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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...

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Sinogram-Free Low-Dose CT Reconstruction via Differentiable Radon-Regularized Optimization Unrolling.

Manas K Nag1, Sandeep Choudhary2, Dr Bethanney Janney3

  • 1Biomedical Engineering , Central University of Rajasthan, CURAJ, Ajmer, RJ, 305817, India.

Physics in Medicine and Biology
|June 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new AI method for low-dose CT scans that reconstructs images without raw data, improving diagnostic quality and patient safety. The Unrolled Radon Consistency Network (URCN) offers a practical solution for retrospective clinical settings.

Keywords:
CT image quality assessmentcomputed tomographyoptimization unrollingphysics-informed deep learningretrospective image reconstructionsinogramfree reconstruction

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Image Reconstruction

Background:

  • Reducing X-ray dose in computed tomography (CT) is crucial for patient safety.
  • Low-dose CT acquisitions often result in noise and artifacts, degrading image quality.
  • Existing physics-informed methods typically require raw projection data, which are not always available in clinical workflows.

Purpose of the Study:

  • To develop a novel reconstruction framework, the Unrolled Radon Consistency Network (URCN), for low-dose CT.
  • To enable physics-informed reconstruction without direct access to projection measurements.
  • To improve image quality in retrospective low-dose CT settings.

Main Methods:

  • URCN embeds approximate projection-domain structure as a differentiable spatial regularizer within a learned unrolled optimization architecture.
  • A parallel-beam Radon surrogate computes projection residuals to guide iterative image refinement.
  • The method operates as a geometry-aware image-domain regularizer, enabling reconstruction directly from DICOM images.

Main Results:

  • URCN achieved high image quality metrics (PSNR: 39.4 dB, SSIM: 0.929) on held-out patient data.
  • It demonstrated a statistically significant PSNR improvement (0.7 dB) over the SwinIR-CT baseline.
  • Task-based analysis showed improved spatial resolution and reduced noise, with robustness to domain shift.

Conclusions:

  • Structured differentiable gradient injection offers a viable pathway for low-dose CT reconstruction.
  • URCN provides a physically grounded approach for settings lacking raw projection data.
  • The method enhances diagnostic image quality and patient safety in retrospective clinical applications.