AIMC Topic: Tomography, X-Ray Computed

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Cystic renal mass screening: machine-learning-based radiomics on unenhanced computed tomography.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: The present study compares the diagnostic performance of unenhanced computed tomography (CT) radiomics-based machine learning (ML) classifiers and a radiologist in cystic renal masses (CRMs).

Data-driven coordinated attention deep learning for high-fidelity brain imaging denoising and inpainting.

Journal of biophotonics
Deep learning offers promise in enhancing low-quality images by addressing weak fluorescence signals, especially in deep in vivo mouse brain imaging. However, current methods struggle with photon scarcity and noise within in vivo deep mouse brains, a...

Development of an individual display optimization system based on deep convolutional neural network transition learning for somatostatin receptor scintigraphy.

Radiological physics and technology
Somatostatin receptor scintigraphy (SRS) is an essential examination for the diagnosis of neuroendocrine tumors (NETs). This study developed a method to individually optimize the display of whole-body SRS images using a deep convolutional neural netw...

Potential diagnostic application of a novel deep learning- based approach for COVID-19.

Scientific reports
COVID-19 is a highly communicable respiratory illness caused by the novel coronavirus SARS-CoV-2, which has had a significant impact on global public health and the economy. Detecting COVID-19 patients during a pandemic with limited medical facilitie...

Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within three years for advanced NSCLC patients by using a deep learning-based segmentation and computed tom...

Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach.

BMC medical informatics and decision making
BACKGROUND: Precise prediction of esophageal squamous cell carcinoma (ESCC) invasion depth is crucial not only for optimizing treatment plans but also for reducing the need for invasive procedures, consequently lowering complications and costs. Despi...

Diagnostic value of artificial intelligence based on computed tomography (CT) density in benign and malignant pulmonary nodules: a retrospective investigation.

PeerJ
OBJECTIVE: To evaluate the diagnostic value of artificial intelligence (AI) in the detection and management of benign and malignant pulmonary nodules (PNs) using computed tomography (CT) density.

High Throughput Tomography (HiTT) on EMBL beamline P14 on PETRA III.

Journal of synchrotron radiation
Here, high-throughput tomography (HiTT), a fast and versatile phase-contrast imaging platform for life-science samples on the EMBL beamline P14 at DESY in Hamburg, Germany, is presented. A high-photon-flux undulator beamline is used to perform tomogr...

Machine Learning and CT Texture Features in Ex-smokers with no CT Evidence of Emphysema and Mildly Abnormal Diffusing Capacity.

Academic radiology
RATIONALE AND OBJECTIVES: Ex-smokers without spirometry or CT evidence of chronic obstructive pulmonary disease (COPD) but with mildly abnormal diffusing capacity of the lungs for carbon monoxide (DL) are at higher risk of developing COPD. It remains...

Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To develop and externally validate subregional radiomics for predicting therapeutic response to anti-PD1 therapy in non-small-cell lung cancer (NSCLC).