Multi-Modality Imaging of Atheromatous Plaques in Peripheral Arterial Disease: Integrating Molecular and Imaging Markers.

Peripheral artery disease (PAD) is a common and debilitating condition characterized by the narrowing of the limb arteries, primarily due to atherosclerosis. Non-invasive multi-modality imaging approaches using computed tomography (CT), magnetic resonance imaging (MRI), and nuclear imaging have emerged as valuable tools for assessing PAD atheromatous plaques and vessel walls. This review provides an overview of these different imaging techniques, their advantages, limitations, and recent advancements. In addition, this review highlights the importance of molecular markers, including those related to inflammation, endothelial dysfunction, and oxidative stress, in PAD pathophysiology. The potential of integrating molecular and imaging markers for an improved understanding of PAD is also discussed. Despite the promise of this integrative approach, there remain several challenges, including technical limitations in imaging modalities and the need for novel molecular marker discovery and validation. Addressing these challenges and embracing future directions in the field will be essential for maximizing the potential of molecular and imaging markers for improving PAD patient outcomes.

Improved amyloid burden quantification with nonspecific estimates using deep learning.

PURPOSE: Standardized uptake value ratio (SUVr) used to quantify amyloid-ß burden from amyloid-PET scans can be biased by variations in the tracer's nonspecific (NS) binding caused by the presence of cerebrovascular disease (CeVD). In this work, we propose a novel amyloid-PET quantification approach that harnesses the intermodal image translation capability of convolutional networks to remove this undesirable source of variability. METHODS: Paired MR and PET images exhibiting very low specific uptake were selected from a Singaporean amyloid-PET study involving 172 participants with different severities of CeVD. Two convolutional neural networks (CNN), ScaleNet and HighRes3DNet, and one conditional generative adversarial network (cGAN) were trained to map structural MR to NS PET images. NS estimates generated for all subjects using the most promising network were then subtracted from SUVr images to determine specific amyloid load only (SAßL). Associations of SAßL with various cognitive and functional test scores were then computed and compared to results using conventional SUVr. RESULTS: Multimodal ScaleNet outperformed other networks in predicting the NS content in cortical gray matter with a mean relative error below 2%. Compared to SUVr, SAßL showed increased association with cognitive and functional test scores by up to 67%. CONCLUSION: Removing the undesirable NS uptake from the amyloid load measurement is possible using deep learning and substantially improves its accuracy. This novel analysis approach opens a new window of opportunity for improved data modeling in Alzheimer's disease and for other neurodegenerative diseases that utilize PET imaging.

Head-to-head comparison of amplified plasmonic exosome Aß42 platform and single-molecule array immunoassay in a memory clinic cohort.

BACKGROUND AND PURPOSE: Various blood biomarkers reflecting brain amyloid-ß (Aß) load have recently been proposed with promising results. However, to date, no comparative study amongst blood biomarkers has been reported. Our objective was to examine the diagnostic performance and cost effectiveness of three blood biomarkers on the same cohort. METHODS: Using the same cohort (n = 68), the performances of the single-molecule array (Simoa) Aß40, Aß42, Aß42/Aß40 and the amplified plasmonic exosome (APEX) Aß42 blood biomarkers were compared using amyloid positron emission tomography (PET) as the reference standard. The extent to which these blood tests can reduce the recruitment cost of clinical trials was also determined by identifying amyloid positive (Aß+) participants. RESULTS: Compared to Simoa biomarkers, APEX-Aß42 showed significantly higher correlations with amyloid PET retention values and excellent diagnostic performance (sensitivity 100%, specificity 93.3%, area under the curve 0.995). When utilized for clinical trial recruitment, our simulation showed that pre-screening with blood biomarkers followed by a confirmatory amyloid PET imaging would roughly half the cost (56.8% reduction for APEX-Aß42 and 48.6% for Simoa-Aß42/Aß40) compared to the situation where only PET imaging is used. Moreover, with 100% sensitivity, APEX-Aß42 pre-screening does not increase the required number of initial participants. CONCLUSIONS: With its high diagnostic performance, APEX is an ideal candidate for Aß+ subject identification, monitoring and primary care screening, and could efficiently enrich clinical trials with Aß+ participants whilst halving recruitment costs.

