Dataset Information

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

ABSTRACT:

Background

Quantum noise intrinsically limits the quality of fluoroscopic images. The lower is the X-ray dose the higher is the noise. Fluoroscopy video processing can enhance image quality and allows further patient's dose lowering. This study aims to assess the performances achieved by a Noise Variance Conditioned Average (NVCA) spatio-temporal filter for real-time denoising of fluoroscopic sequences. The filter is specifically designed for quantum noise suppression and edge preservation. It is an average filter that excludes neighborhood pixel values exceeding noise statistic limits, by means of a threshold which depends on the local noise standard deviation, to preserve the image spatial resolution. The performances were evaluated in terms of contrast-to-noise-ratio (CNR) increment, image blurring (full width of the half maximum of the line spread function) and computational time. The NVCA filter performances were compared to those achieved by simple moving average filters and the state-of-the-art video denoising block matching-4D (VBM4D) algorithm. The influence of the NVCA filter size and threshold on the final image quality was evaluated too.

Results

For NVCA filter mask size of 5?×?5?×?5 pixels (the third dimension represents the temporal extent of the filter) and a threshold level equal to 2 times the local noise standard deviation, the NVCA filter achieved a 10% increase of the CNR with respect to the unfiltered sequence, while the VBM4D achieved a 14% increase. In the case of NVCA, the edge blurring did not depend on the speed of the moving objects; on the other hand, the spatial resolution worsened of about 2.2 times by doubling the objects speed with VBM4D. The NVCA mask size and the local noise-threshold level are critical for final image quality. The computational time of the NVCA filter was found to be just few percentages of that required for the VBM4D filter.

Conclusions

The NVCA filter obtained a better image quality compared to simple moving average filters, and a lower but comparable quality when compared with the VBM4D filter. The NVCA filter showed to preserve edge sharpness, in particular in the case of moving objects (performing even better than VBM4D). The simplicity of the NVCA filter and its low computational burden make this filter suitable for real-time video processing and its hardware implementation is ready to be included in future fluoroscopy devices, offering further lowering of patient's X-ray dose.

SUBMITTER: Sarno A

PROVIDER: S-EPMC6737613 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Publications

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

Sarno A A Andreozzi E E De Caro D D Di Meo G G Strollo A G M AGM Cesarelli M M Bifulco P P

Biomedical engineering online 20190911 1

<h4>Background</h4>Quantum noise intrinsically limits the quality of fluoroscopic images. The lower is the X-ray dose the higher is the noise. Fluoroscopy video processing can enhance image quality and allows further patient's dose lowering. This study aims to assess the performances achieved by a Noise Variance Conditioned Average (NVCA) spatio-temporal filter for real-time denoising of fluoroscopic sequences. The filter is specifically designed for quantum noise suppression and edge preservati ...[more]

PMID: 31511017

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Project description:PURPOSE:To evaluate the image quality of low-dose chest digital radiographic images obtained with a new spatial noise reduction algorithm, compared to a conventional de-noising technique. MATERIALS AND METHODS:In 69 patients, the dose reduction protocol was divided into A, B, and C test groups- 60% (n = 22), 50% (n = 23), and 40% (n = 24) of the baseline dose. In each patient, baseline dose radiographs were obtained with conventional image processing while low-dose images were acquired with new image processing. A set of baseline and low-dose radiographic images per patient was evaluated and scored on a 5-point scale over seven anatomical landmarks (radiolucency of unobscured lung, pulmonary vascularity, trachea, edge of rib, heart border, intervertebral disc space, and pulmonary vessels in the retrocardiac area) and three representative abnormal findings (nodule, consolidation, and interstitial marking) by two thoracic radiologists. A comparison of paired baseline and low-dose images was statistically analyzed using a non-inferiority test based on the paired t-test or the Wilcoxon signed-rank test. RESULTS:In A, B, and C test groups, the mean dose reduction rate of the baseline radiation dose was 63.4%, 53.9%, and 47.8%, respectively. In all test groups, the upper limit of the 95% confidence interval was less than the non-inferiority margin of 0.5 every seven anatomical landmarks and three representative abnormal findings, which suggested that the image quality of the low-dose image was not inferior to that of the baseline dose image even if the maximum average dose reduction rate was reduced to 47.8% of the baseline dose. CONCLUSION:In our study, an image processing technique integrating a new noise reduction algorithm achieved dose reductions of approximately half without compromising image quality for abnormal lung findings and anatomical landmarks seen on chest radiographs. This feature-preserving, noise reduction algorithm adopted in the proposed engine enables a lower radiation dose boundary for the sake of patient's and radiography technologist's radiation safety in routine clinical practice, in compliance with regulatory guidelines.

Project description:Reduction of radiation dose in x-ray imaging has been recognized as a high priority in the medical community. Here the authors show that a regional adaptive exposure method can reduce dose-area product (DAP) in x-ray fluoroscopy. The authors' method is particularly geared toward providing dose savings for the pediatric population.The scanning beam digital x-ray system uses a large-area x-ray source with 8000 focal spots in combination with a small photon-counting detector. An imaging frame is obtained by acquiring and reconstructing up to 8000 detector images, each viewing only a small portion of the patient. Regional adaptive exposure was implemented by varying the exposure of the detector images depending on the local opacity of the object. A family of phantoms ranging in size from infant to obese adult was imaged in anteroposterior view with and without adaptive exposure. The DAP delivered to each phantom was measured in each case, and noise performance was compared by generating noise arrays to represent regional noise in the images. These noise arrays were generated by dividing the image into regions of about 6 mm(2), calculating the relative noise in each region, and placing the relative noise value of each region in a one-dimensional array (noise array) sorted from highest to lowest. Dose-area product savings were calculated as the difference between the ratio of DAP with adaptive exposure to DAP without adaptive exposure. The authors modified this value by a correction factor that matches the noise arrays where relative noise is the highest to report a final dose-area product savings.The average dose-area product saving across the phantom family was (42 ± 8)% with the highest dose-area product saving in the child-sized phantom (50%) and the lowest in the phantom mimicking an obese adult (23%).Phantom measurements indicate that a regional adaptive exposure method can produce large DAP savings without compromising the noise performance in the image regions with highest noise.

Dataset Information

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

Background

Results

Conclusions

Publications

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

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