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Patients with chronic thromboembolic pulmonary hypertension (CTEPH) have morphologic changes to the pulmonary vasculature. These include pruning of the distal vessels, dilation of the proximal vessels, and increased vascular tortuosity. Advances in image processing and computer vision enable objective detection and quantification of these processes in clinically acquired computed tomographic (CT) scans. Three-dimensional reconstructions of the pulmonary vasculature were created from the CT angiograms of 18 patients with CTEPH diagnosed using imaging and hemodynamics as well as 15 control patients referred to our Dyspnea Clinic and found to have no evidence of pulmonary vascular disease. Compared to controls, CTEPH patients exhibited greater pruning of the distal vasculature (median density of small-vessel volume: 2.7 [interquartile range (IQR): 2.5–3.0] vs. 3.2 [3.0–3.8]; P = 0.008), greater dilation of proximal arteries (median fraction of blood in large arteries: 0.35 [IQR: 0.30–0.41] vs. 0.23 [0.21–0.31]; P = 0.0005), and increased tortuosity in the pulmonary arterial tree (median: 4.92% [IQR: 4.85%–5.21%] vs. 4.63% [4.39%–4.92%]; P = 0.004). CTEPH was not associated with dilation of proximal veins or increased tortuosity in the venous system. Distal pruning of the vasculature was correlated with the cardiac index (R = 0.51, P = 0.04). Quantitative models derived from CT scans can be used to measure changes in vascular morphology previously described subjectively in CTEPH. These measurements are also correlated with invasive metrics of pulmonary hemodynamics, suggesting that they may be used to assess disease severity. Further work in a larger cohort may enable the use of such measures as a biomarker for diagnostic, phenotyping, and prognostic purposes.

Artery-vein classification on pulmonary computed tomography (CT) images is becoming of high interest in the scientific community due to the prevalence of pulmonary vascular disease that affects arteries and veins through different mechanisms. In this work, we present a novel approach to automatically segment and classify vessels from chest CT images. We use a scale-space particle segmentation to isolate vessels, and combine a convolutional neural network (CNN) to graph-cut (GC) to classify the single particles. Information about proximity of arteries to airways is learned by the network by means of a bronchus enhanced image. The methodology is evaluated on the superior and inferior lobes of the right lung of twenty clinical cases. Comparison with manual classification and a Random Forests (RF) classifier is performed. The algorithm achieves an overall accuracy of 87% when compared to manual reference, which is higher than the 73% accuracy achieved by RF.

Parkinson’s disease (PD) is a slowly progressing neurodegenerative disease with early manifestation of motor signs. Objective measurements of motor signs are of vital importance for diagnosing, monitoring and developing disease modifying therapies, particularly for the early stages of the disease when putative neuroprotective treatments could stop neurodegeneration. Current medical practice has limited tools to routinely monitor PD motor signs with enough frequency and without undue burden for patients and the healthcare system. In this paper, we present data indicating that the routine interaction with computer keyboards can be used to detect motor signs in the early stages of PD. We explore a solution that measures the key hold times (the time required to press and release a key) during the normal use of a computer without any change in hardware and converts it to a PD motor index. This is achieved by the automatic discovery of patterns in the time series of key hold times using an ensemble regression algorithm. This new approach discriminated early PD groups from controls with an AUC = 0.81 (n = 42/43; mean age = 59.0/60.1; women = 43%/60%;PD/controls). The performance was comparable or better than two other quantitative motor performance tests used clinically: alternating finger tapping (AUC = 0.75) and single key tapping (AUC = 0.61)., Comunidad de Madrid, Fundación Ramón Areces, Michael J. Fox Foundation for Parkinson's Research

Background Natural orifice transluminal endoscopic surgery (NOTES) is technically challenging owing to endoscopic short-sighted visualization, excessive scope flexibility and lack of adequate instrumentation. Augmented reality may overcome these difficulties. This study tested whether an image registration system for NOTES procedures (IR-NOTES) can facilitate navigation. Methods In three human cadavers 15 intra-abdominal organs were targeted endoscopically with and without IR-NOTES via both transgastric and transcolonic routes, by three endoscopists with different levels of expertise. Ease of navigation was evaluated objectively by kinematic analysis, and navigation complexity was determined by creating an organ access complexity score based on the same data. Results Without IR-NOTES, 21 (11·7 per cent) of 180 targets were not reached (expert endoscopist 3, advanced 7, intermediate 11), compared with one (1 per cent) of 90 with IR-NOTES (intermediate endoscopist) (P = 0·002). Endoscope movements were significantly less complex in eight of the 15 listed organs when using IR-NOTES. The most complex areas to access were the pelvis and left upper quadrant, independently of the access route. The most difficult organs to access were the spleen (5 failed attempts; 3 of 7 kinematic variables significantly improved) and rectum (4 failed attempts; 5 of 7 kinematic variables significantly improved). The time needed to access the rectum through a transgastric approach was 206·3 s without and 54·9 s with IR-NOTES (P = 0·027). Conclusion The IR-NOTES system enhanced both navigation efficacy and ease of intra-abdominal NOTES exploration for operators of all levels. The system rendered some organs accessible to non-expert operators, thereby reducing one impediment to NOTES procedures.

This article investigates the suitability of local intensity distributions to analyze six emphysema classes in 342 CT scans obtained from 16 sites hosting scanners by 3 vendors and a total of 9 specific models in subjects with Chronic Obstructive Pulmonary Disease (COPD). We propose using kernel density estimation to deal with the inherent sparsity of local intensity histograms obtained from scarcely populated regions of interest. We validate our approach by leave-one-subject-out classification experiments and full-lung analyses. We compare our results with recently published LBP texture-based methodology. We demonstrate the efficacy of using intensity information alone in multi-scanner cohorts, which is a simpler, more intuitive approach.

Background and study aims Most natural orifice transluminal endoscopic surgery (NOTES) procedures have been performed in animal models through the anterior stomach wall, but this approach does not provide efficient access to all anatomic areas of interest. Moreover, injury of the adjacent structures has been reported when using a blind access. The aim of the current study was to assess the utility of a CT-based (CT: computed tomography) image registered navigation system in identifying safe gastrointestinal access sites for NOTES and identifying intraperitoneal structures. Methods A total of 30 access procedures were performed in 30 pigs: anterior gastric wall (n = 10), posterior gastric wall (n = 10), and anterior rectal wall (n = 10). Of these, 15 procedures used image registered guidance (IR-NOTES) and 15 procedures used a blind access (NOTES only). Timed abdominal exploration was performed with identification of 11 organs. The location of the endoscopic tip was tracked using an electromagnetic tracking system and was recorded for each case. Necropsy was performed immediately after the procedure. The primary outcome was the rate of complications; secondary outcome variables were number of organs identified and kinematic measurements. Results A total of 30 animals weighting a mean (± SD) of 30.2 ± 6.8 kg were included in the study. The incision point was correctly placed in 11 out of 15 animals in each group (73.3 %). The mean peritoneoscopy time and the number of properly identified organs were equivalent in the two groups. There were eight minor complications (26.7 %), two (13.3 %) in the IR-NOTES group and six (40.0 %) in the NOTES only group ( P = n. s.). Characteristics of the endoscope tip path showed a statistically significant improvement in trajectory smoothness of motion for all organs in the IR-NOTES group. Conclusion The image registered system appears to be feasible in NOTES procedures and results from this study suggest that image registered guidance might be useful for supporting navigation with an increased smoothness of motion.