Likelihood Reconstruction Research Articles

Modus Darwin redux

How should we evaluate Darwin and Wallace's arguments for common ancestry over separate ancestry? Elliott Sober defends a likelihood reconstruction of Darwin's reasoning that he dubs modus Darwin: similarity, therefore common ancestry. One assumption of Sober's approach is that separate ancestors have traits that are probabilistically independent. I motivate an objection to this assumption by appeal to 19th century naturalist alternatives such as those of Geoffroy and Owen. On Geoffroy and Owen's separate ancestry models, the ancestors can have traits that are probabilistically dependent. I then prove a generalization of Sober's approach that allows for similarity matching among traits to favour common ancestry over separate ancestry even when the traits of the separate ancestors are probabilistically dependent. I consider Helgeson's recent criticisms of Sober's approach and his alternative interpretation of Darwin's reasoning: more similar, hence, more recent common ancestry. I defend Sober's approach against Helgeson's objections.

Semiparametric maximum likelihood reconstruction of stochastic differential equations driven by white and correlated noise.

A semiparametric methodology for reconstructing Markovian and non-Markovian Langevin equations from time series data using unscented Kalman filtering is introduced and explored. The drift function and the logarithm of the diffusion function are expanded into sets of polynomial basis functions. In contrast to the more common state augmentation approach, the Kalman filter is here used only for state estimation and propagation; the model parameters are determined by maximum likelihood based on the predictive distribution generated by the Kalman filter. Model selection regarding the number of included drift and diffusion basis functions is performed with the Bayesian information criterion. The method is successfully validated on various simulated datasets with known system dynamics; it achieves accurate identification of drift and diffusion functions, also outside the prescribed model class, from datasets of moderate length with medium computational cost.

Optimization of penalization function in Bayesian penalized likelihood reconstruction algorithm for [18F]flutemetamol amyloid PET images.

Point-spread-function (PSF) correction is not recommended for amyloid PET images due to Gibbs artifacts. Q.Clear™, a Bayesian Penalized Likelihood (BPL) reconstruction method without incorporating PSF correction reduces these artifacts but degrades image contrast by our previous findings. The present study aimed to recover lost contrast by optimizing reconstruction parameters in time-of-flight (TOF) BPL reconstruction of amyloid PET images without PSF correction. We selected candidate conditions based on a phantom study and then determined which were optimal in a clinical study. Phantom images were reconstructed under conditions of 1‒9 iterations, β 300-1000 and γ factors from 2 to 10 in TOF-BPL without PSF correction. We evaluated the %contrast and the coefficients of variation (CV, %). Standardized uptake value ratios (SUVr) and Centiloid scales (CL) were calculated from PET images acquired from 71 participants after an [18F]flutemetamol injection. Both %contrast and CV were independent of iterations, whereas a trade-off was found between γ factors and β. We selected a γ factors of 5 without PSF correction (iterations, 1; β, 500) and of 10 without PSF correction (iterations, 1; β, 800) as candidates for clinical investigation. The SUVr and CL remained stable across various conditions, and CL scales effectively discriminated amyloid PET using measured values. The optimal reconstruction parameters of TOF-BPL for [18F]flutemetamol PET images were γ factor 10, iterations 1 and β 800, without PSF correction.

Y-90 PET/MR imaging optimization with a Bayesian penalized likelihood reconstruction algorithm.

Positron Emission Tomography (PET) imaging after Y liver radioembolization is used for both lesion identification and dosimetry. Bayesian penalized likelihood (BPL) reconstruction algorithms are an alternative to ordered subset expectation maximization (OSEM) with improved image quality and lesion detectability. The investigation of optimal parameters for Y image reconstruction of Q.Clear, a commercial BPL algorithm developed by General Electric (GE), in PET/MR is a field of interest and the subject of this study. The NEMA phantom was filled at an 8:1 sphere-to-background ratio. Acquisitions were performed on a PET/MR scanner for clinically relevant activities between 0.7 and 3.3 MBq/ml. Reconstructions with Q.Clear were performed varying the penalty parameter between 20 and 6000, the acquisition time between 5 and 20 min and pixel size between 1.56 and 4.69 mm. OSEM reconstructions of 28 subsets with 2 and 4 iterations with and without Time-of-Flight (TOF) were compared to Q.Clear with = 4000. Recovery coefficients (RC), their coefficient of variation (COV), background variability (BV), contrast-to-noise ratio (CNR) and residual activity in the cold insert were evaluated. Increasing parameter lowered RC, COV and BV, while CNR was maximized at = 4000; further increase resulted in oversmoothing. For quantification purposes, = 1000-2000 could be more appropriate. Longer acquisition times resulted in larger CNR due to reduced image noise. Q.Clear reconstructions led to higher CNR than OSEM. A of 4000 was obtained for optimal image quality, although lower values could be considered for quantification purposes. An optimal acquisition time of 15 min was proposed considering its clinical use.

