Analysis of objective quality metrics in computed tomography images affected by metal artifacts.

Image quality (IQ) assessment plays an important role in the medical world. New methods to evaluate image quality have been developed, but their application in the context of computer tomography is yet limited. In this paper the performance of fifteen well-known full reference (FR) IQ metrics is compared with human judgment using images affected by metal artifacts and processed with metal artifact reduction methods from a phantom. Five region of interest with different sizes were selected. IQ was evaluated by seven experienced radiologists completely blinded to the information. To measure the correlation between FR-IQ, and the score assigned by radiologists non-parametric Spearman rank-order correlation coefficient and Kendall's Rank-order Correlation coefficient were used; so as root mean square error and the mean absolute error to measure the prediction accuracy. Cohen's kappa was employed with the purpose of assessing inter-observer agreement. The metrics GMSD, IWMSE, IWPSNR, WSNR and OSS-PSNR were the best ranked. Inter-observer agreement was between 0.596 and 0.954, with p<0.001 in all study. The objective scores predicted by these methods correlate consistently with the subjective evaluations. The application of this metrics will make possible a better evaluation of metal artifact reduction algorithms in future works.

Inpainting-filtering for metal artifact reduction (IMIF-MAR) in computed tomography.

The reduction of metal artifacts remains a challenge in computed tomography because they decrease image quality, and consequently might affect the medical diagnosis. The objective of this study is to present a novel method to correct metal artifacts based solely on the CT-slices. The proposed method consists of four steps. First, metal implants in the original CT-slice are segmented using an entropy based method, producing a metal image. Second, a prior image is acquired using three transformations: Gaussian filter, Parisotto and Schoenlieb inpainting method with the Mumford-Shah image model and L0 Gradient Minimization method (L0GM). Next, based on the projections from the original CT-slice, prior image and metal image, the sinogram is corrected in the traces affected by metal in the process called normalization and denormalization. Finally, the reconstructed image is obtained by FBP and a Nonlocal Means (NLM) filtering. The efficacy of the algorithm is evaluated by comparing five image quality metrics of the images and by inspecting regions of interest (ROI). Phantom data as well as clinical datasets are included. The proposed method is compared with three established metal artifact reduction (MAR) methods. The results from a phantom and clinical dataset show the visible reduction of artifacts. The conclusion is that IMIF-MAR method can reduce streak metal artifacts effectively and avoid new artifacts around metal implants, while preserving the anatomical structures. Considering both clinical and phantom studies, the proposed MAR algorithm improves the quality of clinical images affected by metal artifacts, and could be integrated in clinical setting.

Methods for Reducing Metal Artifacts in Computerized Tomography / Métodos para reducir los artefactos metálicos en la tomografía computarizada

Abstract Metal artifacts are common in clinical images. Many methods for artifact reduction have been published to overcome this problem. In this work, animage smoothing method for artifact reduction (ISMAR) is proposed for image quality improvement in patients with hip prosthesis and dental fillings, which caused metal artifacts. ISMAR was evaluated and compared with three well-known methods for metal artifact reduction (linear interpolation (LI), normalized metal artifact reduction (NMAR) and frequency split metal artifact reduction (FSMAR)). The new method is based on edge-preserving smoothing via L0 Gradient Minimization filter. Image quality was evaluated by two experienced radiologists completely blinded to the information about if the image was processed or not to suppress the artifacts. They graded image quality in a five points-scale, where zero is an index of clear artifact presence, and five, a whole artifact suppression. The new method had the best results and it was statistically significant respect to the other tested methods (p < 0.05). This new method has a better performance in artifact suppression and tissue feature preservation.

Resumen Los artefactos metálicos son comunes en las imágenes clínicas. Muchos métodos para la reducción de los artefactos han sido publicados para superar este problema. En el presente trabajo, un método de suavizado de imágenes para la reducción de artefactos (ISMAR) es propuesto para mejorar la calidad de la imagen en pacientes con prótesis de cadera y empastes dentales, los cuales causaron artefactos metálicos. ISMAR fue evaluado y comparado con otros tres métodos reconocidos por su desempeño en la reducción de los artefactos metálicos (Interpolación lineal (LI), reducción de artefactos de metal normalizados (NMAR) y reducción de artefactos de metal divididos en frecuencia (FSMAR)). El nuevo método se basa en el suavizado y conservación de bordes, utilizando para ello el filtro de minimización de gradiente L0. La calidad de la imagen fue evaluada por dos radiólogos experimentados completamente ciegos a la información sobre si la imagen fue procesada o no para suprimir los artefactos. Ellos calificaron la calidad de la imagen en una escala de cinco puntos, donde el cero indica la presencia de artefactos, y el cinco, una supresión total de los artefactos. El nuevo método tuvo los mejores resultados y fue estadísticamente significativo con respecto a los otros métodos probados (p < 0.05). Este nuevo método tiene un mejor rendimiento en la supresión de artefactos y en la conservación de las características de los tejidos.