Temperature quenching in LAB based liquid scintillator and muon-induced backgrounds in the SNO+ experiment

The starting SNO+ experiment, successor to the Sudbury Neutrino Observatory, is a neutrino detector using LAB based liquid scintillator as active medium. Situated in the SNOLab deep underground laboratory in Sudbury, Canada, the rock overburden amounts to about 6 km.w.e., providing an effective shielding against cosmic rays. The residual muon rate is 63 μ/day going through the detector volume. About 780 t of an LAB mixture inside an acrylic sphere with a 6 m radius will be observed by ≈ 9300 photomultipliers, surrounded by a ≈ 7000 t water shielding. SNO+ will be searching for low energy solar-, geo-, reactor- and supernova neutrinos, but the main goal is the observation of the neutrinoless double beta decay in Te-130. Under operating conditions, the scintillator will be cooled to about 12° C. This work investigated the effect of temperature changes on the light output of LAB based liquid scintillator in a range from -5° C to 30° C with α-particles and electrons in a small scale setup. Assuming a linear behaviour, a combined negative temperature coefficient of (−0.29 ± 0.01) %/° C is found. Considering hints for a particle type dependency, electrons show (−0.17 ± 0.02) %/° C whereas the temperature dependency seems stronger for α-particles (−0.35 ± 0.03) %/° C. A pulse shape analysis shows increased strength of a slow decay component at lower temperatures, pointing to reduced non-radiative triplet state de-excitations at lower temperatures. Furthermore, this work found upper bounds for the in-situ muon-induced isotope production via scaling calculations and simulations with Geant4 based software. For the most concerning isotope C-11, an upper limit of about 1.3 × 10^3 decays/kt/yr is found and a reduction technique, developed by the Borexino collaboration, can be effectively applied for SNO+. Also a muon reconstruction algorithm is implemented, performing reasonably well, but not good enough to improve the background reduction scheme.Das zukünftige SNO+ experiment, Nachfolger des Sudbury Neutrino Observatory, ist ein Neutrino-Detektor mit LAB basierten Flüssigszintillator als aktivem Medium. Im SNOLab Untertagelabor (Sudbury, Kanada) gelegen, ist es durch die Felsüberdeckung von 6 km.w.e. hervorragend gegen kosmische Strahlung abgeschirmt. Die Rate der übrigen Myonen die das Detektorvolumen durchdringen beträgt ca. 63 μ/Tag. In einer Acrylkugel, mit einem Radius von 6 m, wird eine LAB Mischung von ≈ 9300 Photomultipliern beobachtet und von einer Wasserabschirmung von ≈ 7 kt umgeben. SNO+ wird nach niederenergetischen solaren-, Geo-, Reaktor- und Supernova Neutrinos suchen, aber das Hauptziel ist die Beobachtung von neutrinolosen doppelten Betazerfällen in Te-130. Unter den Betriebsbedingungen wird der Flüssigszintillator eine Temperatur von ca. 12° C annehmen. Diese Arbeit hat den Einfluss von Temperaturveränderungen in einem Bereich von -5° C to 30° C auf die erzeugte Lichtmenge untersucht. Dazu wurden α-Teilchen und Elektronen in einem kleineren Versuchaufbau beobachtet. Unter der Annahme eines linearen Verhaltens, wurde ein globaler negativer Temperaturkoeffizient von (−0.29 ± 0.01) %/° C gefunden. Unter Berücksichtigung von Hinweisen auf eine Teilchenartabhängigkeit, findet sich für Elektronen ein Koeffizient von (−0.17 ± 0.02) %/° C, wohingegen α-Teilchen eine stärkere Abhängikeit von (−0.35 ± 0.03) %/° C aufweisen. Eine Pulsformanalyse zeigt eine bei tieferen Temperaturen stärker ausgeprägte langsame Zerfallskomponente, was darauf hinweist dass die nicht-radiativen Abregungen der Triplet-Zustände bei niedrigeren Temperaturen reduziert sind. Weiterhin wurden in dieser Arbeit obere Ausschlußgrenzen für in-situ Myon-induzierte Isotopenproduktion gefunden, wozu Skalierungsrechnungen und Simulation mit auf Geant4 basierender Software benutzt wurden. Für das wichtigste Isotop C-11 wurde eine obere Grenze von 1.3 × 10^3 Ereignisse/kt/Jahr gefunden und eine Technik zur Reduzierung des Untergrundes, entwickelt von der Borexino Kollaboration, kann effektiv für SNO+ angewendet werden. Darüber hinaus wurde eine Myon Spurrekonstruktion implementiert, die sinnvolle Ergebnisse liefert, aber nicht gut genug ist um die Untergrund Reduzierung zu unterstützen