Anatomically Guided PET Image Reconstruction Using Conditional Weakly-Supervised Multi-Task Learning Integrating Self-Attention.

To address the lack of high-quality training labels in positron emission tomography (PET) imaging, weakly-supervised reconstruction methods that generate network-based mappings between prior images and noisy targets have been developed. However, the learned model has an intrinsic variance proportional to the average variance of the target image. To suppress noise and improve the accuracy and generalizability of the learned model, we propose a conditional weakly-supervised multi-task learning (MTL) strategy, in which an auxiliary task is introduced serving as an anatomical regularizer for the PET reconstruction main task. In the proposed MTL approach, we devise a novel multi-channel self-attention (MCSA) module that helps learn an optimal combination of shared and task-specific features by capturing both local and global channel-spatial dependencies. The proposed reconstruction method was evaluated on NEMA phantom PET datasets acquired at different positions in a PET/CT scanner and 26 clinical whole-body PET datasets. The phantom results demonstrate that our method outperforms state-of-the-art learning-free and weakly-supervised approaches obtaining the best noise/contrast tradeoff with a significant noise reduction of approximately 50.0% relative to the maximum likelihood (ML) reconstruction. The patient study results demonstrate that our method achieves the largest noise reductions of 67.3% and 35.5% in the liver and lung, respectively, as well as consistently small biases in 8 tumors with various volumes and intensities. In addition, network visualization reveals that adding the auxiliary task introduces more anatomical information into PET reconstruction than adding only the anatomical loss, and the developed MCSA can abstract features and retain PET image details.

In silico analysis of a heme-thiolate peroxidase gene discovered in an ectomycorrhizal fungus of Carpathian primeval forest: implications for biotechnological applications

In this work, we focus on the identification of novel fungal peroxygenase gene belonging to the peroxidase-peroxygenase superfamily. We applied a metagenomic approach on soil samples from primeval forest and appropriate bioinformatics tools for analysis of obtained genomic DNA sequence. Peroxidases are ubiquitous metalloenzymes that are able to reduce reactive peroxides, mainly hydrogen peroxide, into water, whereas several substrates can be concomitantly oxidized during their catalytic reaction. Our purpose was to collect unique peroxygenase sequence data originating from a preserved biotope for a robust phylogenetic reconstruction of a particular gene family coding for highly versatile heme-thiolate peroxidases that has peculiar yet undiscovered representatives among ectomycorrhizal fungi. We identified unique DNA sequence, 812 bp long, from ectomycorrhizal Suillus species coding for a heme-thiolate peroxidase with 1 typical intron that appears distinctive for Carpathian forests. After translation in corresponding protein sequence 251 amino acids long we could identify typical signatures of this peroxygenase. On the proximal side of heme we found the conserved P-C-P triad responsible for efficient ligation of heme iron thus influencing the reactivity of this peroxidase. On the distal side we recognized the E-H-D-X-S-L motif for interaction with a stabilizing magnesium ion. Maximum likelihood reconstruction of protein phylogeny revealed with a high bootstrap support the presence of a monophyletic HTP4 clade originating in numerous Suillus representatives. Together with sister clades of edible Boletus and poisonous Paxillus containing diverse peroxygenases these newly discovered heme catalyst can be considered for application of oxyfunctionalization of organic molecules.