Plasma P-tau181 to Aß42 ratio is associated with brain amyloid burden and hippocampal atrophy in an Asian cohort of Alzheimer's disease patients with concomitant cerebrovascular disease.

INTRODUCTION: There is increasing evidence that phosphorylated tau (P-tau181) is a specific biomarker for Alzheimer's disease (AD) pathology, but its potential utility in non-White patient cohorts and patients with concomitant cerebrovascular disease (CeVD) is unknown. METHODS: Single molecule array (Simoa) measurements of plasma P-tau181, total tau, amyloid beta (Aß)40 and Aß42, as well as derived ratios were correlated with neuroimaging modalities indicating brain amyloid (Aß+), hippocampal atrophy, and CeVD in a Singapore-based cohort of non-cognitively impaired (NCI; n = 43), cognitively impaired no dementia (CIND; n = 91), AD (n = 44), and vascular dementia (VaD; n = 22) subjects. RESULTS: P-tau181/Aß42 ratio showed the highest area under the curve (AUC) for Aß+ (AUC = 0.889) and for discriminating between AD Aß+ and VaD Aß- subjects (AUC = 0.903). In addition, P-tau181/Aß42 ratio was associated with hippocampal atrophy. None of the biomarkers was associated with CeVD. DISCUSSION: Plasma P-tau181/Aß42 ratio may be a noninvasive means of identifying AD with elevated brain amyloid in populations with concomitant CeVD.

Improved quantification of amyloid burden and associated biomarker cut-off points: results from the first amyloid Singaporean cohort with overlapping cerebrovascular disease.

PURPOSE: The analysis of the [11C]PiB-PET amyloid images of a unique Asian cohort of 186 participants featuring overlapping vascular diseases raised the question about the validity of current standards for amyloid quantification under abnormal conditions. In this work, we implemented a novel pipeline for improved amyloid PET quantification of this atypical cohort. METHODS: The investigated data correction and amyloid quantification methods included motion correction, standardized uptake value ratio (SUVr) quantification using the parcellated MRI (standard method) and SUVr quantification without MRI. We introduced a novel amyloid analysis method yielding 2 biomarkers: AßL which quantifies the global Aß burden and ns that characterizes the non-specific uptake. Cut-off points were first determined using visual assessment as ground truth and then using unsupervised classification techniques. RESULTS: Subject's motion impacts the accuracy of the measurement outcome but has however a limited effect on the visual rating and cut-off point determination. SUVr computation can be reliably performed for all the subjects without MRI parcellation while, when required, the parcellation failed or was of mediocre quality in 10% of the cases. The novel biomarker AßL showed an association increase of 29.5% with the cognitive tests and increased effect size between positive and negative scans compared with SUVr. ns was found sensitive to cerebral microbleeds, white matter hyperintensity, volume, and age. The cut-off points for SUVr using parcellated MRI, SUVr without parcellation, and AßL were 1.56, 1.39, and 25.5. Finally, k-means produced valid cut-off points without the requirement of visual assessment. CONCLUSION: The optimal processing for the amyloid quantification of this atypical cohort allows the quantification of all the subjects, producing SUVr values and two novel biomarkers: AßL, showing important increased in their association with various cognitive tests, and ns, a parameter sensitive to non-specific retention variations caused by age and cerebrovascular diseases.

Effects of fitting methods, high b-values and image quality on diffusion and perfusion quantification and reproducibility in the calf.

PURPOSES: The study aimed to optimize diffusion-weighted imaging (DWI) image acquisition and analysis protocols in calf muscles by investigating the effects of different model-fitting methods, image quality, and use of high b-value and constraints on parameters of interest (POIs). The optimized modeling methods were used to select the optimal combinations of b-values, which will allow shorter acquisition time while achieving the same reliability as that obtained using 16 b-values. METHODS: Test-retest baseline and high-quality DWI images of ten healthy volunteers were acquired on a 3T MR scanner, using 16 b-values, including a high b-value of 1200 s/mm2, and structural T1-weighted images for calf muscle delineation. Three and six different fitting methods were used to derive ADC from monoexponential (ME) model and Dd, fp, and Dp from intravoxel incoherent motion (IVIM) model, with or without the high b-value. The optimized ME and IVIM models were then used to determine the optimal combinations of b-values, obtainable with the least number of b-values, using the selection criteria of coefficient of variance (CV) ≤10% for all POIs. RESULTS: The find minimum multivariate algorithm was more flexible and yielded smaller fitting errors. The 2-steps fitting method, with fixed Dd, performed the best for IVIM model. The inclusion of high b-value reduced outliers, while constraints improved 2-steps fitting only. CONCLUSIONS: The optimal numbers of b-values for ME and IVIM models were nine and six b-values respectively. Test-retest reliability analyses showed that only ADC and Dd were reliable for calf diffusion evaluation, with CVs of 7.22% and 4.09%.

