Curtise K.C. Ng

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Senior Lecturer

Curtin University

Australia

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About

Dr. Curtise K.C. Ng is a Senior Lecturer at Curtin University in Australia. His research interests encompass medical radiation science education, deep learning applications in radiation therapy, and workplace safety within medical contexts. He has contributed to various studies focusing on innovative approaches to imaging education and advanced radiotherapy planning techniques.

Recent Grants

Grant: Close

Virtually There-Assisted Reality for X-ray Operators

Open Date: 2023-01-01

Close Date: 2023-01-01

Grant: Close

Comparison of Performance of Micro-Computed Tomography (Micro-CT) and Synchrotron Radiation CT in Assessment of Coronary Stenosis Caused by Calcified Plaques

Open Date: 2022-01-01

Close Date: 2023-01-01

Grant: Close

Use of Assisted Reality for Western Australian X-ray Operators' Continuing Professional Development: A Pilot Study

Open Date: 2022-01-01

Close Date: 2023-01-01

Grant: Close

Use of Synchrotron Radiation to Accurately Assess Coronary Artery Stenosis Caused by Calcified Plaques

Open Date: 2018-01-01

Close Date: 2018-01-01

Grant: Close

Synchrotron Radiation Imaging of Aortic Stent Grafts with Different Materials

Open Date: 2018-01-01

Close Date: 2018-01-01

Articles (20)

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2021

Collaborators (1)

Zhonghua Sun

Curtin University

AUSTRALIA

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Curtise K.C. Ng

Research Interests

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Contact this professor

About

Recent Grants

Articles (20)

Innovative Hands-On Approach for Magnetic Resonance Imaging Education of an Undergraduate Medical Radiation Science Course in Australia: A Feasibility Study

Reply to Hårdemark, B. Misconceptions Yield Misleading Results. Comment on “Wong et al. Comparative Study of Eclipse and RayStation Multi-Criteria Optimization-Based Prostate Radiotherapy Treatment Planning Quality. Diagnostics 2024, 14, 465”

Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Prostate Cancer Radiation Therapy Planning: A Systematic Review

Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Breast Cancer Radiation Therapy Planning: A Systematic Review

Use of Smart Glasses (Assisted Reality) for Western Australian X-ray Operators’ Continuing Professional Development: A Pilot Study

Workplace violence in medical radiation science: A systematic review

Comparative Study of Eclipse and RayStation Multi-Criteria Optimization-Based Prostate Radiotherapy Treatment Planning Quality

Comparison of Performance of Micro-Computed Tomography (Micro-CT) and Synchrotron Radiation CT in Assessing Coronary Stenosis Caused by Calcified Plaques in Coronary Artery Phantoms

Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review

Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection and Diagnosis in Pediatric Radiology: A Systematic Review

Special Issue: Advanced Research in Pediatric Radiology and Nuclear Medicine

Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy

Comparative Study of Plan Robustness for Breast Radiotherapy: Volumetric Modulated Arc Therapy Plans with Robust Optimization versus Manual Flash Approach

Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy

Finetuned Super-Resolution Generative Adversarial Network (Artificial Intelligence) Model for Calcium Deblooming in Coronary Computed Tomography Angiography

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

Artificial Intelligence (Enhanced Super-Resolution Generative Adversarial Network) for Calcium Deblooming in Coronary Computed Tomography Angiography: A Feasibility Study

A review of the impact of the COVID-19 pandemic on pre-registration medical radiation science education

3D‐Printed Coronary Plaques to Simulate High Calcification in the Coronary Arteries for Investigation of Blooming Artifacts

Use of Three-Dimensional Printing in Modelling an Anatomical Structure with a High Computed Tomography Attenuation Value: A Feasibility Study

Collaborators (1)

Zhonghua Sun

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