4.7 Article

Improved image quality and dose reduction in abdominal CT with deep-learning reconstruction algorithm: a phantom study

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Radiology, Nuclear Medicine & Medical Imaging

Comparison of image quality between spectral photon-counting CT and dual-layer CT for the evaluation of lung nodules: a phantom study

Salim A. Si-Mohamed et al.

Summary: The study evaluated the image quality of spectral photon-counting CT (SPCCT) compared to dual-layer CT (DLCT) with different reconstruction algorithms. Results showed that SPCCT had lower noise magnitude and higher detectability for nodules compared to DLCT. SPCCT provided higher image quality and better conspicuity for both ground-glass nodules and solid nodules at different iDose(4) levels.

EUROPEAN RADIOLOGY (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data

Joel Greffier et al.

Summary: This study compared the effects of two deep learning image reconstruction (DLR) algorithms in chest CT with different clinical indications. The results showed that DLR algorithms can reduce image noise and improve lesion detectability. However, the impact of different algorithms on noise texture and spatial resolution varies.

DIAGNOSTIC AND INTERVENTIONAL IMAGING (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

iQMetrix-CT: New software for task-based image quality assessment of phantom CT images

Joel Greffier et al.

Summary: This article explains the working principle, current potential, and future applications of iQMetrix-CT software. It also discusses the calculation of three advanced metrics and their significance in evaluating reconstruction algorithms and optimizing image quality.

DIAGNOSTIC AND INTERVENTIONAL IMAGING (2022)

Article Clinical Neurology

Deep learning-based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V)

Injoong Kim et al.

Summary: This study compared the image quality of brain CT images reconstructed with deep learning-based image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V). The results showed that DLIR outperformed ASIR-V in reducing image noise and artifacts, and also demonstrated better subjective image quality scores.

NEURORADIOLOGY (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions

Le Cao et al.

Summary: The study demonstrates that deep learning image reconstruction can significantly reduce radiation dose and improve image quality in abdominal CT. Particularly in low-dose conditions, DLIR-H generates images with lower noise and higher overall image quality.

BRITISH JOURNAL OF RADIOLOGY (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with hybrid iterative reconstruction

Yasutaka Ichikawa et al.

Summary: Compared to hybrid IR, DLIR significantly reduces image noise, improves CNR and SNR, and has higher overall image quality scores for abdominal CT images.

JAPANESE JOURNAL OF RADIOLOGY (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest CT: A phantom study

Caro Franck et al.

Summary: The study assessed the performance of TrueFidelity algorithm at low doses in chest CT, showing that TrueFidelity can better preserve image texture at lower doses compared to ASIR-V.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Improving Image Quality and Reducing Radiation Dose for Pediatric CT by Using Deep Learning Reconstruction

Samuel L. Brady et al.

Summary: A study compared different reconstruction algorithms in pediatric CT, finding that the DLR algorithm showed better object detectability and image quality compared to other algorithms, with a more significant dose reduction.

RADIOLOGY (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep learning reconstruction versus iterative reconstruction for cardiac CT angiography in a stroke imaging protocol: reduced radiation dose and improved image quality

Angelique Bernard et al.

Summary: In a study of 296 patients, the use of a DLR algorithm for cardiac CCTA in an acute stroke imaging protocol significantly reduced radiation dose and improved image quality compared to an iterative reconstruction algorithm.

QUANTITATIVE IMAGING IN MEDICINE AND SURGERY (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection

Yoshifumi Noda et al.

Summary: Low-dose whole-body CT scans reconstructed with deep learning image reconstruction can achieve over 75% reduction in radiation dose while maintaining good image quality and lesion detection rate.

BRITISH JOURNAL OF RADIOLOGY (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

The image quality of deep-learning image reconstruction of chest CT images on a mediastinal window setting

A. Hata et al.

Summary: The study compared the performance of DLIR and ASiR-V on chest CT images, showing that DLIR performed better with lower noise and higher SNR and CNR at high settings, resulting in better overall image quality.

CLINICAL RADIOLOGY (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Diagnostic performance of ultra-low dose versus standard dose CT for non-traumatic abdominal emergencies

Basien Nicolan et al.

Summary: This study aimed to compare the diagnostic performance of ultra-low dose (ULD) and standard dose (STD) computed tomography (CT) for non-traumatic abdominal emergencies. The results showed that ULD-CT had inferior diagnostic performance compared to STD-CT for most abdominal conditions, except for bowel obstruction and colitis/diverticulitis detection.

DIAGNOSTIC AND INTERVENTIONAL IMAGING (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Comparison of two versions of a deep learning image reconstruction algorithm on CT image quality and dose reduction: A phantom study

Joel Greffier et al.

Summary: Compared the impact on CT image quality and dose reduction of two versions of a Deep Learning Image Reconstruction algorithm, finding that AiCE V10 showed improvements in noise magnitude, spatial resolution, and detectability compared to AiCE V8.

MEDICAL PHYSICS (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise

Joo Hee Kim et al.

Summary: This study evaluated image quality and noise of LDCT scan images reconstructed with DLIR and compared with those of images reconstructed with ASiR-V 30%. Results showed that DLIR significantly reduced image noise while maintaining superior image quality, outperforming ASiR-V 30%.

KOREAN JOURNAL OF RADIOLOGY (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

CT iterative reconstruction algorithms: a task-based image quality assessment

J. Greffier et al.

EUROPEAN RADIOLOGY (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep Learning Reconstruction at CT: Phantom Study of the Image Characteristics

Toru Higaki et al.

ACADEMIC RADIOLOGY (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT

Ramandeep Singh et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study

Joel Greffier et al.

EUROPEAN RADIOLOGY (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience

Corey T. Jensen et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2020)

Article Cardiac & Cardiovascular Systems

Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy

Dominik C. Benz et al.

JOURNAL OF CARDIOVASCULAR COMPUTED TOMOGRAPHY (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

CT dose optimization for the detection of pulmonary arteriovenous malformation (PAVM): A phantom study

J. Greffier et al.

DIAGNOSTIC AND INTERVENTIONAL IMAGING (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm

Justin Solomon et al.

MEDICAL PHYSICS (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep learning reconstruction of equilibrium phase CT images in obese patients

Motonori Akagi et al.

EUROPEAN JOURNAL OF RADIOLOGY (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep learning-based image restoration algorithm for coronary CT angiography

Fuminari Tatsugami et al.

EUROPEAN RADIOLOGY (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT

Motonori Akagi et al.

EUROPEAN RADIOLOGY (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Performance evaluation of computed tomography systems: Summary of AAPM Task Group 233

Ehsan Samei et al.

MEDICAL PHYSICS (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

The evolution of image reconstruction for CTfrom filtered back projection to artificial intelligence

Martin J. Willemink et al.

EUROPEAN RADIOLOGY (2019)

Article Computer Science, Artificial Intelligence

Deep Learning-based CT Image Reconstruction: Initial Evaluation Targeting Hypovascular Hepatic Metastases

Yuko Nakamura et al.

RADIOLOGY-ARTIFICIAL INTELLIGENCE (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Detection and characterization of focal liver lesions with ultra-low dose computed tomography in neoplastic patients

A. Larbi et al.

DIAGNOSTIC AND INTERVENTIONAL IMAGING (2018)

Review Radiology, Nuclear Medicine & Medical Imaging

Image quality in CT: From physical measurements to model observers

F. R. Verdun et al.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2015)

Article Radiology, Nuclear Medicine & Medical Imaging

Quantitative comparison of noise texture across CT scanners from different manufacturers

Justin B. Solomon et al.

MEDICAL PHYSICS (2012)