Merging Biomathematical modelling and Machine learning to predict time-activity curves for PET CNS radioligand development

Purpose: The purpose of this study was the proposal of merging biomathematical model and machine learning approach to predict pharmacokinetics, time-activity curves (TACs), of candidate PET radioligand in brain for the development of PET CNS radioligands.Methods: Biomathematical model used in this study was based on the simplified one-tissue compartment model (1TCM) with the kinetic parameters (K1, k2 and BPND) in the human brain (Guo et al., JNNM, 2009). In silico apparent volume (Vx), lipohilisity (MlogP) of the ligand, in vitro affinity of the ligand (KD) to the target molecule and physiological parameter, the density of molecular target (Bmax) were used for the prediction of kinetic parameters (K1, k2 and BPND) (Arakawa et al., JNM, 2017). TAC can be calculated using K1, k2 and BPND and common arterial input function Cp (t). For merging this biomathematical model and machine learning, random forest (RF) algorithm, was introduced. As the training, 28 CNS radioligand database (Guo et al., JNNM, 2009), which includes mature, under developing and failed PET radioligands for various imaging targets was used. In total 280 datasets (28 CNS radioligand with 10 of Bmax values) was numerically created by biomathematical model. Input data for the prediction model was Vx, KD, Bmax, and MlogP, and the output was set to radioactive concentration [kBq/ml]. 3 PET radioligands ([11C]PIB, [11C]BF227, [18F]FACT)were used for the prediction of TACs. To verify the predicted radioactivity concentration, both predicted TACs from merged approach and from biomathematical model only were compared against averaged TAC from clinical PET study. Results: Correlation coefficient (R2) between training data and predicted radioactivity concentration [kBq/ml] was 0.97, 0.96, and 0.96 for time 5, 30 and 60 min, respectively. For the prediction of TACs for 3 PET radioligands, the merging approach resulted in poor prediction of TACs (different shape of TACs from clinical data) especially [18F]FACT compared with that from biomathematical model only. There are possible reasons for observed poor prediction, the number of training data, PET radioligand for the prediction, information of input datasets. Conclusion: In this study, we proposed the approach merging biomathematical model and machine learning to predict time-activity curves for PET radioligand. Further optimization of machine learning, and increase of applicable datasets would be necessary.2021 VIRTUAL IEEE NCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENC

Quantitative mapping of basal and vasareactive cerebral blood flow using split-dose 123I-iodoamphetamine and single photon emission computed tomography

A new method has been developed for diffusible tracers, to quantify CBF at rest and after pharmacological stress from a single session of dynamic scans with dual bolus administration of a radiotracer. The calculation process consisted of three steps, including the procedures of incorporating background radioactivity contaminated from the previous scan. Feasibility of this approach was tested on clinical SPECT studies on 16 subjects. Two sequential SPECT scans, 30 min apart, were carried out on each subject, after each of two splitdose administrations of 111 MBq IMP. Of these, 11 subjects received acetazolamide at 10 min before the second IMP injection. Additional PET scans were also carried out on 6 subjects on a separate day, at rest and after acetazolamide administration. The other 5 subjects were scanned only at rest during the whole study period. Quantitative CBF obtained by this method was in a good agreement with those determined with PET (y(ml/100 g/min)=1.07%*(ml/100 g/min)-1.14, r=0.94). Vasareactivity was approximately 40% over the whole cerebral area on healthy controls, which was consistent with a literature value. Reproducibility of CBF determined in the rest–rest study was 1.5+/-5.7%. Noise enhancement of CBF images, particularly the second CBF, was reduced, providing reasonable image quality.Repeat assessment of quantitative CBF from a single session of scans with split-dose IMP is accurate, and may be applied to clinical research for assessing vascular reactivity in patients with chronic cerebral vascular disease

Body-contour versus circular orbit acquisition in cardiac SPECT: Assessment of defect detectability with channelized Hotelling observer

