AIMC Topic: Tomography, X-Ray Computed

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Artificial intelligence-aided lytic spinal bone metastasis classification on CT scans.

International journal of computer assisted radiology and surgery
PURPOSE: Spinal bone metastases directly affect quality of life, and patients with lytic-dominant lesions are at high risk for neurological symptoms and fractures. To detect and classify lytic spinal bone metastasis using routine computed tomography ...

Deep learning of image-derived measures of body composition in pediatric, adolescent, and young adult lymphoma: association with late treatment effects.

European radiology
OBJECTIVES: The objective of this study was to translate a deep learning (DL) approach for semiautomated analysis of body composition (BC) measures from standard of care CT images to investigate the prognostic value of BC in pediatric, adolescent, an...

Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers.

European radiology
OBJECTIVES: To evaluate image quality, diagnostic acceptability, and lesion conspicuity in abdominal dual-energy CT (DECT) using deep learning image reconstruction (DLIR) compared to those using adaptive statistical iterative reconstruction-V (Asir-V...

Imaging of Neck Nodes in Head and Neck Cancers - a Comprehensive Update.

Clinical oncology (Royal College of Radiologists (Great Britain))
Cervical lymph node metastases from head and neck squamous cell cancers significantly reduce disease-free survival and worsen overall prognosis and, hence, deserve more aggressive management and follow-up. As per the eighth edition of the American Jo...

Incidental findings on CT scans for robot-assisted total joint arthroplasty.

ANZ journal of surgery
INTRODUCTION: This study documented the incidence of incidental findings from preoperative computerized-tomography (CT) scans obtained for robotic-arm-assisted total joint arthroplasty (TJA) and its effect on patient management.

New Frontiers in Oncological Imaging With Computed Tomography: From Morphology to Function.

Seminars in ultrasound, CT, and MR
The latest evolutions in Computed Tomography (CT) technology have several applications in oncological imaging. The innovations in hardware and software allow for the optimization of the oncological protocol. Low-kV acquisitions are possible thanks to...

Rapid estimation of patient-specific organ doses using a deep learning network.

Medical physics
BACKGROUND: Patient-specific organ-dose estimation in diagnostic CT examinations can provide useful insights on individualized secondary cancer risks, protocol optimization, and patient management. Current dose estimation techniques mainly rely on ti...

A quality-checked and physics-constrained deep learning method to estimate material basis images from single-kV contrast-enhanced chest CT scans.

Medical physics
BACKGROUND: Single-kV CT imaging is one of the primary imaging methods in radiology practices. However, it does not provide material basis images for some subtle lesion characterization tasks in clinical diagnosis.

A Characterization of Deep Learning Reconstruction Applied to Dual-Energy Computed Tomography Monochromatic and Material Basis Images.

Journal of computer assisted tomography
OBJECTIVE: Advancements in computed tomography (CT) reconstruction have enabled image quality improvements and dose reductions. Previous advancements have included iterative and model-based reconstruction. The latest image reconstruction advancement ...

Applications of deep learning to reduce the need for iodinated contrast media for CT imaging: a systematic review.

International journal of computer assisted radiology and surgery
PURPOSE: The usage of iodinated contrast media (ICM) can improve the sensitivity and specificity of computed tomography (CT) for many clinical indications. However, the adverse effects of ICM administration can include renal injury, life-threatening ...