Clinical utility of PET/MRI in multiple myeloma.

Introduction: This study aimed to evaluate the clinical utility of positron emission tomography/magnetic resonance imaging (PET/MRI), especially in comparison with PET/computed tomography (CT), which has been widely used in clinical practice in multiple myeloma. Method: F-18 fluorodeoxyglucose PET/MRI and PET/ CT studies were done at baseline and when at least a partial response to treatment was achieved. These were done for newly-diagnosed myeloma patients who have not had more than 1 cycle of anti-myeloma treatment, or for relapsed and/or refractory myeloma patients before the start of next line of therapy. Results: PET/MRI correlated significantly with PET/CT, in terms of number of lesions detected, standardised uptake value (SUVmean and SUVmax, both at baseline and post-treatment. PET/MRI and PET/CT correlated with survival at baseline, but not post-treatment. Conclusion: In this study, PET/MRI was more sensitive in detecting early disease and disease resolution post-treatment, compared with PET/CT. However, PET/MRI was less sensitive in detecting lesions in the ribs, clavicle and skull.

Comparison of quantitative parameters and radiomic features as inputs into machine learning models to predict the Gleason score of prostate cancer lesions.

INTRODUCTION: The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomography (PET), as inputs into machine learning (ML) to predict the Gleason scores (GS) of detected lesions for improved PCa lesion classification. METHODS: 20 biopsy-confirmed PCa subjects underwent imaging before radical prostatectomy. A pathologist assigned GS from tumour tissue. Two radiologists and one nuclear medicine physician delineated the lesions on the mpMR and PET images, yielding 45 lesion inputs. Seven quantitative parameters were extracted from the lesions, namely T2-weighted (T2w) image intensity, apparent diffusion coefficient (ADC), transfer constant (KTRANS), efflux rate constant (Kep), and extracellular volume ratio (Ve) from mpMR images, and SUVmean and SUVmax from PET images. Eight radiomic features were selected out of 109 radiomic features from T2w, ADC and PET images. Quantitative parameters or radiomic features, with risk factors of age, prostate-specific antigen (PSA), PSA density and volume, of 45 different lesion inputs were input in different combinations into four ML models - Decision Tree (DT), Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Ensembles model (EM). RESULTS: SUVmax yielded the highest accuracy in discriminating detected lesions. Among the 4 ML models, kNN yielded the highest accuracies of 0.929 using either quantitative parameters or radiomic features with risk factors as input. CONCLUSIONS: ML models' performance is dependent on the input combinations and risk factors further improve ML classification accuracy.

Validation of deep learning-based nonspecific estimates for amyloid burden quantification with longitudinal data.

PURPOSE: To validate our previously proposed method of quantifying amyloid-beta (Aß) load using nonspecific (NS) estimates generated with convolutional neural networks (CNNs) using [18F]Florbetapir scans from longitudinal and multicenter ADNI data. METHODS: 188 paired MR (T1-weighted and T2-weighted) and PET images were downloaded from the ADNI3 dataset, of which 49 subjects had 2 time-point scans. 40 Aß- subjects with low specific uptake were selected for training. Multimodal ScaleNet (SN) and monomodal HighRes3DNet (HRN), using either T1-weighted or T2-weighted MR images as inputs) were trained to map structural MR to NS-PET images. The optimized SN and HRN networks were used to estimate the NS for all scans and then subtracted from SUVr images to determine the specific amyloid load (SAßL) images. The association of SAßL with various cognitive and functional test scores was evaluated using Spearman analysis, as well as the differences in SAßL with cognitive test scores for 49 subjects with 2 time-point scans and sensitivity analysis. RESULTS: SAßL derived from both SN and HRN showed higher association with memory-related cognitive test scores compared to SUVr. However, for longitudinal scans, only SAßL estimated from multimodal SN consistently performed better than SUVr for all memory-related cognitive test scores. CONCLUSIONS: Our proposed method of quantifying Aß load using NS estimated from CNN correlated better than SUVr with cognitive decline for both static and longitudinal data, and was able to estimate NS of [18F]Florbetapir. We suggest employing multimodal networks with both T1-weighted and T2-weighted MR images for better NS estimation.