Amyloid biomarkers as predictors of conversion from mild cognitive impairment to Alzheimer’s dementia: a comparison of methods

Background!#!Amyloid-β (Aβ) PET is an established predictor of conversion from mild cognitive impairment (MCI) to Alzheimer's dementia (AD). We compared three PET (including an approach based on voxel-wise Cox regression) and one cerebrospinal fluid (CSF) outcome measures in their predictive power.!##!Methods!#!Datasets were retrieved from the ADNI database. In a training dataset (N = 159), voxel-wise Cox regression and principal component analyses were used to identify conversion-related regions (Cox-VOI and AD conversion-related pattern (ADCRP), respectively). In a test dataset (N = 129), the predictive value of mean normalized !##!Results!#!All four Aβ measures were significant predictors (p < 0.001). Prediction accuracies (Harrell's c) showed step-wise significant increases from Cox-SUVR (c = 0.71; HR = 1.84 per Z-score increase), composite SUVR (c = 0.73; HR = 2.18), CSF Aβ!##!Conclusion!#!The PES of ADCRP is the most predictive Aβ PET outcome measure, comparable to CSF A

Prognosis of conversion of mild cognitive impairment to Alzheimer's dementia by voxel-wise Cox regression based on FDG PET data.

AimThe value of 18F-fluorodeoxyglucose (FDG) PET for the prognosis of conversion from mild cognitive impairment (MCI) to Alzheimer's dementia (AD) is controversial. In the present work, the identification of cerebral metabolic patterns with significant prognostic value for conversion of MCI patients to AD is investigated with voxel-based Cox regression, which in contrast to common categorical comparisons also utilizes time information.MethodsFDG PET data of 544 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were randomly split into two equally-sized datasets (training and test). Within a median follow-up duration of 47 months (95% CI: 46-48 months) 181 patients developed AD. In the training dataset, voxel-wise Cox regressions were used to identify regions associated with conversion of MCI to AD. These were compared to regions identified by a classical group comparison (analysis of covariance (ANCOVA) with statistical parametric mapping (SPM) 8) between converters and non-converters (both adjusted for apolipoprotein E (APOE) genotype, mini-mental state examination (MMSE) score, age, sex and education). In the test dataset, normalized FDG uptake within significant brain regions from voxel-wise Cox- and ANCOVA analyses (Cox- and ANCOVA- regions of interest (ROI), respectively) and clinical variables APOE status, MMSE score and education were tested in different Cox models (adjusted for age, sex) including: (1) only clinical variables, (2) only normalized FDG uptake in ANCOVA-ROI, (3) only normalized FDG uptake from Cox-ROI, (4) clinical variables plus FDG uptake in ANCOVA-ROI, (5) clinical variables plus FDG uptake from Cox-ROI.ResultsConversion-related regions with relative hypometabolism comprised parts of the temporo-parietal and posterior cingulate cortex/precuneus for voxel-wise ANCOVA, plus frontal regions for voxel-wise Cox regression (both p < .01, false discovery rate (FDR) corrected). The clinical-only model (1) and the models based on normalized FDG uptake from Cox-ROI only (2) and ANCOVA-ROI only (3) all significantly predicted conversion to AD (Wald Test (WT): p < .001). The clinical model (1) was significantly improved by adding imaging information in model (4) (Akaike information criterion (AIC) relative likelihood (RL) (1) vs (4): RL < 0.018). There were no significant differences between models (2) and (3), as well as (4) and (5).ConclusionsVoxel-wise Cox regression identifies conversion-related patterns of cerebral glucose metabolism, but is not superior to classical group contrasts in this regard. With imaging information from both FDG PET patterns, the prediction of conversion to AD was improved

Prognosis of conversion of mild cognitive impairment to Alzheimer's dementia by voxel-wise Cox regression based on FDG PET data.

