Optimal dose levels in screening chest CT for unimpaired detection and volumetry of lung nodules, with and without computer assisted detection at minimal patient radiation

OBJECTIVES The aim of this phantom study was to minimize the radiation dose by finding the best combination of low tube current and low voltage that would result in accurate volume measurements when compared to standard CT imaging without significantly decreasing the sensitivity of detecting lung nodules both with and without the assistance of CAD. METHODS An anthropomorphic chest phantom containing artificial solid and ground glass nodules (GGNs, 5-12 mm) was examined with a 64-row multi-detector CT scanner with three tube currents of 100, 50 and 25 mAs in combination with three tube voltages of 120, 100 and 80 kVp. This resulted in eight different protocols that were then compared to standard CT sensitivity (100 mAs/120 kVp). For each protocol, at least 127 different nodules were scanned in 21-25 phantoms. The nodules were analyzed in two separate sessions by three independent, blinded radiologists and computer-aided detection (CAD) software. RESULTS The mean sensitivity of the radiologists for identifying solid lung nodules on a standard CT was 89.7% ± 4.9%. The sensitivity was not significantly impaired when the tube and current voltage were lowered at the same time, except at the lowest exposure level of 25 mAs/80 kVp [80.6% ± 4.3% (p = 0.031)]. Compared to the standard CT, the sensitivity for detecting GGNs was significantly lower at all dose levels when the voltage was 80 kVp; this result was independent of the tube current. The CAD significantly increased the radiologists' sensitivity for detecting solid nodules at all dose levels (5-11%). No significant volume measurement errors (VMEs) were documented for the radiologists or the CAD software at any dose level. CONCLUSIONS Our results suggest a CT protocol with 25 mAs and 100 kVp is optimal for detecting solid and ground glass nodules in lung cancer screening. The use of CAD software is highly recommended at all dose levels.

A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

Non-invasive and non-occlusive blood pressure estimation via a chest sensor

The clinical demand for a device to monitor Blood Pressure (BP) in ambulatory scenarios with minimal use of inflation cuffs is increasing. Based on the so-called Pulse Wave Velocity (PWV) principle, this paper introduces and evaluates a novel concept of BP monitor that can be fully integrated within a chest sensor. After a preliminary calibration, the sensor provides non-occlusive beat-by-beat estimations of Mean Arterial Pressure (MAP) by measuring the Pulse Transit Time (PTT) of arterial pressure pulses travelling from the ascending aorta towards the subcutaneous vasculature of the chest. In a cohort of 15 healthy male subjects, a total of 462 simultaneous readings consisting of reference MAP and chest PTT were acquired. Each subject was recorded at three different days: D, D+3 and D+14. Overall, the implemented protocol induced MAP values to range from 80 ± 6 mmHg in baseline, to 107 ± 9 mmHg during isometric handgrip maneuvers. Agreement between reference and chest-sensor MAP values was tested by using intraclass correlation coefficient (ICC = 0.78) and Bland-Altman analysis (mean error = 0.7 mmHg, standard deviation = 5.1 mmHg). The cumulative percentage of MAP values provided by the chest sensor falling within a range of ±5 mmHg compared to reference MAP readings was of 70%, within ±10 mmHg was of 91%, and within ±15mmHg was of 98%. These results point at the fact that the chest sensor complies with the British Hypertension Society (BHS) requirements of Grade A BP monitors, when applied to MAP readings. Grade A performance was maintained even two weeks after having performed the initial subject-dependent calibration. In conclusion, this paper introduces a sensor and a calibration strategy to perform MAP measurements at the chest. The encouraging performance of the presented technique paves the way towards an ambulatory-compliant, continuous and non-occlusive BP monitoring system.

Design and Test of a 0.5 THz Dipole Antenna With Integrated Schottky Diode Detector on a High Dielectric Constant Ceramic Electromagnetic Bandgap Substrate

This paper presents the first analysis of the input impedance and radiation properties of a dipole antenna, placed on top of Fan 's three-dimensional electromagnetic bandgap (EBG) structure, (Applied Physics Letters, 1994) constructed using a high dielectric constant ceramic. The best position of the dipole on the EBG surface is determined following impedance and radiation pattern analyses. Based on this optimum configuration an integrated Schottky heterodyne detector was designed, manufactured and tested from 0.48 to 0.52 THz. The main antenna features were not degraded by the high dielectric constant substrate due to the use of the EBG approach. Measured radiation patterns are in good agreement with the predicted ones.