Optimizing scan time and bayesian penalized likelihood reconstruction algorithm in copper-64 PET/CT imaging: a phantom study

Objectives: The aim of this study was to evaluate Cu-64 PET phantom image quality using Bayesian Penalized Likelihood (BPL) and Ordered Subset Expectation Maximum with point-spread function modeling (OSEM-PSF) reconstruction algorithms. In the BPL, the regularization parameter β was varied to identify the optimum value for image quality. In the OSEM-PSF, the effect of acquisition time was evaluated to assess the feasibility of shortened scan duration. Methods: A NEMA IEC PET body phantom was filled with known activities of water soluble Cu-64. The phantom was imaged on a PET/CT scanner and was reconstructed using BPL and OSEM-PSF algorithms. For the BPL reconstruction, various β values (150, 250, 350, 450, and 550) were evaluated. For the OSEM-PSF algorithm, reconstructions were performed using list-mode data intervals ranging from 7.5 to 240 s. Image quality was assessed by evaluating the signal to noise ratio (SNR), contrast to noise ratio (CNR), and background variability (BV). Results: The SNR and CNR were higher in images reconstructed with BPL compared to OSEM-PSF. Both the SNR and CNR increased with increasing β, peaking at β = 550. The CNR for all β, sphere sizes and tumor-to-background ratios (TBRs) satisfied the Rose criterion for image detectability (CNR > 5). BPL reconstructed images with β = 550 demonstrated the highest improvement in image quality. For OSEM-PSF reconstructed images with list-mode data duration ≥ 120 s, the noise level and CNR were not significantly different from the baseline 240 s list-mode data duration. Conclusions: BPL reconstruction improved Cu-64 PET phantom image quality by increasing SNR and CNR relative to OSEM-PSF reconstruction. Additionally, this study demonstrated scan time can be reduced from 240 to 120 s when using OSEM-PSF reconstruction while maintaining similar image quality. This study provides baseline data that may guide future studies aimed to improve clinical Cu-64 imaging.

Comparison of quantitative whole body PET parameters on [68Ga]Ga-PSMA-11 PET/CT using ordered Subset Expectation Maximization (OSEM) vs. bayesian penalized likelihood (BPL) reconstruction algorithms in men with metastatic castration-resistant prostate cancer

BackgroundPSMA PET/CT is a predictive and prognostic biomarker for determining response to [177Lu]Lu-PSMA-617 in patients with metastatic castration resistant prostate cancer (mCRPC). Thresholds defined to date may not be generalizable to newer image reconstruction algorithms. Bayesian penalized likelihood (BPL) reconstruction algorithm is a novel reconstruction algorithm that may improve contrast whilst preventing introduction of image noise. The aim of this study is to compare the quantitative parameters obtained using BPL and the Ordered Subset Expectation Maximization (OSEM) reconstruction algorithms.MethodsFifty consecutive patients with mCRPC who underwent [68Ga]Ga-PSMA-11 PET/CT using OSEM reconstruction to assess suitability for [177Lu]Lu-PSMA-617 therapy were selected. BPL algorithm was then used retrospectively to reconstruct the same PET raw data. Quantitative and volumetric measurements such as tumour standardised uptake value (SUV)max, SUVmean and Molecular Tumour Volume (MTV-PSMA) were calculated on both reconstruction methods. Results were compared (Bland-Altman, Pearson correlation coefficient) including subgroups with low and high-volume disease burdens (MTV-PSMA cut-off 40 mL).ResultsThe SUVmax and SUVmean were higher, and MTV-PSMA was lower in the BPL reconstructed images compared to the OSEM group, with a mean difference of 8.4 (17.5%), 0.7 (8.2%) and − 21.5 mL (-3.4%), respectively. There was a strong correlation between the calculated SUVmax, SUVmean, and MTV-PSMA values in the OSEM and BPL reconstructed images (Pearson r values of 0.98, 0.99, and 1.0, respectively). No patients were reclassified from low to high volume disease or vice versa when switching from OSEM to BPL reconstruction.Conclusions[68Ga]Ga-PSMA-11 PET/CT quantitative and volumetric parameters produced by BPL and OSEM reconstruction methods are strongly correlated. Differences are proportional and small for SUVmean, which is used as a predictive biomarker. Our study suggests that both reconstruction methods are acceptable without clinical impact on quantitative or volumetric findings. For longitudinal comparison, committing to the same reconstruction method would be preferred to ensure consistency.

