally acknowledged that there is a need to evaluate the influence of different factors affecting the accuracy of dose estimates and to define the actions necessary to maintain treatment uncertainties at acceptable levels. The present study addresses the problem of uncertainty propagation in [sup.90] Y-PET quantification. We assessed the quantitative accuracy in reference conditions of three PET scanners (namely, Siemens Biograph mCT, Siemens Biograph mCT flow, and GE Discovery DST) available at three different Italian Nuclear Medicine centers. Specific aspects of uncertainty within the quantification chain have been addressed, including the uncertainty in the calibration procedure. A framework based on the Guide to the Expression of Uncertainty in Measurement (GUM) approach is proposed for modeling the uncertainty in the quantification processes, and ultimately, an estimation of the uncertainty achievable in clinical conditions is reported.

Molecular Radiation Therapy (MRT) is a valid therapeutic option for a wide range of malignancies, such as neuroendocrine tumors and liver cancers. In its practice, it is generally acknowledged that there is a need to evaluate the influence of different factors affecting the accuracy of dose estimates and to define the actions necessary to maintain treatment uncertainties at acceptable levels. The present study addresses the problem of uncertainty propagation in 90Y-PET quantification. We assessed the quantitative accuracy in reference conditions of three PET scanners (namely, Siemens Biograph mCT, Siemens Biograph mCT flow, and GE Discovery DST) available at three different Italian Nuclear Medicine centers. Specific aspects of uncertainty within the quantification chain have been addressed, including the uncertainty in the calibration procedure. A framework based on the Guide to the Expression of Uncertainty in Measurement (GUM) approach is proposed for modeling the uncertainty in the quantification processes, and ultimately, an estimation of the uncertainty achievable in clinical conditions is reported.

Various factors have been identified that influence quantitative accuracy and image interpretation in positron emission tomography (PET). Through the continuous introduction of new PET technology—both imaging hardware and reconstruction software—into clinical care, we now find ourselves in a transition period in which traditional and new technologies coexist. The effects on the clinical value of PET imaging and its interpretation in routine clinical practice require careful reevaluation. In this review, we provide a comprehensive summary of important factors influencing quantification and interpretation with a focus on recent developments in PET technology. Finally, we discuss the relationship between quantitative accuracy and subjective image interpretation.

Background Device-based respiratory gating improves diagnostic and quantification accuracy in positron emission tomography (PET), but requires additional time to setup the device and the failure rate can be significant. Our aim was to internally validate the quantification performance of data-driven respiratory-gated PET imaging against the gold standard, the device-based method, in clinical oncological practice. We retrospectively analysed [18F]FDG PET/CT scans of patients from our centre with at least one measurable [18F]FDG-avid malignant lesion. All PET/CT acquisitions were performed on a Siemens Biograph 64 Vision 600 system with respiratory gating by belt and also by adding the data-driven gating with OncoFreeze AI™. We recorded the SUVmax and SUVpeak for up to a maximum of 5 lesions per patient. We computed the mean absolute bias between the two gating methods and the 95% confidence intervals (CI) at the cohort level and in subgroups. Results Of the 692 consecutive patients screened for inclusion, 196 patients were analysed, from whom 536 lesions were measured. The mean absolute biases in the SUVmax and SUVpeak of lesions in the whole cohort were 3.8% (CI 3.4–4.2) and 2.1% (CI 1.9–2.4), respectively. At patient-level, 21% of them had at least one lesion with a SUVmax bias above 10%, while for SUVpeak this proportion was 5%. In the subgroup analysis by PERCIST criteria, only 2% of patients had significant bias in the SUVmax, and 0.5% in SUVpeak. There was no clinically significant effect of lesion size or anatomical site on SUV measurements between the two respiratory gating methods. Conclusion Quantitative comparison of data-driven and device-based respiratory-gated PET scans revealed negligible differences, proving that data-driven respiratory gating is a reliable and accurate alternative to the device-based gating method in routine [18F]FDG-PET/CT oncological evaluation.