Background The resolution of a gamma camera is depthdependentand worsens with increasing distance to the camera resulting in a loss of fine details in SPECT images. A common approach to reduce the effects of this resolution loss is to utilize body-contour acquisition orbits. Even though body-contour orbits can improve resolution of reconstructed images their effect on lesion detection is not well known. Objective To investigate whether body-contour orbits offer better defect detection performance than circular orbits in cardiac SPECT. Methods The athematical cardiac torso (MCAT) phantom was used to model 99mTc-sestamibi uptake. A total of four phantoms (two male and two female) with eight defects (four locations and two sizes) were generated and projection data were simulated using an analytical projector with attenuation, scatter, collimator response and acquisition orbit modelling. The circular and body-contour projections were reconstructed using the OSEM algorithm with/without collimator response compensation. Defect detection performance was assessed by calculating area under the receiver operating characteristic (ROC) curve for channelized Hotelling observer. Results The defect detection performance of circular and body-contour acquisition was very similar and the difference in the area under the ROC curve between the orbits was not statistically significant with or without collimator response compensation. The collimator response compensation, on the other hand, was noticed to be valuable and it provided significantly better defect detection performance than reconstruction without it regardless of the acquisition orbit type. Conclusions We conclude that by replacing circular orbit with more complex body-contour orbit will not lead tostatistically significant increase in defect detection performance in cardiac SPECT

Wavelet denoising for parametric imaging of the peripheral benzodiazepine receptors with 18F-FEDAA1106

TitleWAVELET DENOISING FOR PARAMETRIC IMAGING OF THE PERIPHERAL BENZODIAZEPINE RECEPTORS WITH F-18-FEDAA1106\nAuthorsMiho Shidahara, Yoko Ikoma, Chie Seki, Yota Fujimura, Hiroshi Ito, Yuichi Kimura, Tetsuya Suhara and Iwao Kanno \nAffiliations1 Molecular Imaging Center, National Institute of Radiological Sciences, JAPAN.\nBackground and aims: The statistical noise of time activity curve (TAC) at voxel level causes severe bias and poor precision for estimated binding potential, BP(=k3/k4), images using a nonlinear least square fitting (NLS). The purpose of this study is to evaluate noise reduction capability of wavelet denoising for estimated BP images of the peripheral benzodiazepine receptor (PBR). 18F-FEDAA1106 is a radioligand for PBR, and its BP images should be formed using NLS because no reference regions can be assumed due to the physiological aspects of PBR1. We applied wavelet denoising to simulate data and clinical dynamic image of PBR with 18F-FEDAA1106.Methods: After administration of 18F-FEDAA1106, three dimensional dynamic PET scans were performed by ECAT EXACT HR+ system (CTI-Siemens, Knoxville, USA) having 41 frames. The wavelet processing was applied in a volume fashion with a three-dimensional discrete dual-tree complex wavelet transformation2 at 4 scales with 112 subbands. The advantage of wavelet denoising is to realize spatially adaptive smoothing. In order to eliminate noise component in wavelet coefficients, real and imaginary coefficients for each sub-band were individually thresholded with NormalShrink3. Simulations were conducted to evaluate the performance of the wavelet denoising for parameter estimation. A simulated dynamic data was consisted of 4 parts of 2 gray matters (K1=0.25, k2=0.078, k3=0.043 or 0.0516, k4=0.0086), a white matter (K1=0.15, k2=0.075, k3=0.043, k4=0.01), and CSF to simulate anatomical structure of the brain (Hoffman brain phantom: 12812855 pixels, 222 mm). Each part had the BPs of 5, 6, 4.3, and 0, respectively. Then the phantom was smoothed with Gaussian filter (2.5x2.5 mm FWHM) and then Gaussian noise was added to mimic exact measurement at the noise level 20%. The BP images derived from wavelet denoising were compared with true BP image using 156 rectangular ROIs (55 pixel). The Wavelet denoising was also applied to clinical data derived from 3 young normal volunteers. Parametric images of BP were formed using voxel-based NLS fitting, and the results were compared with an ordinary ROI averaged estimates1. Results: In the simulation studies, estimated BP by denoised image showed better correlation against the true BP values (Fig-1A), although no correlation was observed in the estimates. In clinical data, wavelet denoising improved image quality of the estimates (Fig-1B). Originally estimated BP image includes bias against ROI averaged estimates (Y=1.12X+0.94, R2=0.55), however, estimated BP by denoised image improved relationship with ROI analysis (Y=0.91X+0.95, R2=0.57). Conclusions: Wavelet denoising improves bias and variation of pharmacokinetic parameters, especially, BP. \nReference [1] Fujimura Y, 2006, J Nucl Med, 47, 43-50.[2] Alpert NM, 2006, NeuroImage, 30, 444-451.[3] Fourati W, 2005, Inter J on GVIP, 5, 1-9.Brain\u2707 and BrainPET\u270

