Automatic estimation of noise parameters in Fourier-domain optical coherence tomography cross sectional images using statistical information

We present an application and sample independent method for the automatic discrimination of noise and signal in optical coherence tomography Bscans. The proposed algorithm models the observed noise probabilistically and allows for a dynamic determination of image noise parameters and the choice of appropriate image rendering parameters. This overcomes the observer variability and the need for a priori information about the content of sample images, both of which are challenging to estimate systematically with current systems. As such, our approach has the advantage of automatically determining crucial parameters for evaluating rendered image quality in a systematic and task independent way. We tested our algorithm on data from four different biological and nonbiological samples (index finger, lemon slices, sticky tape, and detector cards) acquired with three different experimental spectral domain optical coherence tomography (OCT) measurement systems including a swept source OCT. The results are compared to parameters determined manually by four experienced OCT users. Overall, our algorithm works reliably regardless of which system and sample are used and estimates noise parameters in all cases within the confidence interval of those found by observers.

Prediction of dental implant torque with a fast and automatic finite element analysis: a pilot study

Despite its importance, implant removal torque can be assessed at present only after implantation. This paper presents a new technique to help clinicians preoperatively evaluate implant stability.

Virtobot--a multi-functional robotic system for 3D surface scanning and automatic post mortem biopsy

The Virtopsy project, a multi-disciplinary project that involves forensic science, diagnostic imaging, computer science, automation technology, telematics and biomechanics, aims to develop new techniques to improve the outcome of forensic investigations. This paper presents a new approach in the field of minimally invasive virtual autopsy for a versatile robotic system that is able to perform three-dimensional (3D) surface scans as well as post mortem image-guided soft tissue biopsies.

Validity of an automatic measure protocol in distal femur for allograft selection from a three-dimensional virtual bone bank system

Osteoarticular allograft is one possible treatment in wide surgical resections with large defects. Performing best osteoarticular allograft selection is of great relevance for optimal exploitation of the bone databank, good surgery outcome and patient’s recovery. Current approaches are, however, very time consuming hindering these points in practice. We present a validation study of a software able to perform automatic bone measurements used to automatically assess the distal femur sizes across a databank. 170 distal femur surfaces were reconstructed from CT data and measured manually using a size measure protocol taking into account the transepicondyler distance (A), anterior-posterior distance in medial condyle (B) and anterior-posterior distance in lateral condyle (C). Intra- and inter-observer studies were conducted and regarded as ground truth measurements. Manual and automatic measures were compared. For the automatic measurements, the correlation coefficients between observer one and automatic method, were of 0.99 for A measure and 0.96 for B and C measures. The average time needed to perform the measurements was of 16 h for both manual measurements, and of 3 min for the automatic method. Results demonstrate the high reliability and, most importantly, high repeatability of the proposed approach, and considerable speed-up on the planning.

A Fully Automatic Calibration Framework for Navigated Ultrasound Imaging

Navigated ultrasound (US) imaging is used for the intra-operative acquisition of 3D image data during imageguided surgery. The presented approach includes the design of a compact and easy to use US calibration device and its integration into a software application for navigated liver surgery. User interaction during the calibration process is minimized through automatic detection of the calibration process followed by automatic image segmentation, calculation of the calibration transform and validation of the obtained result. This leads to a fast, interaction-free and fully automatic calibration procedure enabling intra-operative

Autotransfusion system or integrated automatic suction device in minimized extracorporeal circulation: influence on coagulation and inflammatory response

To measure surrogate markers of coagulation activation as well as of the systemic inflammatory response in patients undergoing primary elective coronary artery bypass grafting (CABG) using either the so-called Smart suction device or a continuous autotransfusion system (C.A.T.S.®).

Computed tomography (CT) virtual autopsy and classical autopsy discrepancies: radiologist's error or a demonstration of post-mortem multi-detector computed tomography (MDCT) limitation?

Modern imaging technologies, such as computed tomography (CT) techniques, represent a great challenge in forensic pathology. The field of forensics has experienced a rapid increase in the use of these new techniques to support investigations on critical cases, as indicated by the implementation of CT scanning by different forensic institutions worldwide. Advances in CT imaging techniques over the past few decades have finally led some authors to propose that virtual autopsy, a radiological method applied to post-mortem analysis, is a reliable alternative to traditional autopsy, at least in certain cases. The authors investigate the occurrence and the causes of errors and mistakes in diagnostic imaging applied to virtual autopsy. A case of suicide by a gunshot wound was submitted to full-body CT scanning before autopsy. We compared the first examination of sectional images with the autopsy findings and found a preliminary misdiagnosis in detecting a peritoneal lesion by gunshot wound that was due to radiologist's error. Then we discuss a new emerging issue related to the risk of diagnostic failure in virtual autopsy due to radiologist's error that is similar to what occurs in clinical radiology practice.

[Strategies for reducing the CT radiation dose]

The rapid technical advances in computed tomography have led to an increased number of clinical indications. Unfortunately, at the same time the radiation exposure to the population has also increased due to the increased total number of CT examinations. In the last few years various publications have demonstrated the feasibility of radiation dose reduction for CT examinations with no compromise in image quality and loss in interpretation accuracy. The majority of the proposed methods for dose optimization are easy to apply and are independent of the detector array configuration. This article reviews indication-dependent principles (e.g. application of reduced tube voltage for CT angiography, selection of the collimation and the pitch, reducing the total number of imaging series, lowering the tube voltage and tube current for non-contrast CT scans), manufacturer-dependent principles (e.g. accurate application of automatic modulation of tube current, use of adaptive image noise filter and use of iterative image reconstruction) and general principles (e.g. appropriate patient-centering in the gantry, avoiding over-ranging of the CT scan, lowering the tube voltage and tube current for survey CT scans) which lead to radiation dose reduction.

Applicability of Tableside Flat Panel Detector CT Parenchymal Cerebral Blood Volume Measurement in Neurovascular Interventions: Preliminary Clinical Experience

CBV is a vital perfusion parameter in estimating the viability of brain parenchyma (eg, in cases of ischemic stroke or after interventional vessel occlusion). Recent technologic advances allow parenchymal CBV imaging tableside in the angiography suite just before, during, or after an interventional procedure. The aim of this work was to analyze our preliminary clinical experience with this new imaging tool in different neurovascular interventions.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.