OBJECTIVE: The aim of this study was to prospectively test the performance and potential for clinical integration of software that automatically calculates the right-to-left ventricular (RV/LV) diameter ratio from computed tomography pulmonary angiography images.
METHODS: Using 115 computed tomography pulmonary angiography images that were positive for acute pulmonary embolism, we prospectively evaluated RV/LV ratio measurements that were obtained as follows: (1) completely manual measurement (reference standard), (2) completely automated measurement using the software, and (3 and 4) using a customized software interface that allowed 2 independent radiologists to manually adjust the automatically positioned calipers.
RESULTS: Automated measurements underestimated (P < 0.001) the reference standard (1.09 [0.25] vs1.03 [0.35]). With manual correction of the automatically positioned calipers, the mean ratio became closer to the reference standard (1.06 [0.29] by read 1 and 1.07 [0.30] by read 2), and the correlation improved (r = 0.675 to 0.872 and 0.887). The mean time required for manual adjustment (37  seconds) was significantly less than the time required to perform measurements entirely manually (100  seconds).
CONCLUSIONS: Automated CT RV/LV diameter ratio software shows promise for integration into the clinical workflow for patients with acute pulmonary embolism.
The National Alliance for Medical Image Computing (NA-MIC) was launched in 2004 with the goal of investigating and developing an open source software infrastructure for the extraction of information and knowledge from medical images using computational methods. Several leading research and engineering groups participated in this effort that was funded by the US National Institutes of Health through a variety of infrastructure grants. This effort transformed 3D Slicer from an internal, Boston-based, academic research software application into a professionally maintained, robust, open source platform with an international leadership and developer and user communities. Critical improvements to the widely used underlying open source libraries and tools-VTK, ITK, CMake, CDash, DCMTK-were an additional consequence of this effort. This project has contributed to close to a thousand peer-reviewed publications and a growing portfolio of US and international funded efforts expanding the use of these tools in new medical computing applications every year. In this editorial, we discuss what we believe are gaps in the way medical image computing is pursued today; how a well-executed research platform can enable discovery, innovation and reproducible science ("Open Science"); and how our quest to build such a software platform has evolved into a productive and rewarding social engineering exercise in building an open-access community with a shared vision.
Animal models play a critical role in the study of acute respiratory distress syndrome (ARDS) and ventilator-induced lung injury (VILI). One limitation has been the lack of a suitable method for serial assessment of acute lung injury (ALI) in vivo. In this study, we demonstrate the sensitivity of magnetic resonance imaging (MRI) to assess ALI in real time in rat models of VILI. Sprague-Dawley rats were untreated or treated with intratracheal lipopolysaccharide or PBS. After 48 h, animals were mechanically ventilated for up to 15 h to induce VILI. Free induction decay (FID)-projection images were made hourly. Image data were collected continuously for 30 min and divided into 13 phases of the ventilatory cycle to make cinematic images. Interleaved measurements of respiratory mechanics were performed using a flexiVent ventilator. The degree of lung infiltration was quantified in serial images throughout the progression or resolution of VILI. MRI detected VILI significantly earlier (3.8 ± 1.6 h) than it was detected by altered lung mechanics (9.5 ± 3.9 h, P = 0.0156). Animals with VILI had a significant increase in the Index of Infiltration (P = 0.0027), and early regional lung infiltrates detected by MRI correlated with edema and inflammatory lung injury on histopathology. We were also able to visualize and quantify regression of VILI in real time upon institution of protective mechanical ventilation. Magnetic resonance lung imaging can be utilized to investigate mechanisms underlying the development and propagation of ALI, and to test the therapeutic effects of new treatments and ventilator strategies on the resolution of ALI.
BACKGROUND: In chronic obstructive pulmonary disease, both smaller and larger airways are affected. FEV mainly reflects large airways obstruction, while the later fraction of forced exhalation reflects reduction in terminal expiratory flow. In this study, the objective was to evaluate the relationship between spirometric ratios, including the ratio of forced expiratory volume in 3 and 6 seconds (FEV/FEV), and small airways measures and gas trapping at quantitative chest CT scanning, and clinical outcomes in the Genetic Epidemiology of COPD (COPDGene) cohort.
