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1.
Med Phys ; 43(7): 4023, 2016 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-27370121

RESUMO

PURPOSE: Recent new medical displays do have higher contrast and higher luminance but do not have a High Dynamic Range (HDR). HDR implies a minimum luminance value close to zero. A medical HDR display prototype based on two Liquid Crystal layers has been developed. The goal of this study is to evaluate the potential clinical benefit of such display in comparison with a low dynamic range (LDR) display. METHODS: The study evaluated the clinical performance of the displays in a search and detection task. Eight radiologists read chest x-ray images some of which contained simulated lung nodules. The study used a JAFROC (Jacknife Free Receiver Operating Characteristic) approach for analyzing FROC data. The calculated figure of merit (FoM) is the probability that a lesion is rated higher than all rated nonlesions on all images. Time per case and accuracy for locating the center of the nodules were also compared. The nodules were simulated using Samei's model. 214 CR and DR images [half were "healthy images" (chest nodule-free) and half "diseased images"] were used resulting in a total number of nodules equal to 199 with 25 images with 1 nodule, 51 images with 2 nodules, and 24 images with 3 nodules. A dedicated software interface was designed for visualizing the images for each session. For the JAFROC1 statistical analysis, the study is done per nodule category: all nodules, difficult nodules, and very difficult nodules. RESULTS: For all nodules, the averaged FoMHDR is slightly higher than FoMLDR with 0.09% of difference. For the difficult nodules, the averaged FoMHDR is slightly higher than FoMLDR with 1.38% of difference. The averaged FoMHDR is slightly higher than FoMLDR with 0.71% of difference. For the true positive fraction (TPF), both displays (the HDR and the LDR ones) have similar TPF for all nodules, but looking at difficult and very difficult nodules, there are more TP for the HDR display. The true positive fraction has been also computed in function of the local average luminance around the nodules. For the lowest luminance range, there is more than 30% in favor of the HDR display. For the highest luminance range, there is less than 6% in favor of the LDR display. CONCLUSIONS: This study shows the potential benefit of using a HDR display in radiology.


Assuntos
Apresentação de Dados , Processamento de Imagem Assistida por Computador/instrumentação , Cristais Líquidos , Radiografia Torácica/instrumentação , Calibragem , Simulação por Computador , Desenho de Equipamento , Processamento de Imagem Assistida por Computador/métodos , Pulmão/diagnóstico por imagem , Pneumopatias/diagnóstico por imagem , Modelos Anatômicos , Curva ROC , Radiografia Torácica/métodos , Fatores de Tempo , Interface Usuário-Computador
2.
J Med Imaging (Bellingham) ; 3(1): 011007, 2016 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-26587552

RESUMO

Three-dimensional (3-D) imaging has been intensively studied in the past few decades. Depth information is an important added value of 3-D systems over two-dimensional systems. Special focuses were devoted to the development of stereo matching methods for the generation of disparity maps (i.e., depth information within a 3-D scene). Dedicated frameworks were designed to evaluate and rank the performance of different stereo matching methods but never considering x-ray medical images. Yet, 3-D x-ray acquisition systems and 3-D medical displays have already been introduced into the diagnostic market. To access the depth information within x-ray stereoscopic images, computing accurate disparity maps is essential. We aimed at developing a framework dedicated to x-ray stereoscopic breast images used to evaluate and rank several stereo matching methods. A multiresolution pyramid optimization approach was integrated to the framework to increase the accuracy and the efficiency of the stereo matching techniques. Finally, a metric was designed to score the results of the stereo matching compared with the ground truth. Eight methods were evaluated and four of them [locally scaled sum of absolute differences (LSAD), zero mean sum of absolute differences, zero mean sum of squared differences, and locally scaled mean sum of squared differences] appeared to perform equally good with an average error score of 0.04 (0 is the perfect matching). LSAD was selected for generating the disparity maps.

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