BackgroundMetal artefact reduction (MAR) techniques still are in limited use in positron emission tomography/computed tomography (PET/CT). This study aimed to investigate the effect of Smart MAR on quantitative PET analysis in the vicinity of hip prostheses.Materials and methodsActivities were measured on PET/CT images in 6 sources with tenfold activity concentration contrast to background, attached to the head, neck and the major trochanter of a human cadaveric femur, and in the same sources in similar locations after a hip prosthesis (titanium cup, ceramic head, chrome-cobalt stem) had been inserted into the femur. Measurements were compared between PET attenuation corrected using either conventional or MAR CT. In 38 patients harbouring 49 hip prostheses, standardized uptake values (SUV) in 6 periprosthetic regions and the bladder were compared between PET attenuation corrected with either conventional or MAR CT.ResultsUsing conventional CT, measured activity decreased with 2 to 13% when the prosthesis was inserted. Use of MAR CT increased measured activity by up to 11% compared with conventional CT and reduced the relative difference with the reference values to under 5% in all sources. In all regions, to the exception of the prosthesis shaft, SUVmean increased significantly (p < 0.001) by use of MAR CT. Median (interquartile range) percentual increases of SUVmean were 1.4 (0.0–4.2), 4.0 (1.8–7.8), 7.8 (4.1–12.4), 1.5 (0.0–3.2), 1.4 (0.8–2.8) in acetabulum, lateral neck, medial neck, lateral diaphysis and medial diaphysis, respectively. Except for the shaft, the coefficient of variation did not increase significantly. Except for the erratic changes in the prosthesis shaft, decreases in SUVmean were rare and small. Bladder SUVmean increased by 0.9% in patients with unilateral prosthesis and by 4.1% in patients with bilateral prosthesis.ConclusionsIn a realistic hip prosthesis phantom, Smart MAR restores quantitative accuracy by recovering counts in underestimated sources. In patient studies, Smart MAR increases SUV in all areas surrounding the prosthesis, most markedly in the femoral neck region. This proves that underestimation of activity in the PET image is the most prevalent effect due to metal artefacts in the CT image in patients with hip prostheses. Smart MAR increases SUV in the urinary bladder, indicating effects at a distance from the prosthesis.

The purpose of this study was to investigate the quantitative properties and effects of ordered-subset expectation maximization (OSEM) on kinetic modeling compared with filtered backprojection (FBP) in dynamic PET studies. Both phantom and patient studies were performed. For phantom studies dynamic two-dimensional emission scans with 10-min frames and 20-min scan intervals were acquired over a 14-h period using an HR+ PET scanner. Various phantoms were scanned: 2-, 5-, 10-, and 20-cm-diameter phantoms filled with an 18F solution (300 kBq/mL) and a NEMA phantom filled with an 18F background (40 kBq/mL) and a cold or 11C insert (450 kBq/mL). Transmission (Tx) scans of 5-60 min were acquired. Data were reconstructed using FBP Hanning 0.5 and OSEM with 2-12 iterations and 12 or 24 subsets. Quantitative accuracy and noise characteristics were assessed. For patient studies, five cardiac, three oncologic, and three brain dynamic 18F-FDG scans were used. Five reconstructions were performed: FBP Hanning 0.5, and OSEM 2 x 12 and OSEM 4 x 16 with and without 5-mm full width at half maximum smoothing. Time-activity curves were calculated using volumes of interest. The input function was derived from arterial sampling. Metabolic rate of glucose (MRglu) was calculated with a standard two-tissue compartment model and Patlak analysis. Contribution of Tx noise to the reconstructed image was smaller for OSEM than for FBP. Differences in signal-to-noise ratio between FBP and OSEM depended on number of iterations and phantom size. Bias with OSEM was observed for regions enclosed within a 5- to 10-fold hotter background. For cardiac studies OSEM 2 x 12 and OSEM 4 x 16 resulted in 13% and 21% higher pixel values and 9% and 15% higher MRglu values compared with FBP. Smoothing decreased all these values to 2%. Similar results were found for most tumor studies. For brain studies MRglu of FBP and OSEM 4 x 16 agreed within 2%. Use of OSEM image-derived input functions for cardiac PET studies resulted in a decrease in calculated MRglu of about 15%. For most PET studies OSEM has equal quantitative accuracy as FBP. The higher pixel and MRglu values are explained by the better resolution of OSEM. However, OSEM does not provide accurate image-derived input functions for FDG cardiac PET studies because of bias in regions located within a hotter background.