Phantom and clinical evaluation of the Bayesian penalised likelihood reconstruction algorithm Q.Clear without PSF correction in amyloid PET images

PurposeBayesian penalised likelihood (BPL) reconstruction, which incorporates point-spread-function (PSF) correction, provides higher signal-to-noise ratios and more accurate quantitation than conventional ordered subset expectation maximization (OSEM) reconstruction. However, applying PSF correction to brain PET imaging is controversial due to Gibbs artefacts that manifest as unpredicted cortical uptake enhancement. The present study aimed to validate whether BPL without PSF would be useful for amyloid PET imaging.MethodsImages were acquired from Hoffman 3D brain and cylindrical phantoms for phantom study and 71 patients administered with [18F]flutemetamol in clinical study using a Discovery MI. All images were reconstructed using OSEM, BPL with PSF correction, and BPL without PSF correction. Count profile, %contrast, recovery coefficients (RCs), and image noise were calculated from the images acquired from the phantoms. Amyloid β deposition in patients was visually assessed by two physicians and quantified based on the standardised uptake value ratio (SUVR).ResultsThe overestimated radioactivity in profile curves was eliminated using BPL without PSF correction. The %contrast and image noise decreased with increasing β values in phantom images. Image quality and RCs were better using BPL with, than without PSF correction or OSEM. An optimal β value of 600 was determined for BPL without PSF correction. Visual evaluation almost agreed perfectly (κ = 0.91–0.97), without depending on reconstruction methods. Composite SUVRs did not significantly differ between reconstruction methods.ConclusionGibbs artefacts disappeared from phantom images using the BPL without PSF correction. Visual and quantitative evaluation of [18F]flutemetamol imaging was independent of the reconstruction method. The BPL without PSF correction could be the standard reconstruction method for amyloid PET imaging, despite being qualitatively inferior to BPL with PSF correction for [18F]flutemetamol amyloid PET imaging.

Impact of [11C]methionine PET with Bayesian penalized likelihood reconstruction on glioma grades based on new WHO 2021 classification.

The uptake of [11C]methionine in positron emission tomography (PET) imaging overlapped in earlier images of tumors. Bayesian penalized likelihood (BPL) reconstruction increases the quantitative values of tumors compared with conventional ordered subset-expectation maximization (OSEM). The present study aimed to grade glioma malignancy based on the new WHO 2021 classification using [11C]methionine PET images reconstructed using BPL. We categorized 32 gliomas in 28 patients as grades 2/3 (n = 15) and 4 (n = 17) based on the WHO 2021 classification. All [11C]methionine images were reconstructed using OSEM + time-of-flight (TOF) and BPL + TOF (β = 200). Maximum standardized uptake value (SUVmax) and tumor-to-normal tissue ratio (T/Nmax) were measured at each lesion. The mean SUVmax was 4.65 and 4.93 in grade 2/3 and 6.38 and 7.11 in grade 4, and the mean T/Nmax was 7.08 and 7.22 in grade 2/3 and 9.30 and 10.19 in grade 4 for OSEM and BPL, respectively. The BPL significantly increased these values in grade 4 gliomas. The area under the receiver operator characteristic (ROC) curve (AUC) for SUVmax was the highest (0.792) using BPL. The BPL increased mean SUVmax and mean T/Nmax in lesions with higher contrast such as grade 4 glioma. The discrimination power between grades 2/3 and 4 in SUVmax was also increased using [11C]methionine PET images reconstructed with BPL.

Supplemental Transmission Aided Attenuation Correction for Quantitative Cardiac PET.

