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Updated: Jan 19, 2026

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

Real-time algorithm for Poissonian noise reduction in low-dose fluoroscopy: performance evaluation.

A Sarno¹, E Andreozzi^2,3, D De Caro²

¹Università di Napoli, "Federico II", dip. di Fisica "E. Pancini" & INFN sez. di Napoli, Via Cintia, 80126, Naples, Italy. sarno@na.infn.it.

Biomedical Engineering Online

|September 13, 2019

Summary

This study introduces a Noise Variance Conditioned Average (NVCA) filter for enhancing fluoroscopic images. The NVCA filter effectively reduces quantum noise while preserving image quality and edges, enabling lower X-ray doses for patients.

Keywords:

Fluoroscopy NVCA Quantum noise Real-time processing VBM4D Video denoising X-ray dose reduction

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Last Updated: Jan 19, 2026

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

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Published on: September 11, 2011

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Area of Science:

Medical Imaging
Image Processing
Radiology

Background:

Quantum noise limits fluoroscopic image quality, increasing with lower X-ray doses.
Fluoroscopy video processing can improve image quality and reduce patient radiation exposure.
A novel Noise Variance Conditioned Average (NVCA) spatio-temporal filter is proposed for real-time denoising.

Purpose of the Study:

To assess the performance of the NVCA filter for real-time fluoroscopic sequence denoising.
To evaluate the NVCA filter's effectiveness in suppressing quantum noise and preserving edges.
To compare NVCA filter performance against moving average filters and the VBM4D algorithm.

Main Methods:

The NVCA filter utilizes a threshold based on local noise standard deviation to exclude noisy pixels.
Performance metrics include contrast-to-noise ratio (CNR) increment, edge blurring (FWHM), and computational time.
Evaluated NVCA filter parameters: mask size (5x5x5) and threshold (2x local noise standard deviation).

Main Results:

NVCA achieved a 10% CNR increase; VBM4D achieved 14%.
NVCA preserved edge sharpness regardless of object speed; VBM4D showed significant blurring with faster objects.
NVCA exhibited significantly lower computational time compared to VBM4D.

Conclusions:

The NVCA filter offers improved image quality over simple moving average filters and comparable quality to VBM4D.
NVCA excels at preserving edge sharpness, particularly with moving objects.
NVCA's simplicity and low computational cost make it suitable for real-time processing and hardware implementation in future fluoroscopy devices, facilitating dose reduction.