Cost-effectiveness of initial stress cardiovascular MR, stress SPECT or stress echocardiography as a gate-keeper test, compared with upfront invasive coronary angiography in the investigation and management of patients with stable chest pain: Mid-term outcomes from the CECaT randomised controlled trial

Objectives: To compare outcomes and cost-effectiveness of various initial imaging strategies in the management of stable chest pain in a long-term prospective randomised trial. Setting: Regional cardiothoracic referral centre in the east of England. Participants: 898 patients (69% man) entered the study with 869 alive at 2 years of follow-up. Patients were included if they presented for assessment of stable chest pain with a positive exercise test and no prior history of ischaemic heart disease. Exclusion criteria were recent infarction, unstable symptoms or any contraindication to stress MRI. Primary outcome measures: The primary outcomes of this follow-up study were survival up to a minimum of 2 years post-treatment, quality-adjusted survival and cost-utility of each strategy. Results: 898 patients were randomised. Compared with angiography, mortality was marginally higher in the groups randomised to cardiac MR (HR 2.6, 95% CI 1.1 to 6.2), but similar in the single photon emission CT-methoxyisobutylisonitrile (SPECT-MIBI; HR 1.0, 95% CI 0.4 to 2.9) and ECHO groups (HR 1.6, 95% CI 0.6 to 4.0). Although SPECT-MIBI was marginally superior to other non-invasive tests there were no other significant differences between the groups in mortality, quality-adjusted survival or costs. Conclusions: Non-invasive cardiac imaging can be used safely as the initial diagnostic test to diagnose coronary artery disease without adverse effects on patient outcomes or increased costs, relative to angiography. These results should be interpreted in the context of recent advances in imaging technology. Trial registration: ISRCTN 47108462, UKCRN 3696

The effect of open lung ventilation on right ventricular and left ventricular function in lung-lavaged pigs

INTRODUCTION: Ventilation according to the open lung concept (OLC) consists of recruitment maneuvers, followed by low tidal volume and high positive end-expiratory pressure, aiming at minimizing atelectasis. The minimization of atelectasis reduces the right ventricular (RV) afterload, but the increased intrathoracic pressures used by OLC ventilation could increase the RV afterload. We hypothesize that when atelectasis is minimized by OLC ventilation, cardiac function is not affected despite the higher mean airway pressure. METHODS: After repeated lung lavage, each pig (n = 10) was conventionally ventilated and was ventilated according to OLC in a randomized cross-over setting. Conventional mechanical ventilation (CMV) consisted of volume-controlled ventilation with 5 cmH2O positive end-expiratory pressure and a tidal volume of 8-10 ml/kg. No recruitment maneuvers were performed. During OLC ventilation, recruitment maneuvers were applied until PaO2/FiO2 > 60 kPa. The peak inspiratory pressure was set to obtain a tidal volume of 6-8 ml/kg. The cardiac output (CO), th

Aerospace Medicine and Biology: A continuing bibliography (supplement 229)

This bibliography lists 109 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1982

An ultra-fast digital diffuse optical spectroscopic imaging system for neoadjuvant chemotherapy monitoring

Up to 20% of breast cancer patients who undergo presurgical (neoadjuvant) chemotherapy have no response to treatment. Standard-of-care imaging modalities, including MRI, CT, mammography, and ultrasound, measure anatomical features and tumor size that reveal response only after months of treatment. Recently, non-invasive, near-infrared optical markers have shown promise in indicating the efficacy of treatment at the outset of the chemotherapy treatment. For example, frequency domain Diffuse Optical Spectroscopic Imaging (DOSI) can be used to characterize the optical scattering and absorption properties of thick tissue, including breast tumors. These parameters can then be used to calculate tissue concentrations of chromophores, including oxyhemoglobin, deoxyhemoglobin, water, and lipids. Tumors differ in hemoglobin concentration, as compared with healthy background tissue, and changes in hemoglobin concentration during neoadjuvant chemotherapy have been shown to correlate with efficacy of treatment. Using DOSI early in treatment to measure chromophore concentrations may be a powerful tool for guiding neoadjuvant chemotherapy treatment. Previous frequency-domain DOSI systems have been limited by large device footprints, complex electronics, high costs, and slow acquisition speeds, all of which complicate access to patients in the clinical setting. In this work a new digital DOSI (dDOSI) system has been developed, which is relatively inexpensive and compact, allowing for use at the bedside, while providing unprecedented measurement speeds. The system builds on, and significantly advances, previous dDOSI setups developed by our group and, for the first time, utilizes hardware-integrated custom board-level direct digital synthesizers (DDS) and analog to digital converters (ADC) to generate and directly measure signals utilizing undersampling techniques. The dDOSI system takes high-speed optical measurements by utilizing wavelength multiplexing while sweeping through hundreds of modulation frequencies in tens of milliseconds. The new dDOSI system is fast, inexpensive, and compact without compromising accuracy and precision

