AIMC Topic: Tomography, X-Ray Computed

Clear Filters Showing 2561 to 2570 of 5128 articles

Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen.

European radiology
OBJECTIVES: To evaluate the usefulness of deep learning image reconstruction (DLIR) to improve the image quality of dual-energy computed tomography (DECT) of the abdomen, compared to hybrid iterative reconstruction (IR).

Artificial intelligence in computed tomography for quantifying lung changes in the era of CFTR modulators.

The European respiratory journal
BACKGROUND: Chest computed tomography (CT) remains the imaging standard for demonstrating cystic fibrosis (CF) airway structural disease . However, visual scoring systems as an outcome measure are time consuming, require training and lack high reprod...

Defining Normal Ranges of Skeletal Muscle Area and Skeletal Muscle Index in Children on CT Using an Automated Deep Learning Pipeline: Implications for Sarcopenia Diagnosis.

AJR. American journal of roentgenology
Skeletal muscle area (SMA), representing skeletal muscle cross-sectional area at the L3 vertebral level, and skeletal muscle index (SMI), representing height-normalized SMA, can serve as markers of sarcopenia. Normal SMA and SMI values have been rep...

Deep learning representations to support COVID-19 diagnosis on CT slices.

Biomedica : revista del Instituto Nacional de Salud
INTRODUCTION: The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding chara...

Segmentation of CT Lung Images Using FCM with Active Contour and CNN Classifier.

Asian Pacific journal of cancer prevention : APJCP
OBJECTIVE: Lung cancer is one of the unsafe diseases for human which reduces the patient life time. Generally, most of the lung cancers are identified after it has been spread into the lung parts and moreover it is difficult to find the lung cancer a...

Advances in computed tomography-based prognostic methods for intracerebral hemorrhage.

Neurosurgical review
Spontaneous intracerebral hemorrhage (ICH) has high morbidity and mortality. Computed tomography (CT) plays an important role in the diagnosis, treatment, and research of cerebrovascular diseases. Non-contrast CT is widely used in the clinical diagno...

A CT image feature space (CTIS) loss for restoration with deep learning-based methods.

Physics in medicine and biology
Deep learning-based methods have been widely used in medical imaging field such as detection, segmentation and image restoration. For supervised learning methods in CT image restoration, different loss functions will lead to different image qualities...

Classification of COVID-19 and Influenza Patients Using Deep Learning.

Contrast media & molecular imaging
Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019-22. As this virus is very similar to influenza in its ...

Tomographic Ultrasound Imaging in the Diagnosis of Breast Tumors under the Guidance of Deep Learning Algorithms.

Computational intelligence and neuroscience
This study was aimed to discuss the feasibility of distinguishing benign and malignant breast tumors under the tomographic ultrasound imaging (TUI) of deep learning algorithm. The deep learning algorithm was used to segment the images, and 120 patien...

An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease.

Computational intelligence and neuroscience
Lyme disease is one of the most common vector-borne infections. It typically causes cardiac illnesses, neurologic illnesses, musculoskeletal disorders, and dermatologic conditions. However, most of the time, it is poorly diagnosed due to many similar...