Tomographic reconstruction from positron emission tomography (PET) data is an ill-posed problem that requires regularization. An attractive approach is to impose an l(1) -regularization constraint, which favors sparse solutions in the wavelet domain. This can be achieved quite efficiently thanks to the iterative algorithm developed by Daubechies et al., 2004. In this paper, we apply this technique and extend it for the reconstruction of dynamic (spatio-temporal) PET data. Moreover, instead of using classical wavelets in the temporal dimension, we introduce exponential-spline wavelets (E-spline wavelets) that are specially tailored to model time activity curves (TACs) in PET. We show that the exponential-spline wavelets naturally arise from the compartmental description of the dynamics of the tracer distribution. We address the issue of the selection of the "optimal" E-spline parameters (poles and zeros) and we investigate their effect on reconstruction quality. We demonstrate the usefulness of spatio-temporal regularization and the superior performance of E-spline wavelets over conventional Battle-LemariE wavelets in a series of experiments: the 1-D fitting of TACs, and the tomographic reconstruction of both simulated and clinical data. We find that the E-spline wavelets outperform the conventional wavelets in terms of the reconstructed signal-to-noise ratio (SNR) and the sparsity of the wavelet coefficients. Based on our simulations, we conclude that replacing the conventional wavelets with E-spline wavelets leads to equal reconstruction quality for a 40% reduction of detected coincidences, meaning an improved image quality for the same number of counts or equivalently a reduced exposure to the patient for the same image quality.
Abstract Introduction Dual‐biomarker positron emission tomography (PET), providing complementary information on cerebral blood flow and amyloid‐β deposition, is of clinical interest for Alzheimer's disease (AD). The purpose of this study was to validate the perfusion components of early‐phase 18 F‐florbetapir (eAV45), the 18 F‐AV45 delivery rate (R1), and 18 F‐FDG against 15 O‐H 2 O PET and assess how they change with disease severity. Methods This study included ten controls, 19 amnestic mild cognitive impairment, and 10 AD dementia subjects. Within‐subject regional correlations between modalities, between‐group regional and voxel‐wise analyses of covariance per modality, and receiver operating characteristic analyses for discrimination between groups were performed. Results FDG standardized uptake value ratio, eAV45 (0–2 min) standardized uptake value ratio, and AV45‐R1 were significantly associated with H 2 O PET (regional Pearson r = 0.54–0.82, 0.70–0.94, and 0.65–0.92, respectively; P < .001). All modalities confirmed reduced cerebral blood flow in the posterior cingulate of patients with amnestic mild cognitive impairment and AD dementia, which was associated with lower cognition (r = 0.36–0.65, P < .025) and could discriminate between patient and control groups (area under the curve > 0.80). However, eAV45 was less sensitive to reflect the disease severity than AV45‐R1 or FDG. Discussion R1 is preferable over eAV45 for accurate representation of brain perfusion in dual‐biomarker PET for AD.
The image space reconstruction algorithm (ISRA) has been shown to be a non-negative least squares estimator, and was introduced as an alternative iterative image reconstruction method for positron emission tomography (PET) data. The implementation of ISRA is straightforward: the ratio of the backprojected measured data to that of the backprojected expected data is used to multiplicatively update the current image estimate. This work starts with a modified weighted least squares objective function to derive a more general form of the ISRA algorithm, which importantly accommodates weighting of the backprojection. Simply by changing the choice of backprojection weighting factors at a given iteration, both the well known ML-EM (maximum likelihood expectation maximization) algorithm as well as the standard ISRA, are obtained as special cases. ML-EM corresponds to using the current estimate of the expected data as the weights for backprojection, and ISRA corresponds to the case of unit weighting during backprojection. Of particular interest however, is that the framework naturally suggests the existence of many alternative reconstruction algorithms through alternative data weighting choices. By changing the weighting factors, a performance improvement over ISRA is obtained, as well as a slight performance improvement compared to ML-EM (for the task of accurate region quantification which is considered in this work). Specifically, these improvements are obtained, for example, by using a spatially-smoothed copy of the measured data as weighting factors during backprojection.