AimThe value of 18F-fluorodeoxyglucose (FDG) PET for the prognosis of conversion from mild cognitive impairment (MCI) to Alzheimer's dementia (AD) is controversial. In the present work, the identification of cerebral metabolic patterns with significant prognostic value for conversion of MCI patients to AD is investigated with voxel-based Cox regression, which in contrast to common categorical comparisons also utilizes time information.MethodsFDG PET data of 544 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were randomly split into two equally-sized datasets (training and test). Within a median follow-up duration of 47 months (95% CI: 46-48 months) 181 patients developed AD. In the training dataset, voxel-wise Cox regressions were used to identify regions associated with conversion of MCI to AD. These were compared to regions identified by a classical group comparison (analysis of covariance (ANCOVA) with statistical parametric mapping (SPM) 8) between converters and non-converters (both adjusted for apolipoprotein E (APOE) genotype, mini-mental state examination (MMSE) score, age, sex and education). In the test dataset, normalized FDG uptake within significant brain regions from voxel-wise Cox- and ANCOVA analyses (Cox- and ANCOVA- regions of interest (ROI), respectively) and clinical variables APOE status, MMSE score and education were tested in different Cox models (adjusted for age, sex) including: (1) only clinical variables, (2) only normalized FDG uptake in ANCOVA-ROI, (3) only normalized FDG uptake from Cox-ROI, (4) clinical variables plus FDG uptake in ANCOVA-ROI, (5) clinical variables plus FDG uptake from Cox-ROI.ResultsConversion-related regions with relative hypometabolism comprised parts of the temporo-parietal and posterior cingulate cortex/precuneus for voxel-wise ANCOVA, plus frontal regions for voxel-wise Cox regression (both p < .01, false discovery rate (FDR) corrected). The clinical-only model (1) and the models based on normalized FDG uptake from Cox-ROI only (2) and ANCOVA-ROI only (3) all significantly predicted conversion to AD (Wald Test (WT): p < .001). The clinical model (1) was significantly improved by adding imaging information in model (4) (Akaike information criterion (AIC) relative likelihood (RL) (1) vs (4): RL < 0.018). There were no significant differences between models (2) and (3), as well as (4) and (5).ConclusionsVoxel-wise Cox regression identifies conversion-related patterns of cerebral glucose metabolism, but is not superior to classical group contrasts in this regard. With imaging information from both FDG PET patterns, the prediction of conversion to AD was improved

18F-FMISO-PET Hypoxia Monitoring for Head-and-Neck Cancer Patients: Radiomics Analyses Predict the Outcome of Chemo-Radiotherapy

Tumor hypoxia is associated with radiation resistance and can be longitudinally monitored by 18F-fluoromisonidazole (18F-FMISO)-PET/CT. Our study aimed at evaluating radiomics dynamics of 18F-FMISO-hypoxia imaging during chemo-radiotherapy (CRT) as predictors for treatment outcome in head-and-neck squamous cell carcinoma (HNSCC) patients. We prospectively recruited 35 HNSCC patients undergoing definitive CRT and longitudinal 18F-FMISO-PET/CT scans at weeks 0, 2 and 5 (W0/W2/W5). Patients were classified based on peritherapeutic variations of the hypoxic sub-volume (HSV) size (increasing/stable/decreasing) and location (geographically-static/geographically-dynamic) by a new objective classification parameter (CP) accounting for spatial overlap. Additionally, 130 radiomic features (RF) were extracted from HSV at W0, and their variations during CRT were quantified by relative deviations (∆RF). Prediction of treatment outcome was considered statistically relevant after being corrected for multiple testing and confirmed for the two 18F-FMISO-PET/CT time-points and for a validation cohort. HSV decreased in 64% of patients at W2 and in 80% at W5. CP distinguished earlier disease progression (geographically-dynamic) from later disease progression (geographically-static) in both time-points and cohorts. The texture feature low grey-level zone emphasis predicted local recurrence with AUCW2 = 0.82 and AUCW5 = 0.81 in initial cohort (N = 25) and AUCW2 = 0.79 and AUCW5 = 0.80 in validation cohort. Radiomics analysis of 18F-FMISO-derived hypoxia dynamics was able to predict outcome of HNSCC patients after CRT