The recent progress of real-time ultrasound in image fusion may provide several new possibilities, including diagnosis, treatment, and follow-up of oncologic patients.

IntroductionConfocal laser endomicroscopy (CLE) is becoming a popular method for optical biopsy of digestive mucosa for both diagnostic and therapeutic procedures. Computer aided diagnosis of CLE images, using image processing and fractal analysis can be used to quantify the histological structures in the CLE generated images. The aim of this study is to develop an automatic diagnosis algorithm of colorectal cancer (CRC), based on fractal analysis and neural network modeling of the CLE-generated colon mucosa images.Materials and MethodsWe retrospectively analyzed a series of 1035 artifact-free endomicroscopy images, obtained during CLE examinations from normal mucosa (356 images) and tumor regions (679 images). The images were processed using a computer aided diagnosis (CAD) medical imaging system in order to obtain an automatic diagnosis. The CAD application includes image reading and processing functions, a module for fractal analysis, grey-level co-occurrence matrix (GLCM) computation module, and a feature identification module based on the Marching Squares and linear interpolation methods. A two-layer neural network was trained to automatically interpret the imaging data and diagnose the pathological samples based on the fractal dimension and the characteristic features of the biological tissues.ResultsNormal colon mucosa is characterized by regular polyhedral crypt structures whereas malignant colon mucosa is characterized by irregular and interrupted crypts, which can be diagnosed by CAD. For this purpose, seven geometric parameters were defined for each image: fractal dimension, lacunarity, contrast correlation, energy, homogeneity, and feature number. Of the seven parameters only contrast, homogeneity and feature number were significantly different between normal and cancer samples. Next, a two-layer feed forward neural network was used to train and automatically diagnose the malignant samples, based on the seven parameters tested. The neural network operations were cross-entropy with the results: training: 0.53, validation: 1.17, testing: 1.17, and percent error, resulting: training: 16.14, validation: 17.42, testing: 15.48. The diagnosis accuracy error was 15.5%.ConclusionsComputed aided diagnosis via fractal analysis of glandular structures can complement the traditional histological and minimally invasive imaging methods. A larger dataset from colorectal and other pathologies should be used to further validate the diagnostic power of the method.

This 3D navigation system demonstrates good ex vivo accuracy and is sufficiently accurate in vivo to explore its potential for improved endovascular navigation.

Background Image-guided robots are manipulators that operate based on medical images. Perhaps the most common class of image-guided robots are robots for needle interventions. Typically, these robots actively position and/or orient a needle guide, but needle insertion is still done by the physician. While this arrangement may have safety advantages and keep the physician in control of needle insertion, actuated needle drivers can incorporate other useful features. Methods We first present a new needle driver that can actively insert and rotate a needle. With this device we investigate the use of needle rotation in controlled in-vitro experiments performed with a specially developed revolving needle driver. Results These experiments show that needle rotation can improve targeting and may reduce errors by as much as 70%. Conclusion The new needle driver provides a unique kinematic architecture that enables insertion with a compact mechanism. Perhaps the most interesting conclusion of the study is that lesions of soft tissue organs may not be perfectly targeted with a needle without using special techniques, either manually or with a robotic device. The results of this study show that needle rotation may be an effective method of reducing targeting errors.

Arteriolar arcades provide alternate pathways for blood flow after obstruction of arteries or arterioles such as occurs in stroke and coronary and peripheral vascular disease. When obstruction is prolonged, remaining vessels adjust their diameters chronically in response to altered hemodynamic and metabolic conditions. Here, the effectiveness of arcades in maintaining perfusion both immediately following obstruction and after structural adaptation was examined. Morphometric data from a vascular casting of the pig triceps brachii muscle and published data were used to develop a computational model for the hemodynamics and structural adaptation of the arcade network between two feed artery branches, FA1 and FA2. The predicted total flow to capillaries (Q(TA)) in the region initially supplied by FA2 decreased to 26% of the normal value immediately after FA2 obstruction but was restored to 78% of the normal value after adaptation. After obstruction of 1-10 randomly selected arcade segments, Q(TA) was on average 18% higher in the arcade network than in a corresponding two-tree network without arcades. Structural adaptation increased Q(TA) by an additional 16% in the arcade network but had almost no effect in the two-tree network. These results indicate that arcades can partially maintain blood flow after vascular blockage and that this effect is substantially enhanced by structural adaptation.

