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General General

[Possible Radiation Dose Reduction in Abdominal Plain CT Using Deep Learning Reconstruction].

In Nihon Hoshasen Gijutsu Gakkai zasshi

PURPOSE : The purposes of this study were to evaluate the low-contrast detectability of CT images assuming hepatocellular carcinoma and to determine whether dose reduction in abdominal plain CT imaging is possible.

METHODS : A Catphan 600 was imaged at 350, 250, 150, and 50 mA using an Aquilion ONE PRISM Edition (Canon) and reconstructed using deep learning reconstruction (DLR) and model-based iterative reconstruction (MBIR). A low-contrast object-specific contrast-to-noise ratio (CNRLO) was measured and compared in a 5-mm module with a CT value difference of 10 HU, assuming hepatocellular carcinoma; a visual examination was also performed. Moreover, an NPS within a uniform module was measured.

RESULTS : CNRLO was higher for DLR at all doses (1.12 at 150 mA for DLR and 1.07 at 250 mA for MBIR). On visual evaluation, DLR could detect up to 150 mA and MBIR up to 250 mA. The NPS was lower for DLR at 0.1 cycles/mm at 150 mA.

CONCLUSION : The low-contrast detection performance was better with DLR than with MBIR, indicating the possibility of dose reduction.

Onizuka Yasuhiro, Sakai Yuki, Shirasaka Takashi, Kondo Masatoshi, Kato Toyoyuki

2023-Mar-03

abdominal plain computed tomography (CT), deep learning reconstruction, low contrast detectability, radiation dose reduction

General General

Synthesis of Prospective Multiple Time Points F-18 FDG PET Images from a Single Scan Using a Supervised Generative Adversarial Network.

In Nuklearmedizin. Nuclear medicine

The cumulative activity map estimation are essential tools for patient specific dosimetry with high accuracy, which is estimated using biokinetic models instead of patient dynamic data or the number of static PET scans, owing to economical and time-consuming points of view. In the era of deep learning applications in medicine, the pix-to-pix (p2 p) GAN neural networks play a significant role in image translation between imaging modalities. In this pilot study, we extended the p2 p GAN networks to generate PET images of patients at different times according to a 60 min scan time after the injection of F-18 FDG. In this regard, the study was conducted in two sections: phantom and patient studies. In the phantom study section, the SSIM, PSNR, and MSE metric results of the generated images varied from 0.98-0.99, 31-34 and 1-2 respectively and the fine-tuned Resnet-50 network classified the different timing images with high performance. In the patient study, these values varied from 0.88-0.93, 36-41 and 1.7-2.2, respectively and the classification network classified the generated images in the true group with high accuracy. The results of phantom studies showed high values of evaluation metrics owing to ideal image quality conditions. However, in the patient study, promising results were achieved which showed that the image quality and training data number affected the network performance. This study aims to assess the feasibility of p2 p GAN network application for different timing image generation.

Karimipourfard Merhnoosh, Sina Sedigheh, Khodadai Shoshtari Fereshteh, Alavi Mehrsadat

2023-Mar-06

General General

A multivariate brain signature for reward.

In NeuroImage ; h5-index 117.0

The processing of reinforcers and punishers is crucial to adapt to an ever changing environment and its dysregulation is prevalent in mental health and substance use disorders. While many human brain measures related to reward have been based on activity in individual brain regions, recent studies indicate that many affective and motivational processes are encoded in distributed systems that span multiple regions. Consequently, decoding these processes using individual regions yields small effect sizes and limited reliability, whereas predictive models based on distributed patterns yield larger effect sizes and excellent reliability. To create such a predictive model for the processes of rewards and losses, termed the Brain Reward Signature (BRS), we trained a model to predict the signed magnitude of monetary rewards on the Monetary Incentive Delay task (MID; N = 39) and achieved a highly significant decoding performance (92% for decoding rewards versus losses). We subsequently demonstrate the generalizability of our signature on another version of the MID in a different sample (92% decoding accuracy; N = 12) and on a gambling task from a large sample (73% decoding accuracy, N = 1084). We further provided preliminary data to characterize the specificity of the signature by illustrating that the signature map generates estimates that significantly differ between rewarding and negative feedback (92% decoding accuracy) but do not differ for conditions that differ in disgust rather than reward in a novel Disgust-Delay Task (N = 39). Finally, we show that passively viewing positive and negatively valenced facial expressions loads positively on our signature, in line with previous studies on morbid curiosity. We thus created a BRS that can accurately predict brain responses to rewards and losses in active decision making tasks, and that possibly relates to information seeking in passive observational tasks.