Purpose This study was conducted to directly compare the high-resolution research tomograph (HRRT) (high-resolution brain) and HR+ (standard whole-body) positron emission tomography (PET) only scanners for quantitative brain studies using three tracers with vastly different tracer distributions. Procedures Healthy volunteers underwent successive scans on HR+ and HRRT scanners (in random order) using either ( R )-[ 11 C]verapamil ( n  = 6), [ 11 C]raclopride ( n  = 7) or [ 11 C]flumazenil ( n  = 7). For all tracers, metabolite-corrected plasma-input functions were generated. Results After resolution matching, HRRT-derived kinetic parameter values correlated well with those of HR+ for all tracers (intraclass correlation coefficients ≥0.78), having a good absolute interscanner test-retest variability (≤15 %). However, systematic differences can be seen for HRRT-derived kinetic parameter values (range −13 to +15 %). Conclusion Quantification of kinetic parameters based on plasma-input models leads to comparable results when spatial resolution between HRRT and HR+ data is matched. When using reference-tissue models, differences remain that are likely caused by differences in attenuation and scatter corrections and/or image reconstruction.

The purpose of this study was to evaluate the quantitative accuracy of three-dimensional (3D) rendered images acquired with multi-detector row computed tomography (MDCT) by means of distance measurements of a dry human skull for various slice thicknesses and acquisition modes. A radiologist directly measured the distance of line items on the skull surface to establish reference \"gold standards.\" The skull specimen was scanned with a MDCT with various scanning parameters (slice thicknesses and acquisition modes). An observer measured the corresponding distances of the same items on 3D rendered images. The quantitative accuracy of distance measurements was statistically evaluated. There were no significant statistical differences (P value <.05) in accuracy of distance measurements among the scan modes. However, the results showed that acquisition slice thickness was the influential factor in determining the accuracy of the 3D rendered MDCT images. The quantitative analysis of distance measurement may be a useful tool evaluating the accuracy and defining optimal parameters of 3D rendered images.

Insomnia is the second most prevalent mental disorder, with no sufficient treatment available. Despite substantial heritability, insight into the associated genes and neurobiological pathways remains limited. Here, we use a large genetic association sample ( n  = 1,331,010) to detect novel loci and gain insight into the pathways, tissue and cell types involved in insomnia complaints. We identify 202 loci implicating 956 genes through positional, expression quantitative trait loci, and chromatin mapping. The meta-analysis explained 2.6% of the variance. We show gene set enrichments for the axonal part of neurons, cortical and subcortical tissues, and specific cell types, including striatal, hypothalamic, and claustrum neurons. We found considerable genetic correlations with psychiatric traits and sleep duration, and modest correlations with other sleep-related traits. Mendelian randomization identified the causal effects of insomnia on depression, diabetes, and cardiovascular disease, and the protective effects of educational attainment and intracranial volume. Our findings highlight key brain areas and cell types implicated in insomnia, and provide new treatment targets. Genome-wide analyses in >1 million individuals identify new loci and pathways associated with insomnia. The findings implicate key brain areas and cell types in the neurobiology of insomnia and highlight potential targets for developing new treatments.