Background and Objective:Navigation of a flexible endoscopic ultrasound (EUS) probe inside the gastrointestinal (GI) tract is problematic due to the small window size and complex anatomy. The goal of the present study was to test the feasibility of a novel fusion imaging (FI) system which uses electromagnetic (EM) sensors to co-register the live EUS images with the pre-procedure computed tomography (CT) data with a novel navigation algorithm and catheter.Methods:An experienced gastroenterologist and a novice EUS operator tested the FI system on a GI tract bench top model. Also, the experienced gastroenterologist performed a case series of 20 patients during routine EUS examinations.Results:On the bench top model, the experienced and novice doctors reached the targets in 67 ± 18 s and 150 ± 24 s with a registration error of 6 ± 3 mm and 11 ± 4 mm, respectively. In the case series, the total procedure time was 24.6 ± 6.6 min, while the time to reach the clinical target was 8.7 ± 4.2 min.Conclusions:The FI system is feasible for clinical use, and can reduce the learning curve for EUS procedures and improve navigation and targeting in difficult anatomic locations.

IntroductionNumerous anti-angiogenic agents are currently developed to limit tumor growth and metastasis. While these drugs offer hope for cancer patients, their transient effect on tumor vasculature is difficult to assess in clinical settings. Confocal laser endomicroscopy (CLE) is a novel endoscopic imaging technology that enables histological examination of the gastrointestinal mucosa. The aim of the present study was to evaluate the feasibility of using CLE to image the vascular network in fresh biopsies of human colorectal tissue. For this purpose we have imaged normal and malignant biopsy tissue samples and compared the vascular network parameters obtained with CLE with established histopathology techniques.Materials and MethodsFresh non-fixed biopsy samples of both normal and malignant colorectal mucosa were stained with fluorescently labeled anti-CD31 antibodies and imaged by CLE using a dedicated endomicroscopy system. Corresponding biopsy samples underwent immunohistochemical staining for CD31, assessing the microvessel density (MVD) and vascular areas for comparison with CLE data, which were measured offline using specific software.ResultsThe vessels were imaged by CLE in both normal and tumor samples. The average diameter of normal vessels was 8.5±0.9 µm whereas in tumor samples it was 13.5±0.7 µm (p = 0.0049). Vascular density was 188.7±24.9 vessels/mm2 in the normal tissue vs. 242.4±16.1 vessels/mm2 in the colorectal cancer samples (p = 0.1201). In the immunohistochemistry samples, the MVD was 211.2±42.9/mm2 and 351.3±39.6/mm2 for normal and malignant mucosa, respectively. The vascular area was 2.9±0.5% of total tissue area for the normal mucosa and 8.5±2.1% for primary colorectal cancer tissue.ConclusionSelective imaging of blood vessels with CLE is feasible in normal and tumor colorectal tissue by using fluorescently labeled antibodies targeted against an endothelial marker. The method could be translated into the clinical setting for monitoring of anti-angiogenic therapy.

Differential diagnosis of focal pancreatic masses is based on endoscopic ultrasound (EUS) guided fine needle aspiration biopsy (EUS-FNA/FNB). Several imaging techniques (i.e. gray-scale, color Doppler, contrast-enhancement and elastography) are used for differential diagnosis. However, diagnosis remains highly operator dependent. To address this problem, machine learning algorithms (MLA) can generate an automatic computer-aided diagnosis (CAD) by analyzing a large number of clinical images in real-time. We aimed to develop a MLA to characterize focal pancreatic masses during the EUS procedure. The study included 65 patients with focal pancreatic masses, with 20 EUS images selected from each patient (grayscale, color Doppler, arterial and venous phase contrast-enhancement and elastography). Images were classified based on cytopathology exam as: chronic pseudotumoral pancreatitis (CPP), neuroendocrine tumor (PNET) and ductal adenocarcinoma (PDAC). The MLA is based on a deep learning method which combines convolutional (CNN) and long short-term memory (LSTM) neural networks. 2688 images were used for training and 672 images for testing the deep learning models. The CNN was developed to identify the discriminative features of images, while a LSTM neural network was used to extract the dependencies between images. The model predicted the clinical diagnosis with an area under curve index of 0.98 and an overall accuracy of 98.26%. The negative (NPV) and positive (PPV) predictive values and the corresponding 95% confidential intervals (CI) are 96.7%, [94.5, 98.9] and 98.1%, [96.81, 99.4] for PDAC, 96.5%, [94.1, 98.8], and 99.7%, [99.3, 100] for CPP, and 98.9%, [97.5, 100] and 98.3%, [97.1, 99.4] for PNET. Following further validation on a independent test cohort, this method could become an efficient CAD tool to differentiate focal pancreatic masses in real-time.