METHODS: Seven thousand eight hundred fifty-three current and ex-smokers were evaluated for airflow obstruction by using recently defined linear iteratively derived equations of Hansen et al to determine lower limit of normal (LLN) equations for prebronchodilator FEV/FVC, FEV/FEV, FEV/FEV, and FEV/FVC. General linear and ordinal regression models were applied to the relationship between prebronchodilator spirometric and radiologic and clinical data.
RESULTS: Of the 10,311 participants included in the COPDGene phase I study, participants with incomplete quantitative CT scanning or relevant spirometric data were excluded, resulting in 7,853 participants in the present study. Of 4,386 participants with FEV/FVC greater than or equal to the LLN, 15.4% had abnormal FEV/FEV. Compared with normal FEV/FEV and FEV/FVC, abnormal FEV/FEV was associated with significantly greater gas trapping; St. George's Respiratory Questionnaire score; modified Medical Research Council dyspnea score; and BMI, airflow obstruction, dyspnea, and exercise index and with shorter 6-min walking distance (all P < .0001) but not with CT scanning evidence of emphysema.
CONCLUSIONS: Current and ex-smokers with prebronchodilator FEV/FEV less than the LLN as the sole abnormality identifies a distinct population with evidence of small airways disease in quantitative CT scanning, impaired indexes of physical function and quality of life otherwise deemed normal by using the current spirometric definition.
RATIONALE AND OBJECTIVES: We propose a novel single index for the quantification of emphysema severity based on an aggregation of multiple computed tomographic features evident in the lung parenchyma of smokers. Our goal was to demonstrate that this single index provides complementary information to the current standard measure of emphysema, percent emphysema (percent low attenuation areas [LAA%]), and may be superior in its association with clinically relevant outcomes.
MATERIALS AND METHODS: The inputs to our algorithm were objective assessments of multiple emphysema subtypes (normal tissue; panlobular; paraseptal; and mild, moderate, and severe centrilobular emphysema). We applied dimensionality reduction techniques to the emphysema quantities to find a space that maximizes the variance of these subtypes. A single emphysema severity index was then derived from a parametrization of the reduced space, and the clinical utility of the measure was explored in a large cross-sectional cohort of 8914 subjects from the COPDGene Study.
RESULTS: There was a statistically significant association between the severity index and the LAA%. Subjects with more severe chronic obstructive pulmonary disease (higher Global initiative for Obstructive Lung Disease stage) tended to have a higher computed tomography severity index. Finally, the severity index was associated with clinical outcomes such as lung function and provided a stronger association to these measures than the LAA%.
CONCLUSIONS: The method provides a single clinically relevant index that can assess the severity of emphysema and that provides information that is complimentary to the more commonly used LAA%.
Recent advances in the three-dimensional (3D) printing industry have enabled clinicians to explore the use of 3D printing in preprocedural planning, biomedical tissue modeling, and direct implantable device manufacturing. Despite the increased adoption of rapid prototyping and additive manufacturing techniques in the health-care field, many physicians lack the technical skill set to use this exciting and useful technology. Additionally, the growth in the 3D printing sector brings an ever-increasing number of 3D printers and printable materials. Therefore, it is important for clinicians to keep abreast of this rapidly developing field in order to benefit. In this Ahead of the Curve, we review the history of 3D printing from its inception to the most recent biomedical applications. Additionally, we will address some of the major barriers to wider adoption of the technology in the medical field. Finally, we will provide an initial guide to 3D modeling and printing by demonstrating how to design a personalized airway prosthesis via 3D Slicer. We hope this information will reduce the barriers to use and increase clinician participation in the 3D printing health-care sector.
Emphysema is one of the hallmarks of Chronic Obstructive Pulmonary Disorder (COPD), a devastating lung disease often caused by smoking. Emphysema appears on Computed Tomography (CT) scans as a variety of textures that correlate with disease subtypes. It has been shown that the disease subtypes and textures are linked to physiological indicators and prognosis, although neither is well characterized clinically. Most previous computational approaches to modeling emphysema imaging data have focused on supervised classification of lung textures in patches of CT scans. In this work, we describe a generative model that jointly captures heterogeneity of disease subtypes and of the patient population. We also describe a corresponding inference algorithm that simultaneously discovers disease subtypes and population structure in an unsupervised manner. This approach enables us to create image-based descriptors of emphysema beyond those that can be identified through manual labeling of currently defined phenotypes. By applying the resulting algorithm to a large data set, we identify groups of patients and disease subtypes that correlate with distinct physiological indicators.