Optimal scan time of oxygen-15-labeled gas inhalation autoradiographic method for measurement of cerebral oxygen extraction fraction and cerebral oxygen metabolic rate

OBJECTIVE: Regional cerebral blood flow (CBF), cerebral blood volume, oxygen extraction fraction (OEF), and cerebral metabolic rate of oxygen (CMRO2) can be estimated from C15O, H(2)15O, and 15O2 tracers and positron emission tomography (PET) using an autoradiographic (ARG) method. Our objective in this study was to optimize the scan time for 15O2 gas study for accurate estimation of OEF and CMRO2. \nMETHODS: We evaluated statistical noise in OEF by varying the scan time and error caused by the tissue heterogeneity in estimated OEF and CMRO2 using computer simulations. The characteristics of statistical noise were investigated by signal-to-noise (S/N) ratio from repeated tissue time activity curves with noise, which were generated using measured averaged arterial input function and assuming CBF=20, 50, and 80 (ml/100 g per minute). Error caused by tissue heterogeneity was also investigated by estimated OEF and CMRO2 from tissue time activity curve with mixture of gray and white matter varying fraction of mixture. In the simulations, three conditions were assumed (i) CBF in gray and white matter (CBFg and CBFw) was 80 and 20, OEF in gray and white matter (Eg and Ew) was 0.4 and 0.3, (ii) CBFg and CBFw decreased by 50%, and Eg and Ew increased by 50% when compared with conditions (i) and (iii). CBFg and CBFw decreased by 80%, and Eg and Ew increased by 50% when compared with condition (i). \nRESULTS: The longer scan time produced the better S/N ratio of estimated OEF value from three CBF values (20, 50, and 80). Errors of estimated OEF for three conditions owing to tissue heterogeneity decreased, as scan time took longer. Meanwhile in the case of CMRO2, 3 min of scan time was desirable. \nCONCLUSIONS: The optimal scan time of 15O2 inhalation study with the ARG method was concluded to be 3 min from taking into account for maintaining the S/N ratio and the quantification of accurate OEF and CMRO2

Biomathematical screening of amyloid radiotracers with clinical usefulness index

Introduction: To facilitate radiotracers’ development, a screening methodology using a biomathematicalmodel and clinical usefulness index (CUI) was proposed to evaluate radiotracers’ diagnosticcapabilities.Methods: A total of 31 amyloid positron emission tomography radiotracers were evaluated. A previouslydeveloped biomathematical model was used to simulate 1000 standardized uptake valueratios with population and noise simulations, which were used to determine the integrated receiveroperating characteristics curve (Az), effect size (Es), and standardized uptake value ratio (Sr) ofconditions-pairs of healthy control–mild cognitive impaired and mild cognitive impaired–Alzheimer’s disease. CUI was obtained from the product of averaged AzðAzÞ, EsðEsÞ, and SrðSrÞ.Results: The relationships of Az, Es, and Sr with CUI were different, suggesting that they assesseddifferent radiotracer properties. The combination of Az, Es, and Sr complemented each other and resultedin CUI of 0.10 to 5.72, with clinically applied amyloid positron emission tomography radiotracershaving CUI greater than 3.0.Discussion: The CUI rankings of clinically applied radiotracers were close to their reported clinicalresults, attesting to the applicability of the screening methodology

PET kinetic analysis --- compartmental model

PET enables not only visualization of the distribution of radiotracer, but also has ability to quantify several biomedical functions. Compartmental model is a basic idea to analyze dynamic PET data. This review describes the principle of the compartmental model and categorizes the techniques and approaches for the compartmental model according to various aspects: model design, experimental design, invasiveness, and mathematical solution. We also discussed advanced applications of the compartmental analysis with PET

PET kinetic analysis --- Pitfalls and a solution for the Logan plot

The Logan plot is a widely used algorithm for the quantitative analysis of neuroreceptors using PET because it is easy to use and simple to implement. The Logan plot is also suitable for receptor imaging because its algorithm is fast. However, use of the Logan plot, and interpretation of the formed receptor images should be regarded with caution, because noise in PET data causes bias in the Logan plot estimates. In this paper, we describe the basic concept of the Logan plot in detail and introduce three algorithms for the Logan plot. By comparing these algorithms, we demonstrate the pitfalls of the Logan plot and discuss the solution