Comparison of Three Automated Approaches for Classification of Amyloid-PET Images.

Automated amyloid-PET image classification can support clinical assessment and increase diagnostic confidence. Three automated approaches using global cut-points derived from Receiver Operating Characteristic (ROC) analysis, machine learning (ML) algorithms with regional SUVr values, and deep learning (DL) network with 3D image input were compared under various conditions: number of training data, radiotracers, and cohorts. 276 [11C]PiB and 209 [18F]AV45 PET images from ADNI database and our local cohort were used. Global mean and maximum SUVr cut-points were derived using ROC analysis. 68 ML models were built using regional SUVr values and one DL network was trained with classifications of two visual assessments - manufacturer's recommendations (gray-scale) and with visually guided reference region scaling (rainbow-scale). ML-based classification achieved similarly high accuracy as ROC classification, but had better convergence between training and unseen data, with a smaller number of training data. Naïve Bayes performed the best overall among the 68 ML algorithms. Classification with maximum SUVr cut-points yielded higher accuracy than with mean SUVr cut-points, particularly for cohorts showing more focal uptake. DL networks can support the classification of definite cases accurately but performed poorly for equivocal cases. Rainbow-scale standardized image intensity scaling and improved inter-rater agreement. Gray-scale detects focal accumulation better, thus classifying more amyloid-positive scans. All three approaches generally achieved higher accuracy when trained with rainbow-scale classification. ML yielded similarly high accuracy as ROC, but with better convergence between training and unseen data, and further work may lead to even more accurate ML methods.

Comparison of the performances of machine learning and deep learning in improving the quality of low dose lung cancer PET images.

PURPOSE: To compare the performances of machine learning (ML) and deep learning (DL) in improving the quality of low dose (LD) lung cancer PET images and the minimum counts required. MATERIALS AND METHODS: 33 standard dose (SD) PET images, were used to simulate LD PET images at seven-count levels of 0.25, 0.5, 1, 2, 5, 7.5 and 10 million (M) counts. Image quality transfer (IQT), a ML algorithm that uses decision tree and patch-sampling was compared to two DL networks-HighResNet (HRN) and deep-boosted regression (DBR). Supervised training was performed by training the ML and DL algorithms with matched-pair SD and LD images. Image quality evaluation and clinical lesion detection tasks were performed by three readers. Bias in 53 radiomic features, including mean SUV, was evaluated for all lesions. RESULTS: ML- and DL-estimated images showed higher signal and smaller error than LD images with optimal image quality recovery achieved using LD down to 5 M counts. True positive rate and false discovery rate were fairly stable beyond 5 M counts for the detection of small and large true lesions. Readers rated average or higher ratings to images estimated from LD images of count levels above 5 M only, with higher confidence in detecting true lesions. CONCLUSION: LD images with a minimum of 5 M counts (8.72 MBq for 10 min scan or 25 MBq for 3 min scan) are required for optimal clinical use of ML and DL, with slightly better but more varied performance shown by DL.

Comparison of metrics for the evaluation of medical segmentations using prostate MRI dataset.