Abstract Purpose Positron emission tomography (PET) imaging of mutant huntingtin (mHTT) aggregates is a potential tool to monitor disease progression as well as the efficacy of candidate therapeutic interventions for Huntington’s disease (HD). To date, the focus has been mainly on the investigation of 11 C radioligands; however, favourable 18 F radiotracers will facilitate future clinical translation. This work aimed at characterising the novel [ 18 F]CHDI-650 PET radiotracer using a combination of in vivo and in vitro approaches in a mouse model of HD. Methods After characterising [ 18 F]CHDI-650 using in vitro autoradiography, we assessed in vivo plasma and brain radiotracer stability as well as kinetics through dynamic PET imaging in the heterozygous (HET) zQ175DN mouse model of HD and wild-type (WT) littermates at 9 months of age. Additionally, we performed a head-to-head comparison study at 3 months with the previously published [ 11 C]CHDI-180R radioligand. Results Plasma and brain radiometabolite profiles indicated a suitable metabolic profile for in vivo imaging of [ 18 F]CHDI-650. Both in vitro autoradiography and in vivo [ 18 F]CHDI-650 PET imaging at 9 months of age demonstrated a significant genotype effect ( p <0.0001) despite the poor test-retest reliability. [ 18 F]CHDI-650 PET imaging at 3 months of age displayed higher differentiation between genotypes when compared to [ 11 C]CHDI-180R. Conclusion Overall, [ 18 F]CHDI-650 allows for discrimination between HET and WT zQ175DN mice at 9 and 3 months of age. [ 18 F]CHDI-650 represents the first suitable 18 F radioligand to image mHTT aggregates in mice and its clinical evaluation is underway.
In this work we aim to track the head motion of the awake mouse during the PET scan and correct the scan for motion to obtain motion-free brain images. The mouse head motion is tracked using radioactive point sources attached to the head (point source tracking, PST). In addition, a specially designed holder with a platform area of 9 × 10 cm was used to maintain the mouse inside the scanner field of view. Two mice were injected with [ 18 F]FDG and scanned awake during 20 min and under anesthesia 20 min to obtain a motion-free reconstruction for comparison. The mice moved over the entire available platform area. The average mouse head speed was 0.804 and 0.788 cm/s and the tracking success rate was 85.3% and 84.9% for the two mice respectively. After motion correction, the mouse brain FDG uptake pattern was recovered in the reconstructed image. The pearson's r correlation between awake and anesthesia reconstructions brain relative regional uptake was 0.875 and 0.986 the two mice respectively. The mouse head was successfully tracked using the PST method and the brain motion corrected reconstructions showed high correlation with motionfree reconstructions.
OBJECTIVES: To assess the predictive value of the St. Thomas's Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) instrument, a simple fall‐risk assessment tool, when administered at a patient's hospital bedside by nurses. DESIGN: Prospective multicenter study. SETTING: Six Belgian hospitals. PARTICIPANTS: A total of 2,568 patients (mean age±standard deviation 67.2±18.4; 55.3% female) on four surgical (n=875, 34.1%), eight geriatric (n=687, 26.8%), and four general medical wards (n=1,006, 39.2%) were included in this study upon hospital admission. All patients were hospitalized for at least 48 hours. MEASUREMENTS: Nurses completed the STRATIFY within 24 hours after admission of the patient. Falls were documented on a standardized incident report form. RESULTS: The number of fallers was 136 (5.3%), accounting for 190 falls and an overall rate of 7.3 falls per 1,000 patient days for all hospitals. The STRATIFY showed good sensitivity (≥84%) and high negative predictive value (≥99%) for the total sample, for patients admitted to general medical and surgical wards, and for patients younger than 75, although it showed moderate (69%) to low (52%) sensitivity and high false‐negative rates (31–48%) for patients admitted to geriatric wards and for patients aged 75 and older. CONCLUSION: Although the STRATIFY satisfactorily predicted the fall risk of patients admitted to general medical and surgical wards and patients younger than 75, it failed to predict the fall risk of patients admitted to geriatric wards and patients aged 75 and older (particularly those aged 75–84).