Antenna Simulations and Measurements of Focal Plane Array Facet Reflectors

We present a conceptual prototype model of a focal plane array unit for the STEAMR instrument, highlighting the challenges presented by the required high relative beam proximity of the instrument and focus on how edge-diffraction effects contribute to the array's performance. The analysis was carried out as a comparative process using both PO & PTD and MoM techniques. We first highlight general differences between these computational techniques, with the discussion focusing on diffractive edge effects for near-field imaging reflectors with high truncation. We then present the results of in-depth modeling analyses of the STEAMR focal plane array followed by near-field antenna measurements of a breadboard model of the array. The results of these near-field measurements agree well with both simulation techniques although MoM shows slightly higher complex beam coupling to the measurements than PO & PTD.

Combining Wireless Sensor Networks and Machine Learning for Flash Flood Nowcasting

This paper addresses an investigation with machine learning (ML) classification techniques to assist in the problem of flash flood now casting. We have been attempting to build a Wireless Sensor Network (WSN) to collect measurements from a river located in an urban area. The machine learning classification methods were investigated with the aim of allowing flash flood now casting, which in turn allows the WSN to give alerts to the local population. We have evaluated several types of ML taking account of the different now casting stages (i.e. Number of future time steps to forecast). We have also evaluated different data representation to be used as input of the ML techniques. The results show that different data representation can lead to results significantly better for different stages of now casting.

Growth Codes: Intermediate Performance Analysis and Application to Video

Growth codes are a subclass of Rateless codes that have found interesting applications in data dissemination problems. Compared to other Rateless and conventional channel codes, Growth codes show improved intermediate performance which is particularly useful in applications where partial data presents some utility. In this paper, we investigate the asymptotic performance of Growth codes using the Wormald method, which was proposed for studying the Peeling Decoder of LDPC and LDGM codes. Compared to previous works, the Wormald differential equations are set on nodes' perspective which enables a numerical solution to the computation of the expected asymptotic decoding performance of Growth codes. Our framework is appropriate for any class of Rateless codes that does not include a precoding step. We further study the performance of Growth codes with moderate and large size codeblocks through simulations and we use the generalized logistic function to model the decoding probability. We then exploit the decoding probability model in an illustrative application of Growth codes to error resilient video transmission. The video transmission problem is cast as a joint source and channel rate allocation problem that is shown to be convex with respect to the channel rate. This illustrative application permits to highlight the main advantage of Growth codes, namely improved performance in the intermediate loss region.

Landmark Detection for Fusion of Fundus and MRI Towards a Patient-Specific Multi-Modal Eye Model

Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise e.g., Fundus photography, Optical Coherence Tomography (OCT), Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Yet, these images are often complementary and do express the same pathologies in a different way. Some pathologies are only visible in a particular modality. Thus, it is beneficial for the ophthalmologist to have these modalities fused into a single patient-specific model. The presented article’s goal is a fusion of Fundus photography with segmented MRI volumes. This adds information to MRI which was not visible before like vessels and the macula. This article’s contributions include automatic detection of the optic disc, the fovea, the optic axis and an automatic segmentation of the vitreous humor of the eye.

Localization and Segmentation of 3D Intervertebral Discs in MR Images by Data Driven Estimation

This paper addresses the problem of fully-automatic localization and segmentation of 3D intervertebral discs (IVDs) from MR images. Our method contains two steps, where we first localize the center of each IVD, and then segment IVDs by classifying image pixels around each disc center as foreground (disc) or background. The disc localization is done by estimating the image displacements from a set of randomly sampled 3D image patches to the disc center. The image displacements are estimated by jointly optimizing the training and test displacement values in a data-driven way, where we take into consideration both the training data and the geometric constraint on the test image. After the disc centers are localized, we segment the discs by classifying image pixels around disc centers as background or foreground. The classification is done in a similar data-driven approach as we used for localization, but in this segmentation case we are aiming to estimate the foreground/background probability of each pixel instead of the image displacements. In addition, an extra neighborhood smooth constraint is introduced to enforce the local smoothness of the label field. Our method is validated on 3D T2-weighted turbo spin echo MR images of 35 patients from two different studies. Experiments show that compared to state of the art, our method achieves better or comparable results. Specifically, we achieve for localization a mean error of 1.6-2.0 mm, and for segmentation a mean Dice metric of 85%-88% and a mean surface distance of 1.3-1.4 mm.

Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge

Cephalometric analysis is an essential clinical and research tool in orthodontics for the orthodontic analysis and treatment planning. This paper presents the evaluation of the methods submitted to the Automatic Cephalometric X-Ray Landmark Detection Challenge, held at the IEEE International Symposium on Biomedical Imaging 2014 with an on-site competition. The challenge was set to explore and compare automatic landmark detection methods in application to cephalometric X-ray images. Methods were evaluated on a common database including cephalograms of 300 patients aged six to 60 years, collected from the Dental Department, Tri-Service General Hospital, Taiwan, and manually marked anatomical landmarks as the ground truth data, generated by two experienced medical doctors. Quantitative evaluation was performed to compare the results of a representative selection of current methods submitted to the challenge. Experimental results show that three methods are able to achieve detection rates greater than 80% using the 4 mm precision range, but only one method achieves a detection rate greater than 70% using the 2 mm precision range, which is the acceptable precision range in clinical practice. The study provides insights into the performance of different landmark detection approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

A complete-pelvis segmentation framework for image-free total hip arthroplasty (THA): methodology and clinical study

Background Complete-pelvis segmentation in antero-posterior pelvic radiographs is required to create a patient-specific three-dimensional pelvis model for surgical planning and postoperative assessment in image-free navigation of total hip arthroplasty. Methods A fast and robust framework for accurately segmenting the complete pelvis is presented, consisting of two consecutive modules. In the first module, a three-stage method was developed to delineate the left hemipelvis based on statistical appearance and shape models. To handle complex pelvic structures, anatomy-specific information processing techniques were employed. As the input to the second module, the delineated left hemi-pelvis was then reflected about an estimated symmetry line of the radiograph to initialize the right hemi-pelvis segmentation. The right hemi-pelvis was segmented by the same three-stage method, Results Two experiments conducted on respectively 143 and 40 AP radiographs demonstrated a mean segmentation accuracy of 1.61±0.68 mm. A clinical study to investigate the postoperative assessment of acetabular cup orientations based on the proposed framework revealed an average accuracy of 1.2°±0.9° and 1.6°±1.4° for anteversion and inclination, respectively. Delineation of each radiograph costs less than one minute. Conclusions Despite further validation needed, the preliminary results implied the underlying clinical applicability of the proposed framework for image-free THA.

Vision-based Proximity Detection in Retinal Surgery

In retinal surgery, surgeons face difficulties such as indirect visualization of surgical targets, physiological tremor, and lack of tactile feedback, which increase the risk of retinal damage caused by incorrect surgical gestures. In this context, intraocular proximity sensing has the potential to overcome current technical limitations and increase surgical safety. In this paper, we present a system for detecting unintentional collisions between surgical tools and the retina using the visual feedback provided by the opthalmic stereo microscope. Using stereo images, proximity between surgical tools and the retinal surface can be detected when their relative stereo disparity is small. For this purpose, we developed a system comprised of two modules. The first is a module for tracking the surgical tool position on both stereo images. The second is a disparity tracking module for estimating a stereo disparity map of the retinal surface. Both modules were specially tailored for coping with the challenging visualization conditions in retinal surgery. The potential clinical value of the proposed method is demonstrated by extensive testing using a silicon phantom eye and recorded rabbit in vivo data.

Slow conduction in mixed cultured strands of primary ventricular cells and stem cell-derived cardiomyocytes

Modern concepts for the treatment of myocardial diseases focus on novel cell therapeutic strategies involving stem cell-derived cardiomyocytes (SCMs). However, functional integration of SCMs requires similar electrophysiological properties as primary cardiomyocytes (PCMs) and the ability to establish intercellular connections with host myocytes in order to contribute to the electrical and mechanical activity of the heart. The aim of this project was to investigate the properties of cardiac conduction in a co-culture approach using SCMs and PCMs in cultured cell strands. Murine embryonic SCMs were pooled with fetal ventricular cells and seeded in predefined proportions on microelectrode arrays to form patterned strands of mixed cells. Conduction velocity (CV) was measured during steady state pacing. SCM excitability was estimated from action potentials measured in single cells using the patch clamp technique. Experiments were complemented with computer simulations of conduction using a detailed model of cellular architecture in mixed cell strands. CV was significantly lower in strands composed purely of SCMs (5.5 ± 1.5 cm/s, n = 11) as compared to PCMs (34.9 ± 2.9 cm/s, n = 21) at similar refractoriness (100% SCMs: 122 ± 25 ms, n = 9; 100% PCMs: 139 ± 67 ms, n = 14). In mixed strands combining both cell types, CV was higher than in pure SCMs strands, but always lower than in 100% PCM strands. Computer simulations demonstrated that both intercellular coupling and electrical excitability limit CV. These data provide evidence that in cultures of murine ventricular cardiomyocytes, SCMs cannot restore CV to control levels resulting in slow conduction, which may lead to reentry circuits and arrhythmias.