Nine previously proposed segmentation evaluation metrics, targeting medical relevance, accounting for holes, and added regions or differentiating over- and under-segmentation, were compared with 24 traditional metrics to identify those which better capture the requirements for clinical segmentation evaluation. Evaluation was first performed using 2D synthetic shapes to highlight features and pitfalls of the metrics with known ground truths (GTs) and machine segmentations (MSs). Clinical evaluation was then performed using publicly-available prostate images of 20 subjects with MSs generated by 3 different deep learning networks (DenseVNet, HighRes3DNet, and ScaleNet) and GTs drawn by 2 readers. The same readers also performed the 2D visual assessment of the MSs using a dual negative-positive grading of -5 to 5 to reflect over- and under-estimation. Nine metrics that correlated well with visual assessment were selected for further evaluation using 3 different network ranking methods - based on a single metric, normalizing the metric using 2 GTs, and ranking the network based on a metric then averaging, including leave-one-out evaluation. These metrics yielded consistent ranking with HighRes3DNet ranked first then DenseVNet and ScaleNet using all ranking methods. Relative volume difference yielded the best positivity-agreement and correlation with dual visual assessment, and thus is better for providing over- and under-estimation. Interclass Correlation yielded the strongest correlation with the absolute visual assessment (0-5). Symmetric-boundary dice consistently yielded good discrimination of the networks for all three ranking methods with relatively small variations within network. Good rank discrimination may be an additional metric feature required for better network performance evaluation.

Validation of low-dose lung cancer PET-CT protocol and PET image improvement using machine learning.

PURPOSE: To conduct a simplified lesion-detection task of a low-dose (LD) PET-CT protocol for frequent lung screening using 30% of the effective PETCT dose and to investigate the feasibility of increasing clinical value of low-statistics scans using machine learning. METHODS: We acquired 33 SD PET images, of which 13 had actual LD (ALD) PET, and simulated LD (SLD) PET images at seven different count levels from the SD PET scans. We employed image quality transfer (IQT), a machine learning algorithm that performs patch-regression to map parameters from low-quality to high-quality images. At each count level, patches extracted from 23 pairs of SD/SLD PET images were used to train three IQT models - global linear, single tree, and random forest regressions with cubic patch sizes of 3 and 5 voxels. The models were then used to estimate SD images from LD images at each count level for 10 unseen subjects. Lesion-detection task was carried out on matched lesion-present and lesion-absent images. RESULTS: LD PET-CT protocol yielded lesion detectability with sensitivity of 0.98 and specificity of 1. Random forest algorithm with cubic patch size of 5 allowed further 11.7% reduction in the effective PETCT dose without compromising lesion detectability, but underestimated SUV by 30%. CONCLUSION: LD PET-CT protocol was validated for lesion detection using ALD PET scans. Substantial image quality improvement or additional dose reduction while preserving clinical values can be achieved using machine learning methods though SUV quantification may be biased and adjustment of our research protocol is required for clinical use.

Evaluation of Multimodal Algorithms for the Segmentation of Multiparametric MRI Prostate Images.

Prostate segmentation in multiparametric magnetic resonance imaging (mpMRI) can help to support prostate cancer diagnosis and therapy treatment. However, manual segmentation of the prostate is subjective and time-consuming. Many deep learning monomodal networks have been developed for automatic whole prostate segmentation from T2-weighted MR images. We aimed to investigate the added value of multimodal networks in segmenting the prostate into the peripheral zone (PZ) and central gland (CG). We optimized and evaluated monomodal DenseVNet, multimodal ScaleNet, and monomodal and multimodal HighRes3DNet, which yielded dice score coefficients (DSC) of 0.875, 0.848, 0.858, and 0.890 in WG, respectively. Multimodal HighRes3DNet and ScaleNet yielded higher DSC with statistical differences in PZ and CG only compared to monomodal DenseVNet, indicating that multimodal networks added value by generating better segmentation between PZ and CG regions but did not improve the WG segmentation. No significant difference was observed in the apex and base of WG segmentation between monomodal and multimodal networks, indicating that the segmentations at the apex and base were more affected by the general network architecture. The number of training data was also varied for DenseVNet and HighRes3DNet, from 20 to 120 in steps of 20. DenseVNet was able to yield DSC of higher than 0.65 even for special cases, such as TURP or abnormal prostate, whereas HighRes3DNet's performance fluctuated with no trend despite being the best network overall. Multimodal networks did not add value in segmenting special cases but generally reduced variations in segmentation compared to the same matched monomodal network.

Low dose positron emission tomography emulation from decimated high statistics: A clinical validation study.

