AIMC Topic: Tomography, X-Ray Computed

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Deep learning combined with radiomics may optimize the prediction in differentiating high-grade lung adenocarcinomas in ground glass opacity lesions on CT scans.

European journal of radiology
PURPOSE: Adenocarcinoma (ADC) is the most common histological subtype of lung cancers in non-small cell lung cancer (NSCLC) in which ground glass opacifications (GGOs) found on computed tomography (CT) scans are the most common lesions. However, the ...

CT kernel conversions using convolutional neural net for super-resolution with simplified squeeze-and-excitation blocks and progressive learning among smooth and sharp kernels.

Computer methods and programs in biomedicine
PURPOSE: Computed tomography (CT) volume sets reconstructed with different kernels are helping to increase diagnostic accuracy. However, several CT volumes reconstructed with different kernels are difficult to sustain, due to limited storage and main...

Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study.

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: We aimed to thoroughly characterize image quality of a novel deep learning image reconstruction (DLIR), and investigate its potential for dose reduction in abdominal CT in comparison with filtered back-projection (FBP) and a partial model-ba...

Survival prediction for oral tongue cancer patients via probabilistic genetic algorithm optimized neural network models.

The British journal of radiology
OBJECTIVES: High throughput pre-treatment imaging features may predict radiation treatment outcome and guide individualized treatment in radiotherapy (RT). Given relatively small patient sample (as compared with high dimensional imaging features), id...

Deep learning detection of prostate cancer recurrence with F-FACBC (fluciclovine, Axumin®) positron emission tomography.

European journal of nuclear medicine and molecular imaging
PURPOSE: To evaluate the performance of deep learning (DL) classifiers in discriminating normal and abnormal F-FACBC (fluciclovine, Axumin®) PET scans based on the presence of tumor recurrence and/or metastases in patients with prostate cancer (PC) a...

Low-dose CT with deep learning regularization via proximal forward-backward splitting.

Physics in medicine and biology
Low-dose x-ray computed tomography (LDCT) is desirable for reduced patient dose. This work develops new image reconstruction methods with deep learning (DL) regularization for LDCT. Our methods are based on the unrolling of a proximal forward-backwar...

Deep learning in interstitial lung disease-how long until daily practice.

European radiology
Interstitial lung diseases are a diverse group of disorders that involve inflammation and fibrosis of interstitium, with clinical, radiological, and pathological overlapping features. These are an important cause of morbidity and mortality among lung...

Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: We aimed to evaluate the performance of deep learning-based generalization of ultra-low-count amyloid PET/MRI enhancement when applied to studies acquired with different scanning hardware and protocols.

A deep learning risk prediction model for overall survival in patients with gastric cancer: A multicenter study.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Risk prediction of overall survival (OS) is crucial for gastric cancer (GC) patients to assess the treatment programs and may guide personalized medicine. A novel deep learning (DL) model was proposed to predict the risk for O...