Quantitative PET attenuation correction (AC) for cardiac PET/CT and PET/MR is a challenging problem. We propose and evaluate an AC approach that uses coincidences from a relatively weak and physically fixed sparse external source, in combination with that from the patient, to reconstruct μ -maps based on physics principles alone. The low 30 cm3 volume of the source makes it easy to fill and place, and the method does not use prior image data or attenuation map assumptions. Our supplemental transmission aided maximum likelihood reconstruction of attenuation and activity (sTX-MLAA) algorithm contains an attenuation map update that maximizes the likelihood of terms representing coincidences originating from tracer in the patient and a weighted expression of counts segmented from the external source alone. Both external source and patient scatter and randoms are fully corrected. We evaluated performance of sTX-MLAA compared to reference standard CT-based AC with FDG PET/CT phantom studies; including modeling a patient with myocardial inflammation. Through an ROI analysis we measured ≤ 5 % bias in activity concentrations for PET images generated with sTX-MLAA and a TX source strength ≥ 12.7 MBq, relative to CT-AC. PET background variability (from noise and sparse sampling) was substantially reduced with sTX-MLAA compared to using counts segmented from the transmission source alone for AC. Results suggest that sTX-MLAA will enable quantitative PET during cardiac PET/CT and PET/MR of human patients.

First Report of Isolation and Characterization of Acanthamoeba spp. from the Milk Used for Calf Feeding.

Acanthamoeba spp. can be found in natural and artificial environments, which reflects their high adaptability to different conditions. Based on the available data, there is scarce information about the isolation of amoeba from milk. This study aimed to investigate the probable presence of Acanthamoeba in milk used for calf feeding. 200 milk samples from 50 industrial and traditional farms were collected. The samples were filtered and cultured on the 1.5% Non-nutrient agar medium. The amoebic growth was examined with an inverted microscope daily. DNA was extracted from the positive plates, and a PCR reaction was undertaken using the primers amplifying the Acanthamoeba 18S rRNA gene. Five samples were purified and sequenced using specific primers. Maximum likelihood reconstructions were performed using the phylogenetic program MEGA software. The osmo and thermotolerance of isolated trophozoites were examined as well. Out of 200 milk samples, Acanthamoeba was isolated from 27 (13.5%). The phylogenetic tree represents that all the isolates belonged to the genotype T4. Results of thermo and osmotolerance tests showed that isolates could develop at 37 and 43 ◦C. Besides, trophozoites survived at 0.5M mannitol and 1M. For the first time, Acanthamoeba spp. were isolated from milk used to feed dairy calves. Due to Acanthamoeba's neglected role in pathogen persistence and survival, hygiene instructions should be reconsidered.

NEMA NU 2-2018 evaluation and image quality optimization of a new generation digital 32-cm axial field-of-view Omni Legend PET-CT using a genetic evolutionary algorithm

A performance evaluation was conducted on the new General Electric (GE) digital Omni Legend PET-CT system with 32 cm extended field of view. The first commercially available clinical digital bismuth germanate system. The system does not use time of flight (ToF). Testing was performed in accordance with the NEMA NU2–2018 standard. A comparison was made between two other commercial GE scanners with extended fields of view; the Discovery MI − 6 ring (ToF enabled) and the Discovery IQ (non-ToF). A genetic evolutionary algorithm was developed to optimize image reconstruction parameters from image quality assessments. The Omni demonstrated average spatial resolutions at 1 cm radial offset as 3.9 mm FWHM. The total system sensitivity at the center was 44.36 cps/kBq. The peak NECR was measured as 501 kcps at 17.8 kBq ml−1 with a 35.48% scatter fraction. The maximum count-rate error below NECR peak was 5.5%. Using standard iterative reconstructions, sphere contrast recovery coefficients were from 52.7 ± 3.2% (10 mm) to 92.5 ± 2.4% (37 mm). The PET-CT co-registration accuracy was 2.4 mm. In place of ToF, the Omni employs software corrections through a pre-trained neural network (PDL) (trained on non-ToF to ToF) that takes Bayesian penalized likelihood reconstruction (Q.Clear) images as input. The optimum parameters for image reconstruction, determined using the genetic algorithm were a Q.Clear parameter, β, of 350 and a ‘medium’ PDL setting. Using standard iterative reconstructions, the Omni initially showed increased background variability compared to the Discovery MI. With optimized PDL reconstruction parameters selected using the genetic algorithm the performance of the Omni surpassed that of the Discovery MI on all NEMA tests. The genetic algorithm’s demonstrated ability to enhance image quality in PET-CT imaging underscores the importance of algorithm driven optimization and underscores the requirement to validate its use in the clinical setting.

Characterization of the Complete Mitochondrial Genome of the Elongate Loach and Its Phylogenetic Implications in Cobitidae.