PURPOSE: The fundamental nature of positron emission tomography (PET), as an event detection system, provides some flexibility for data handling, including retrospective data manipulation. The reorganization of acquisition data allows the emulation of new scans arising from identical radiotracer spatial distributions, but with different statistical compositions, and is especially useful for evaluating the stability and reproducibility of reconstruction algorithms or when investigating extremely low count conditions. This approach is ubiquitous in the research literature but has only been validated, from the point of view of the noise properties, with numerical simulations and phantom data. We present here the first experiment comparing PET images of the same human subjects generated with two separate injections of radiotracer, using actual low dose (LD) data to validate a randomly decimated emulation from a standard dose scan. A key point of the work is focused on the randoms fractions, which scale differently than the trues at varying activity levels. METHODS: Eleven patients with non-small cell lung cancer were enrolled in the study. Each imaging session consisted of two independent FDG-PET/CT scans: a LD scan followed by a standard dose (SD) scan. Images were first reconstructed, using filtered back-projection (FBP) and OSEM incorporating time-of-flight information and point-spread function modeling (PSFTOF), from the LD and SD datasets comprising all counts from each scanned bed position. The number of true counts was recorded for all LD scans, and independent, count-matched emulations (ELD) were reconstructed from the SD data. Noise distribution within the liver and standardized uptake value reproducibility within a population of contoured, tracer-avid lesion volumes were evaluated across scans and statistics. RESULTS: The randoms fraction estimates were 17.4 ± 1.6% (14.9-19.4) in the LD data and 42 ± 2.3% (37.1-45.5) in the SD data. Eleven lesions were identified and volumes of interest were generated with a 50% threshold isocontour for each lesion, in every image. The distributions of metabolic volumes, means and maxima defined by the contoured volumes-of-interest (VOIs) were similar between the LD and SD sets. A two-tailed, matched t-test was performed on the populations of region statistics for both LD and ELD reconstructions, and the t-statistics were 1.1 (P = 0.267) and -0.22 (P = 0.828) for the background liver VOIs and -0.54 (P = 0.603) and 0.23 (P = 0.821) for the lesion VOIs, for FBP and PSFTOF respectively. In every test, the null hypothesis that the two populations had the same mean could not be rejected at the 5% significance level. CONCLUSIONS: Our results demonstrate that clinical LD PET scans can indeed be accurately emulated by the statistical decimation of standard dose scans, and this was achieved through validation by images generated with unbiased random coincidence estimations.

Evaluation of the Feasibility of Screening Tau Radiotracers Using an Amyloid Biomathematical Screening Methodology.

The purpose of this study is to evaluate the feasibility of extending a previously developed amyloid biomathematical screening methodology to support the screening of tau radiotracers during compound development. 22 tau-related PET radiotracers were investigated. For each radiotracer, in silico MLogP, V x, and in vitro K D were input into the model to predict the in vivo K 1, k 2, and BPND under healthy control (HC), mild cognitive impaired (MCI), and Alzheimer's disease (AD) conditions. These kinetic parameters were used to simulate the time activity curves (TACs) in the target regions of HC, MCI, and AD and a reference region. Standardized uptake value ratios (SUVR) were determined from the integrated area under the TACs of the target region over the reference region within a default time window of 90-110 min. The predicted K 1, k 2, and BPND values were compared with the clinically observed values. The TACs and SUVR distributions were also simulated with population variations and noise. Finally, the clinical usefulness index (CUI) ranking was compared with clinical comparison results. The TACs and SUVR distributions differed for tau radiotracers with lower tau selectivity. The CUI values ranged from 0.0 to 16.2, with 6 out of 9 clinically applied tau radiotracers having CUI values higher than the recommend CUI value of 3.0. The differences between the clinically observed TACs and SUVR results showed that the evaluation of the clinical usefulness of tau radiotracer based on single target binding could not fully reflect in vivo tau binding. The screening methodology requires further study to improve the accuracy of screening tau radiotracers. However, the higher CUI rankings of clinically applied tau radiotracers with higher signal-to-noise ratio supported the use of the screening methodology in radiotracer development by allowing comparison of candidate radiotracers with clinically applied radiotracers based on SUVR, with respect to binding to a single target.

