Multi-scale analysis of lung computed tomography images

A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on a dataset of low-dose and thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.Comment: 18 pages, 12 low-resolution figure

Focal Spot, Fall/Winter 1996

https://digitalcommons.wustl.edu/focal_spot_archives/1071/thumbnail.jp

Detection of pulmonary nodules by computer-aided diagnosis in multidetector computed tomography: preliminary study of 24 cases

OBJECTIVES: To evaluate the performance of a computer program designed to facilitate the detection of pulmonary nodules using multidetector computed tomography (MDCT) scans of the chest. METHODS: We evaluated 24 consecutive MDCT scans of the chest at the Fleury Diagnostic Imaging Center during the period from October 7 to October 19 of 2006, using a 64-channel CT scanner. The study comprised 12 females and 12 males, ranging from 35 to 77 years of age (mean, 57.9 years). Double reading and a computer-aided diagnosis (CAD) system were used in order to perform two independent analyses of the data. The nodules found using both methods were recorded, and the data were compared. RESULTS: The total sensitivity of CAD for the detection of nodules was 16.5%, increasing to 55% when nodules 1 cm. More than 99% of true nodules detected by CAD were registered in the image double reading process. CONCLUSIONS: In this preliminary 24-case study, the sensitivity of computer program tested was not significantly greater than that of the double-reading process that is routinely performed in this facility.OBJETIVOS: Avaliar o desempenho de um programa para auxílio na detecção de nódulos pulmonares em tomografia computadorizada com múltiplos detectores (TCMD). MÉTODOS: Foram avaliadas 24 tomografias computadorizadas de tórax consecutivas realizadas no Centro de Medicina Diagnóstica Fleury no período de 07/10/2006 a 19/10/2006 usando um tomógrafo helicoidal multidetectores de 64 canais. O estudo compreendeu 12 pacientes do sexo feminino e 12 do sexo masculino, com idades variando entre 35 e 77 anos, idade média de 57,9. As imagens foram analisadas independentemente pelo método da dupla leitura e pelo programa diagnóstico auxiliado por computador (DAC). Os nódulos encontrados nos diferentes processos foram registrados e os dados comparados. RESULTADOS: A sensibilidade total da detecção de nódulos pelo DAC nesse trabalho foi de 16,5%, 55% excluindo os nódulos medindo 1 cm. Menos de 1% dos nódulos verdadeiros destacados pelo DAC não haviam sido registrados no processo de dupla leitura. CONCLUSÕES: Neste trabalho preliminar de 24 casos, o programa testado não conseguiu superar de forma significativa a sensibilidade da dupla leitura realizada de rotina neste serviço.Universidade Federal de São Paulo (UNIFESP) Departamento de Diagnóstico por ImagemCentro de Medicina Diagnóstica FleuryUniversidade Federal de São Paulo (UNIFESP)UNIFESP, Depto. de Diagnóstico por ImagemUNIFESPSciEL

A novel ultrafast-low-dose computed tomography protocol allows concomitant coronary artery evaluation and lung cancer screening

BACKGROUND:Cardiac computed tomography (CT) is often performed in patients who are at high risk for lung cancer in whom screening is currently recommended. We tested diagnostic ability and radiation exposure of a novel ultra-low-dose CT protocol that allows concomitant coronary artery evaluation and lung screening. METHODS: We studied 30 current or former heavy smoker subjects with suspected or known coronary artery disease who underwent CT assessment of both coronary arteries and thoracic area (Revolution CT, General Electric). A new ultrafast-low-dose single protocol was used for ECG-gated helical acquisition of the heart and the whole chest. A single IV iodine bolus (70-90 ml) was used. All patients with CT evidence of coronary stenosis underwent also invasive coronary angiography. RESULTS: All the coronary segments were assessable in 28/30 (93%) patients. Only 8 coronary segments were not assessable in 2 patients due to motion artefacts (assessability: 98%; 477/485 segments). In the assessable segments, 20/21 significant stenoses (> 70% reduction of vessel diameter) were correctly diagnosed. Pulmonary nodules were detected in 5 patients, thus requiring to schedule follow-up surveillance CT thorax. Effective dose was 1.3 ± 0.9 mSv (range: 0.8-3.2 mSv). Noteworthy, no contrast or radiation dose increment was required with the new protocol as compared to conventional coronary CT protocol. CONCLUSIONS:The novel ultrafast-low-dose CT protocol allows lung cancer screening at time of coronary artery evaluation. The new approach might enhance the cost-effectiveness of coronary CT in heavy smokers with suspected or known coronary artery disease

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Automatic 3D pulmonary nodule detection in CT images: a survey

This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images. Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks