AOtools: a Python package for adaptive optics modelling and analysis.

AOtools is a Python package that is open-source and aimed at providing tools for adaptive optics users and researchers. We present version 1.0, which contains tools for adaptive optics processing, including analysing data in the pupil plane, images and point spread functions in the focal plane, wavefront sensors, modelling of atmospheric turbulence, physical optical propagation of wavefronts, and conversion between frequently used adaptive optics and astronomical units. The main drivers behind AOtools is that it should be easy to install and use. To achieve this the project features extensive documentation, automated unit testing and is registered on the Python Package Index. AOtools is under continuous active development to expand the features available, and we encourage everyone involved in adaptive optics to become involved and contribute to the project.

Local noise estimation in low-dose chest CT images.

PURPOSE: Image noise in computed tomography (CT) images may have significant local variation due to tissue properties, dose, and location of the X-ray source. We developed and tested an automated tissue-based estimator method for estimating local noise in CT images. METHOD: An automated TBE method for estimating the local noise in CT image in 3 steps was developed: (1) Partition the image into homogeneous and transition regions, (2) For each pixel in the homogeneous regions, compute the standard deviation in a 15 x 15 x 1 voxel local region using only pixels from the same homogeneous region, and (3) Interpolate the noise estimate from the homogeneous regions in the transition regions. Noise-aware fat segmentation was implemented. Experiments were conducted on the anthropomorphic phantom and in vivo low-dose chest CT scans to validate the TBE, characterize the magnitude of local noise variation, and determine the sensitivity of noise estimates to the size of the region in which noise is computed. The TBE was tested on all scans from the Early Lung Cancer Action Program public database. The TBE was evaluated quantitatively on the phantom data and qualitatively on the in vivo data. RESULTS: The results show that noise can vary locally by over 200 Hounsfield units on low-dose in vivo chest CT scans and that the TBE can characterize these noise variations within 5 %. The new fat segmentation algorithm successfully improved segmentation on all 50 scans tested. CONCLUSION: The TBE provides a means to estimate noise for image quality monitoring, optimization of denoising algorithms, and improvement of segmentation algorithms. The TBE was shown to accurately characterize the large local noise variations that occur due to changes in material, dose, and X-ray source location.

Data sets for the qualification of volumetric CT as a quantitative imaging biomarker in lung cancer.

The drug development industry is faced with increasing costs and decreasing success rates. New ways to understand biology as well as the increasing interest in personalized treatments for smaller patient segments requires new capabilities for the rapid assessment of treatment responses. Deployment of qualified imaging biomarkers lags apparent technology capabilities. The lack of consensus methods and qualification evidence needed for large-scale multi-center trials, as well as the standardization that allows them, are widely acknowledged to be the limiting factors. The current fragmentation in imaging vendor offerings, coupled with the independent activities of individual biopharmaceutical companies and their contract research organizations (CROs), may stand in the way of the greater opportunity were these efforts to be drawn together. A preliminary report, "Volumetric CT: a potential biomarker of response," of the Quantitative Imaging Biomarkers Alliance (QIBA) activity was presented at the Medical Imaging Continuum: Path Forward for Advancing the Uses of Medical Imaging in the Development of New Biopharmaceutical Products meeting of the Extended Pharmaceutical Research and Manufacturers of America (PhRMA) Imaging Group sponsored by the Drug Information Agency (DIA) in October 2008. The clinical context in Lung Cancer and a methodology for approaching the qualification of volumetric CT as a biomarker has since been reported [Acad. Radiol. 17, 100-106, 107-115 (2010)]. This report reviews the effort to collect and utilize publicly available data sets to provide a transparent environment in which to pursue the qualification activities in such a way as to allow independent peer review and verification of results. This article focuses specifically on our role as stewards of image sets for developing new tools.

A public image database to support research in computer aided diagnosis.

The Public Lung Database to address drug response (PLD) has been developed to support research in computer aided diagnosis (CAD). Originally established for applications involving the characterization of pulmonary nodules, the PLD has been augmented to provide initial datasets for CAD research of other diseases. In general, the best performance for a CAD system is achieved when it is trained with a large amount of well documented data. Such training databases are very expensive to create and their lack of general availability limits the targets that can be considered for CAD applications and hampers development of the CAD field. The approach taken with the PLD has been to make available small datasets together with both manual and automated documentation. Furthermore, datasets with special properties are provided either to span the range of task complexity or to provide small change repeat images for direct calibration and evaluation of CAD systems. This resource offers a starting point for other research groups wishing to pursue CAD research in new directions. It also provides an on-line reference for better defining the issues relating to specific CAD tasks.

The Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) project: a resource for the development of change-analysis software.

Critical to the clinical evaluation of effective novel therapies for lung cancer is the early and accurate determination of tumor response, which requires an understanding of the sources of uncertainty in tumor measurement and subsequent attempts to minimize their effects on the assessment of the therapeutic agent. The Reference Image Database to Evaluate Response (RIDER) project seeks to develop a consensus approach to the optimization and benchmarking of software tools for the assessment of tumor response to therapy and to provide a publicly available database of serial images acquired during lung cancer drug and radiation therapy trials. Images of phantoms and patient images acquired under situations in which tumor size or biology is known to be unchanged also will be provided. The RIDER project will create standardized methods for benchmarking software tools to reduce sources of uncertainty in vital clinical assessments such as whether a specific tumor is responding to therapy.

Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation.

