Digital Radiography, PACS, DICOM, and Artificial Intelligence Informatics — WACS Viva & Clinical Scenarios (Radiological Physics, Equipment, and Radiation Safety)

Exam-style digital radiography, pacs, dicom, and artificial intelligence informatics viva scenarios with examiner probes and model answers for Radiology…

Scenarios covered

• SCENARIO 1: Structured Physics Essay: Compare and contrast the physical architecture, signal conversion mechanisms, and spatial resolution characteristics of direct-conversion flat-panel detectors (amorphous selenium) versus indirect-conversion flat-panel detectors (cesium iodide or gadolinium oxysulfide coupled to amorphous silicon). Define Detective Quantum Efficiency (DQE) mathematically in conceptual terms and explain why digital radiography systems achieve superior DQE over conventional film-screen and computed radiography systems.
• SCENARIO 2: Physics and Instrumentation Question: Describe the operational principles of Computed Radiography (CR). Detail the composition of photostimulable phosphor (PSP) plates, the mechanism of latent image formation, photostimulated luminescence during laser scanning, optical collection, signal digitization, and plate erasure. Discuss the physical causes of latent image decay and plate artifacts.
• SCENARIO 3: Image Processing and Quality Assurance Prompt: Explain digital radiography image processing pathways from raw data acquisition to display. Detail histogram analysis, Look-Up Table (LUT) application, dynamic range compression, and spatial frequency filtering. Define the International Electrotechnical Commission (IEC) standard Exposure Index (EI), Target Exposure Index (EI-T), and Deviation Index (DI), outlining how these metrics prevent digital dose creep.
• SCENARIO 4: Departmental Informatics and Networking Question: Detail the structural architecture of a modern Picture Archiving and Communication System (PACS) and its integration with Radiology Information Systems (RIS) and Hospital Information Systems (HIS). Describe the Digital Imaging and Communications in Medicine (DICOM) standard, focusing on DICOM Information Object Definitions (IODs), Modality Worklist (MWL), Storage Commitment, and the DICOM Part 14 Grayscale Standard Display Function (GSDF).
• SCENARIO 5: Clinical Artificial Intelligence and Machine Learning Prompt: Outline the fundamental principles of deep learning and Convolutional Neural Networks (CNNs) in diagnostic radiology. Distinguish between Computer-Aided Detection (CADe), Computer-Aided Diagnosis (CADx), and workflow triage algorithms. Describe standard validation metrics including Area Under the Receiver Operating Characteristic curve (AUROC), sensitivity, specificity, and positive predictive value, highlighting key clinical pitfalls such as algorithmic bias and data overfitting.