Speer Sebastian P H, Keysers Christian, Barrios Judit Campdepadrós, Teurlings Cas J S, Smidts Ale, Boksem Maarten A S, Wager Tor D, Gazzola Valeria

2023-Mar-04

decoding, fMRI, loss, machine learning, neural signature, reward

Pathology Pathology

Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects.

In Journal of mass spectrometry and advances in the clinical lab

Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.

Rischke S, Hahnefeld L, Burla B, Behrens F, Gurke R, Garrett T J

2023-Apr

(U)HPLC (Ultra-), High pressure liquid chromatography, Biomarker Discovery Study, HILIC, Hydrophilic interaction liquid chromatography, HRMS, High resolution mass spectrometry, LC-MS, Liquid chromatography – mass spectrometry, LC-MS-Based Clinical Research, Lipidomics, MRM, Multiple reaction monitoring, Metabolomics, PCA, Principal component analysis, QA, Quality assurance, QC, Quality control, RF, Random Forest, RP, Reversed phase, SVA, Support vector machine

General General

Ensemble machine learning approach for examining critical process parameters and scale-up opportunities of microbial electrochemical systems for hydrogen peroxide production.

In Chemosphere

Hydrogen peroxide (H2O2) production in microbial electrochemical systems (MESs) is an attractive option for enabling a circular economy in the water/wastewater sector. Here, a machine learning algorithm was developed, using a meta-learning approach, to predict the H2O2 production rates in MES based on the seven input variables, including various design and operating parameters. The developed models were trained and cross-validated using the experimental data collected from 25 published reports. The final ensemble meta-learner model (combining 60 models) demonstrated a high prediction accuracy with very high R2 (0.983) and low root-mean-square error (RMSE) (0.647 kg H2O2 m-3 d-1) values. The model identified the carbon felt anode, GDE cathode, and cathode-to-anode volume ratio as the top three most important input features. Further scale-up analysis for small-scale wastewater treatment plants indicated that proper design and operating conditions could increase the H2O2 production rate to as high as 9 kg m-3 d-1.

Chung Tae Hyun, Shahidi Manjila, Mezbahuddin Symon, Dhar Bipro Ranjan

2023-Mar-04

Hydrogen peroxide, Machine learning, Meta-learning, Microbial electrochemical system, Microbial electrochemical technology

Ophthalmology Ophthalmology

Artificial intelligence in uveitis: A comprehensive review.

In Survey of ophthalmology ; h5-index 35.0

Uveitis is a disease complex characterized by intraocular inflammation of the uvea that is an important cause of blindness and social morbidity. With the dawn of artificial intelligence (AI) and machine learning integration in healthcare, their application in uveitis creates an avenue to improve screening and diagnosis. Our review identified the use of artificial intelligence in studies of uveitis and classified them as diagnosis support, finding detection, screening, and standardization of uveitis nomenclature. The overall performance of models is poor, with limited datasets and a lack of validation studies and publicly available data and codes. We conclude that AI holds great promise to assist with the diagnosis and detection of ocular findings of uveitis, but further studies and large representative datasets are needed to guarantee generalizability and fairness.

Nakayama Luis Filipe, Ribeiro Lucas Zago, Dychiao Robyn Gayle, Zamora Yuslay Fernández, Regatieri Caio Vinicius Saito, Celi Leo Anthony, Silva Paolo, Sobrin Lucia, Belfort Jr Rubens

2023-Mar-04

artificial intelligence, computer vision, deep learning, toxoplasmosis, uveitis