The elongate loach is an endemic fish in China. Previous studies have provided some insights into the mitochondrial genome composition and the phylogenetic relationships of the elongate loach inferred using protein-coding genes (PCGs), yet detailed information about it remains limited. Therefore, in this study we sequenced the complete mitochondrial genome of the elongate loach and analyzed its structural characteristics. The PCGs and mitochondrial genome were used for selective stress analysis and genomic comparative analysis. The complete mitochondrial genome of the elongate loach, together with those of 35 Cyprinidae species, was used to infer the phylogenetic relationships of the Cobitidae family through maximum likelihood (ML) reconstruction. The results showed that the genome sequence has a full length of 16,591 bp, which includes 13 PCGs, 22 transfer RNA genes (tRNA), 2 ribosomal RNA genes (rRNA), and 2 non-coding regions (CR D-loop and light chain sub-chain replication origin OL). Overall, the elongate loach shared the same gene arrangement and composition of the mitochondrial genes with other teleost fishes. The Ka/Ks ratios of all mitochondrial PCGs were less than 1, indicating that all of the PCGs were evolving under purifying selection. Genome comparison analyses showed a significant sequence homology of species of Leptobotia. A significant identity between L. elongata and the other five Leptobotia species was observed in the visualization result, except for L. mantschurica, which lacked the tRNA-Arg gene and had a shorter tRNA-Asp gene. The phylogenetic tree revealed that the Cobitidae species examined here can be grouped into two clades, with the elongate loach forming a sister relationship with L. microphthalma. This study could provide additional inferences for a better understanding of the phylogenetic relationships among Cobitidae species.

Optimization of Q.Clear reconstruction for dynamic 18F PET imaging

BackgroundQ.Clear, a Bayesian penalized likelihood reconstruction algorithm, has shown high potential in improving quantitation accuracy in PET systems. The Q.Clear algorithm controls noise during the iterative reconstruction through a β penalization factor. This study aimed to determine the optimal β-factor for accurate quantitation of dynamic PET scans.MethodsA Flangeless Esser PET Phantom with eight hollow spheres (4–25 mm) was scanned on a GE Discovery MI PET/CT system. Data were reconstructed into five sets of variable acquisition times using Q.Clear with 18 different β-factors ranging from 100 to 3500. The recovery coefficient (RC), coefficient of variation (CVRC) and root-mean-square error (RMSERC) were evaluated for the phantom data. Two male patients with recurrent glioblastoma were scanned on the same scanner using 18F-PSMA-1007. Using an irreversible two-tissue compartment model, the area under curve (AUC) and the net influx rate Ki were calculated to assess the impact of different β-factors on the pharmacokinetic analysis of clinical PET brain data.ResultsIn general, RC and CVRC decreased with increasing β-factor in the phantom data. For small spheres (< 10 mm), and in particular for short acquisition times, low β-factors resulted in high variability and an overestimation of measured activity. Increasing the β-factor improves the variability, however at a cost of underestimating the measured activity. For the clinical data, AUC decreased and Ki increased with increased β-factor; a change in β-factor from 300 to 1000 resulted in a 25.5% increase in the Ki.ConclusionIn a complex dynamic dataset with variable acquisition times, the optimal β-factor provides a balance between accuracy and precision. Based on our results, we suggest a β-factor of 300–500 for quantitation of small structures with dynamic PET imaging, while large structures may benefit from higher β-factors.Trial registrationClinicaltrials.gov, NCT03951142. Registered 5 October 2019, https://clinicaltrials.gov/ct2/show/NCT03951142. EudraCT no 2018-003229-27. Registered 26 February 2019, https://www.clinicaltrialsregister.eu/ctr-search/trial/2018-003229-27/NO.