PURPOSE: To determine the accuracy of high-resolution computed tomographic (CT) volumetric measurements of small pulmonary nodules to assess growth and malignancy status. MATERIALS AND METHODS: The accuracy of three-dimensional (3D) image extraction and isotropic resampling techniques was assessed by performing three experiments. The first experiment measured volumes in spherical synthetic nodules of two diameters (3.20 and 3.96 mm), the second measured deformable silicone synthetic nodules prior to and after their shape had been altered markedly, and the third measured nodules of various shapes and sizes. Three-dimensional techniques were used to assess growth in 13 patients for whom the final diagnosis was known and whose initial nodule diameters were less than 10 mm. By using the exponential growth model and the calculated nodule volume at two points in time, the doubling time for each subject was calculated. RESULTS: The three synthetic nodule studies revealed that the volume could be measured accurately to within +/-3%. All five malignant nodules grew, and all had doubling times less than 177 days. Some malignant nodules had asymmetric patterns of growth identified by using the 3D techniques but not the two-dimensional methods. All eight benign nodules had doubling times of 396 days or greater or showed a decrease in volume. CONCLUSION: CT volumetric measurements are highly accurate for determining volume and are useful in assessing growth of small nodules and calculating their doubling times.

Computer-aided diagnosis for lung cancer.

CAD methods may provide radiologists with tools to obtain more accurate diagnoses for lung cancer. Considerable effort has been devoted to developing CAD tools for CXR; however, these are limited by the fundamental constraints of the projective CXR modality. CT provides far more detailed information that can be exploited better by CAD systems. There has been very little work done in this area to date, although the basic technology has already been developed through the more extensive research in the computer vision areas supported by industry and the military. Initial prototype CT CAD systems have been described that are highly effective in detecting small pulmonary nodules and in predicting malignancy of nodules. CT is now achieving momentum in the study of lung cancer. It has taken time for this modality to gain acceptance because of several factors: higher radiation dose, higher cost, and the novelty of use in this application. It is important to note that the technology for CT scanners is still rapidly evolving. As the speed, resolution, and cost of CT scanners continue to improve, computer techniques for the measurement and analysis of nodules will also achieve corresponding improvements in accuracy and diagnostic utility. Future knowledge-based CT CAD systems will provide detailed analysis of the related conditions of the lungs, such as emphysema, and diagnostic analysis of nodules. The issue is not whether CAD will improve the performance and capabilities of the radiologist, but at what rate their development and the corresponding improvement will occur. Current prototype CAD systems may be considered as tools. As such they will improve the performance of the user/radiologist if they are well engineered and if the user understands their capabilities and limitations. These systems need to be improved by knowledge-based engineering, which is notoriously difficult to implement robustly and requires model refinement and optimization based on a large database of cases. Research should be directed at developing these methods rather than comparing prototype systems with current practices. Future performance should be expected to exceed that of today's grand masters.

Computer-aided diagnosis of small pulmonary nodules.

Computer-aided methods are now being developed for the detection and characterization of pulmonary nodules found in CT images, based on techniques from computer vision, image processing, and pattern classification. With the increasing resolution of modern CT scanners, computer methods provide continually improving accuracy, reproducibility, and utility in analyzing the larger numbers of images acquired in a lung screening exam or diagnostic study. This article describes the fundamental tools and issues involved in computer-aided nodule detection and characterization, as we move from two-dimensional toward three-dimensional automated methods. In particular, we focus on the new domain of "small" pulmonary nodules.

Examination of the cupula and stereocilia of the horizontal semicircular canal in the toadfish Opsanus tau.

We imaged the horizontal semicircular canal (HSCC) crista and cupula of toadfish, Opsanus tau, by using a) confocal light microscopy of isolated vital HSCC; b) serial sections of fixed, trichrome-stained HSCC; and c) scanning electron microscopy of fixed HSCCs. HSCC were dissections which included an ampulla and an attached canal tube (long and slender canal portion), and, in some cases, a small portion of the utricular wall. Cupulae were seen as multipartite mucus connective tissue shells rising from the crista and extending toward the ampullary roof. They were composed of several refractile bands traversing the cupulae perpendicular to longitudinal fibers extending from the cupular base to its apex. Alcian green-stained cupulae showed an asymmetric alcianphilic, dark, X-shaped structure, indicating that the pillar is rich in mucin and carbohydrate, an interpretation supported by images of trichrome-stained sections. The cupular antrum is devoid of prominent refractile fibers. No tubes or channels were observed in the cupula or antrum of vital preparations. Cupular shell fibers cover the surface of the crista, are roughly parallel, and are associated with a translucent material having a refractive index greater than the surrounding endolymph. Stereocilia were thin, 100-microm-long structures, with little longitudinal curvature, which end with no end bulb. No strands extend from stereocilia to the roof or other portions of the cupular antrum. Gross movements of stereocilia were not seen in mechanically quiescent preparations. Within the cupular antrum, stereocilia were parallel to connective tissue fibers, all embedded in an isotropic gel. This fiber-reinforced gel and cupular matrix are sensitive to N-acetlyneuraminidase and beta-N-acetyl glucosaminidase, and minimally sensitive to beta-N-acetyl hexosaminidase. Connective tissue fibers may serve to stiffen the gel, whose matrix would restrict lateral motion of embedded fibers and stereocilia thereby providing mechanical support for stereocilia.

Computational cost of image registration with a parallel binary array processor.

The application of a simulated binary array processor (BAP) to the rapid analysis of a sequence of images has been studied. Several algorithms have been developed which may be implemented on many existing parallel processing machines. The characteristic operations of a BAP are discussed and analyzed. A set of preprocessing algorithms are described which are designed to register two images of TV-type video data in real time. These algorithms illustrate the potential uses of a BAP and their cost is analyzed in detail. The results of applying these algorithms to FLIR data and to noisy optical data are given. An analysis of these algorithms illustrates the importance of an efficient global feature extraction hardware for image understanding applications.