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Radiographic Image Interpretation, Computer-Assisted

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Detection of Vertebral Mass and Diagnosis of Spinal Cord Compression in Computed Tomography With Deep Learning Reconstruction: Comparison With Hybrid Iterative Reconstruction.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
PURPOSE: To compare the impact of deep learning reconstruction (DLR) and hybrid-iterative reconstruction (hybrid-IR) on vertebral mass depiction, detection, and diagnosis of spinal cord compression on computed tomography (CT).

Preclinical validation of a novel deep learning-based metal artifact correction algorithm for orthopedic CT imaging.

Journal of applied clinical medical physics
PURPOSE: To validate a novel deep learning-based metal artifact correction (MAC) algorithm for CT, namely, AI-MAC, in preclinical setting with comparison to conventional MAC and virtual monochromatic imaging (VMI) technique.

Deep learning imaging reconstruction of reduced-dose 40 keV virtual monoenergetic imaging for early detection of colorectal cancer liver metastases.

European journal of radiology
OBJECTIVE: To explore whether reduced-dose (RD) gemstone spectral imaging (GSI) and deep learning image reconstruction (DLIR) of 40 keV virtual monoenergetic image (VMI) enhanced the early detection and diagnosis of colorectal cancer liver metastases...

External validation of deep learning-based automated detection algorithm for chest radiograph: practical issues in outpatient clinic.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: There have been no reports on diagnostic performance of deep learning-based automated detection (DLAD) for thoracic diseases in real-world outpatient clinic.

Deep learning-based iodine contrast-augmenting algorithm for low-contrast-dose liver CT to assess hypovascular hepatic metastasis.

Abdominal radiology (New York)
PURPOSE: To investigate the image quality and diagnostic performance of low-contrast-dose liver CT using a deep learning-based iodine contrast-augmenting algorithm (DLICA) for hypovascular hepatic metastases.

Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm.

European radiology
OBJECTIVES: To perform a comprehensive within-subject image quality analysis of abdominal CT examinations reconstructed with DLIR and to evaluate diagnostic accuracy compared to the routinely applied adaptive statistical iterative reconstruction (ASi...

Deep learning-based reconstruction can improve canine thoracolumbar magnetic resonance image quality and reduce slice thickness.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
In veterinary practice, thin-sliced thoracolumbar MRI is useful in detecting small lesions, especially in small-breed dogs. However, it is challenging due to the partial volume averaging effect and increase in scan time. Currently, deep learning-base...

A novel image deep learning-based sub-centimeter pulmonary nodule management algorithm to expedite resection of the malignant and avoid over-diagnosis of the benign.

European radiology
OBJECTIVES: With the popularization of chest computed tomography (CT) screening, there are more sub-centimeter (≤ 1 cm) pulmonary nodules (SCPNs) requiring further diagnostic workup. This area represents an important opportunity to optimize the SCPN ...

Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI.

European radiology
OBJECTIVE: This study aimed to evaluate the image quality and lesion conspicuity of the deep learning image reconstruction (DLIR) algorithm compared with standard image reconstruction algorithms on abdominal enhanced computed tomography (CT) scanning...