Evaluation of image resolution and quantification parameters on fdg-pet/ct images in patients with metastatic breast cancer using Q. clear and osem reconstruction techniques

We compared the 2-[18F]FDG-PET/CT scans performed for response monitoring in patients with metastatic breast cancer in a prospective setting using the ordered subset expectation maximization (OSEM) algorithm and the bayesian penalized likelihood reconstruction algorithm (Q.Clear) and the image quality and quantification parameters. 35 patients with metastatic breast cancer who were treated and followed up with 2-[18F]FDG-PET/CT were included. A total of 150 scans were evaluated on a five-point scale for the image quality parameters of noise, sharpness, contrast, diagnostic confidence, artefact, and blotchy look while being blinded to the Q.Clear and OSEM reconstruction algorithms. In scans with detectable disease, the lesion with the highest volume of interest was chosen, taking into account both reconstruction techniques' interest levels. For the same heated lesion, SULpeak (g/mL) and SUVmax (g/mL) were contrasted. The OSEM reconstruction had significantly less blotchy appearance than the Q.Clear reconstruction, while there was no significant difference between the two methods in terms of noise, diagnostic confidence, or artefact. Q.Clear had significantly better sharpness (p &amp;#60; 0.002) and contrast (p &amp;#60; 0.002) than the OSEM reconstruction. Quantitative examination of 75/150 scans revealed that Q.Clear reconstruction considerably outperformed OSEM reconstruction in terms of SULpeak (6.33 ± 1.8 vs. 5.85 ± 1.5, p &amp;#60; 0.002) and SUVmax (7.27 ± 5.8 vs. 3.90 ± 2.8, p 0.002). In conclusion, OSEM reconstruction was less blotchy, but Q.Clear reconstruction showed superior sharpness, better contrast, higher SUVmax, and higher SULpeak.

Variaciones en los parámetros cuantitativos y en la puntuación de la escala de Deauville aplicando diferentes algoritmos de reconstrucción en las imágenes PET/TC con [18F]FDG de pacientes con linfoma

Introduction and Objectives18F-FDG PET with the Deauville score (DS) is a unique semiquantitative method for lymphoma. However, type of standard uptake values (max, mean, and peak) reconstruction algorithms could affect DS. We compared the Bayesian Penalized Likelihood reconstruction algorithm (BPL) with Ordered Subsets Expectation Maximization (OSEM) on quantitative parameters and DS in lymphoma. We investigated the effect of the size of the lymph node on quantitative variation. Patients and MethodsRaw PET data of 255 lymphoma patients were reconstructed separately with Q.Clear (GE Healthcare), a BPL, and SharpIR (GE Healthcare), an OSEM algorithm. In both images, each patient's liver, mediastinal blood pool, and SUVs (SUVmax, SUVmean, and SUVpeak) of a total of 487 lesions selected from the patients were performed. DSmax, DSmean, and DSpeak were compared. ResultsIn our study, DS increased significantly with BPL (p<0.001), and the DS increased to 4-5 in 30 patients evaluated as 1-2-3 with OSEM. It was found that the quantitative values of the lymph nodes increased statistically with BPL (p<0.001), and the liver from the reference regions were significantly decreased (p<0.001). In addition, difference in lymph node was independently associated with size of lesion and was significantly more pronounced in small lesions (p<0.001). The effects of BPL algorithm were more pronounced in SUVmax than in SUVmean and SUVpeak. DS-mean and DS-peak scores were less changed by BPL than DS-max. ConclusionDifferent reconstruction algorithms in FDG PET/CT affect the quantitative evaluation. That variation may affect the change in DS in lymphoma patients, thus affecting patient management.

Novel B2 mitogenomes from Continental southern Patagonia's Late Holocene: New insights into the peopling of the Southern Cone.

The main aim of this study is to discuss the migratory processes and peopling dynamics that shaped the genetic variability of populations during the settlement of the Southern Cone, through the analysis of complete mitogenomes of individuals from southern Patagonia. Complete mitogenomes were sequenced through massively parallel sequencing from two late Holocene individuals (SAC 1-1-3 and SAC 1-1-4) buried in the same chenque at Salitroso Lake Basin (Santa Cruz province, Argentina). To evaluate matrilineal phylogenetic affinities with other haplotypes, maximum likelihood and Bayesian phylogenetic reconstructions were performed, as well as a haplotype median-joining network. The mitogenomes were assigned to haplogroups B2 and B2b, exhibiting an average depth of 54X and 89X (≥1X coverage of 98.6% and 100%), and a high number of nucleotide differences among them. The phylogenetic analyses showed a relatively close relationship between the haplotype found in SAC 1-1-4 and those retrieved from a Middle Holocene individual from Laguna Chica (Buenos Aires province), and from a group of individuals from the Peruvian coast. For the SAC 1-1-3, no clear affiliations to any other haplotype were established. The large divergence between the haplotypes presented in this study suggests either a highly variable founder gene pool, or a later enrichment by frequent biological contact with other populations. Our results underline the persistence of genetic signals related to the first waves of peopling in South America, suggesting that the regional settlement of the southern end of the continent has been much more complex than initially thought.