Machine learning for efficient recognition of anatomical structures and abnormalities in biomedical images

Three studies have been carried out to investigate new approaches to efficient image segmentation and anomaly detection. The first study investigates the use of deep learning in patch based segmentation. Current approaches to patch based segmentation use low level features such as the sum of squared differences between patches. We argue that better segmentation can be achieved by harnessing the power of deep neural networks. Currently these networks make extensive use of convolutional layers. However, we argue that in the context of patch based segmentation, convolutional layers have little advantage over the canonical artificial neural network architecture. This is because a patch is small, and does not need decomposition and thus will not benefit from convolution. Instead, we make use of the canonical architecture in which neurons only compute dot products, but also incorporate modern techniques of deep learning. The resulting classifier is much faster and less memory-hungry than convolution based networks. In a test application to the segmentation of hippocampus in human brain MR images, we significantly outperformed prior art with a median Dice score up to 90.98% at a near real-time speed (<1s). The second study is an investigation into mouse phenotyping, and develops a high-throughput framework to detect morphological abnormality in mouse embryo micro-CT images. Existing work in this line is centred on, either the detection of phenotype-specific features or comparative analytics. The former approach lacks generality and the latter can often fail, for example, when the abnormality is not associated with severe volume variation. Both these approaches often require image segmentation as a pre-requisite, which is very challenging when applied to embryo phenotyping. A new approach to this problem in which non-rigid registration is combined with robust principal component analysis (RPCA), is proposed. The new framework is able to efficiently perform abnormality detection in a batch of images. It is sensitive to both volumetric and non-volumetric variations, and does not require image segmentation. In a validation study, it successfully distinguished the abnormal VSD and polydactyly phenotypes from the normal, respectively, at 85.19% and 88.89% specificities, with 100% sensitivity in both cases. The third study investigates the RPCA technique in more depth. RPCA is an extension of PCA that tolerates certain levels of data distortion during feature extraction, and is able to decompose images into regular and singular components. It has previously been applied to many computer vision problems (e.g. video surveillance), attaining excellent performance. However these applications commonly rest on a critical condition: in the majority of images being processed, there is a background with very little variation. By contrast in biomedical imaging there is significant natural variation across different images, resulting from inter-subject variability and physiological movements. Non-rigid registration can go some way towards reducing this variance, but cannot eliminate it entirely. To address this problem we propose a modified framework (RPCA-P) that is able to incorporate natural variation priors and adjust outlier tolerance locally, so that voxels associated with structures of higher variability are compensated with a higher tolerance in regularity estimation. An experimental study was applied to the same mouse embryo micro-CT data, and notably improved the detection specificity to 94.12% for the VSD and 90.97% for the polydactyly, while maintaining the sensitivity at 100%.Open Acces

Deep Learning Framework for Spleen Volume Estimation from 2D Cross-sectional Views

Abnormal spleen enlargement (splenomegaly) is regarded as a clinical indicator for a range of conditions, including liver disease, cancer and blood diseases. While spleen length measured from ultrasound images is a commonly used surrogate for spleen size, spleen volume remains the gold standard metric for assessing splenomegaly and the severity of related clinical conditions. Computed tomography is the main imaging modality for measuring spleen volume, but it is less accessible in areas where there is a high prevalence of splenomegaly (e.g., the Global South). Our objective was to enable automated spleen volume measurement from 2D cross-sectional segmentations, which can be obtained from ultrasound imaging. In this study, we describe a variational autoencoder-based framework to measure spleen volume from single- or dual-view 2D spleen segmentations. We propose and evaluate three volume estimation methods within this framework. We also demonstrate how 95% confidence intervals of volume estimates can be produced to make our method more clinically useful. Our best model achieved mean relative volume accuracies of 86.62% and 92.58% for single- and dual-view segmentations, respectively, surpassing the performance of the clinical standard approach of linear regression using manual measurements and a comparative deep learning-based 2D-3D reconstruction-based approach. The proposed spleen volume estimation framework can be integrated into standard clinical workflows which currently use 2D ultrasound images to measure spleen length. To the best of our knowledge, this is the first work to achieve direct 3D spleen volume estimation from 2D spleen segmentations.Comment: 22 pages, 7 figure