Investigation of the quantitative accuracy of low-dose amyloid and tau PET imaging.

With the increasing incidence of dementia worldwide, the frequent use of amyloid and tau positron emission tomography imaging requires low-dose protocols for the differential diagnoses of various neurodegenerative diseases and the monitoring of disease progression. In this study, we investigated the feasibility to reduce the PET dose without a significant loss of quantitative accuracy in 3D dynamic row action maximum likelihood algorithm-reconstructed PET images using [11C]PIB and [18F]THK5351. Eighteen cognitively normal young controls, cognitively normal elderly controls, and patients with probable Alzheimer's disease (n = 6 each), were included. Reduced doses were simulated by randomly sampling half and quarter of the full counts in list mode data for one independent realization at each simulated dose. Bias was evaluated between the reduced dose from the full dose of standardized uptake value ratio (SUVR), distribution volume ratio (DVR) from reference Logan, and non-displaceable binding potential (BPND) from simplified reference tissue model (SRTM). DVR yielded the least bias at low dose compared to SUVR and BPND, and thus, is highly recommended. The dose of [18F]THK5351 and [11C]PIB can be reduced to a quarter of the full dose using DVR for evaluation, whereas the dose can only be reduced to half and a quarter of the full dose for [18F]THK5351 and [11C]PIB using SUVR. BPND showed inconsistent trend and large bias at low dose. The feasibility of dose reduction was dependent on the selected parameters of interest, reconstruction algorithms, reference regions, and to a lesser degree by motion effects.

Biomathematical screening of amyloid radiotracers with clinical usefulness index.

INTRODUCTION: To facilitate radiotracers' development, a screening methodology using a biomathematical model and clinical usefulness index (CUI) was proposed to evaluate radiotracers' diagnostic capabilities. METHODS: A total of 31 amyloid positron emission tomography radiotracers were evaluated. A previously developed biomathematical model was used to simulate 1000 standardized uptake value ratios with population and noise simulations, which were used to determine the integrated receiver operating characteristics curve (Az), effect size (Es), and standardized uptake value ratio (Sr) of conditions-pairs of healthy control-mild cognitive impaired and mild cognitive impaired-Alzheimer's disease. CUI was obtained from the product of averaged [Formula: see text], [Formula: see text], and [Formula: see text]. RESULTS: The relationships of [Formula: see text], [Formula: see text], and [Formula: see text] with CUI were different, suggesting that they assessed different radiotracer properties. The combination of Az, Es, and Sr complemented each other and resulted in CUI of 0.10 to 5.72, with clinically applied amyloid positron emission tomography radiotracers having CUI greater than 3.0. DISCUSSION: The CUI rankings of clinically applied radiotracers were close to their reported clinical results, attesting to the applicability of the screening methodology.

137Cs transmission imaging and segmented attenuation corrections in a small animal PET scanner.

Attenuation correction (AC) is required for accurate quantitative evaluation of small animal PET data. Our objective was to compare three AC methods in the small animal Clairvivo-PET scanner. The three AC methods involve applying attenuation coefficient maps generated by simulating a cylindrical map (SAC), segmenting the emission data (ESAC), and segmenting the transmission data (TSAC), imaged using a 137Cs single-photon source. Investigation was carried out using a 65 mm uniform cylinder and an NEMA NU4 2008 mouse phantom, filled with water or tungsten liquid, to mimic bone. Evaluation was carried out using the difference of the segmented map volume from the known cylindrical phantom volume, the recovery of the radioactivity concentration, and the line profiles. The optimal transmission scan time for achieving accurate AC using TSAC was determined using 5, 10, 15, 20, and 25 min transmission scan time. The effects of scatter correction and reconstruction algorithms on ESAC were investigated. SAC showed the best performance but was unable to correct for different tissues and the scanner bed, and faced difficulty with correct positioning of the attenuation coefficient map. ESAC was affected by scatter correction and reconstruction algorithm, and may result in poor boundary delineation, and hence was unreliable. TSAC showed reasonable performance but required further optimization of the default segmentation setting. A minimum transmission scan time of 20 min is recommended for Clairvivo-PET using 137Cs source to ensure that sufficient transmission counts are obtained to generate accurate attenuation coefficient map.