A retrospective evaluation of Bayesian-penalized likelihood reconstruction for [15O]H2O myocardial perfusion imaging

BackgroundNew Block-Sequential-Regularized-Expectation-Maximization (BSREM) image reconstruction technique has been introduced for clinical use mainly for oncologic use. Accurate and quantitative image reconstruction is essential in myocardial perfusion imaging with positron emission tomography (PET) as it utilizes absolute quantitation of myocardial blood flow (MBF). The aim of the study was to evaluate BSREM reconstruction for quantitation in patients with suspected coronary artery disease (CAD). Methods and ResultsWe analyzed cardiac [15O]H2O PET studies of 177 patients evaluated for CAD. Differences between BSREM and Ordered-Subset-Expectation-Maximization with Time-Of-Flight (TOF) and Point-Spread-Function (PSF) modeling (OSEM-TOF-PSF) in terms of MBF, perfusable tissue fraction, and vascular volume fraction were measured. Classification of ischemia was assessed between the algorithms. OSEM-TOF-PSF and BSREM provided similar global stress MBF in patients with ischemia (1.84 ± 0.21 g⋅ml−1⋅min−1 vs 1.86 ± 0.21 g⋅ml−1⋅min−1) and no ischemia (3.26 ± 0.34 g⋅ml−1⋅min−1 vs 3.28 ± 0.34 g⋅ml−1⋅min−1). Global resting MBF was also similar (0.97 ± 0.12 g⋅ml−1⋅min−1 and 1.12 ± 0.06 g⋅ml−1⋅min−1). The largest mean relative difference in MBF values was 7%. Presence of myocardial ischemia was classified concordantly in 99% of patients using OSEM-TOF-PSF and BSREM reconstructions ConclusionOSEM-TOF-PSF and BSREM image reconstructions produce similar MBF values and diagnosis of myocardial ischemia in patients undergoing [15O]H2O PET due to suspected obstructive coronary artery disease

The variation of quantitative parameters and Deauville scores with different reconstruction algorithms in FDG PET/CT imaging of lymphoma patients

Introduction and objectives18F-FDG PET with the Deauville score (DS) is a unique semiquantitative method for lymphoma. However, type of standard uptake values (max, mean, and peak) reconstruction algorithms could affect DS. We compared the Bayesian Penalized Likelihood reconstruction algorithm (BPL) with Ordered Subsets Expectation Maximization (OSEM) on quantitative parameters and DS in lymphoma. We investigated the effect of the size of the lymph node on quantitative variation. Patients and methodsRaw PET data of 255 lymphoma patients were reconstructed separately with Q.Clear (GE Healthcare), a BPL, and SharpIR (GE Healthcare), an OSEM algorithm. In both images, each patient's liver, mediastinal blood pool, and SUVs (SUVmax, SUVmean, and SUVpeak) of a total of 487 lesions selected from the patients were performed. DSmax, DSmean, and DSpeak were compared. ResultsIn our study, DS increased significantly with BPL (p < 0.001), and the DS increased to 4–5 in thirty patients evaluated as 1−2–3 with OSEM. It was found that the quantitative values of the lymph nodes increased statistically with BPL (p < 0.001), and the liver from the reference regions were significantly decreased (p < 0.001). In addition, difference in lymph node was independently associated with size of lesion and was significantly more pronounced in small lesions (p < 0.001). The effects of BPL algorithm were more pronounced in SUVmax than in SUVmean and SUVpeak. DS-mean and DS-peak scores were less changed by BPL than DS-max. ConclusionDifferent reconstruction algorithms in FDG PET/CT affect the quantitative evaluation. That variation may affect the change in DS in lymphoma patients, thus affecting patient management.