David Koff

Accelerated muscle and adipose tissue loss may predict survival in pancreatic cancer patients: the relationship with diabetes and anaemia.

Weight loss leading to cachexia is associated with poor treatment response and reduced survival in pancreatic cancer patients. We aim to identify indicators that allow for early detection that will advance our understanding of cachexia and will support targeted anti-cachexia therapies. A total of fifty pancreatic cancer patients were analysed for skeletal muscle and visceral adipose tissue (VAT) changes using computed tomography (CT) scans. These changes were related to physical characteristics, secondary disease states and treatment parameters. Overall, patients lost 1.72 (SD 3.29) kg of muscle and 1.04 (SD 1.08) kg of VAT during the disease trajectory (413 (SD 213) d). After sorting patients into tertiles by rate of VAT and muscle loss, patients losing VAT at > -0.40 kg/100 d had poorer survival outcomes compared with patients with < -0.10 kg/100 d of VAT loss (P= 0.020). Patients presenting with diabetes at diagnosis demonstrated significantly more and accelerated VAT loss compared with non-diabetic patients. In contrast, patients who were anaemic at the first CT scan lost significantly more muscle tissue and at accelerated rates compared with non-anaemic patients. Accelerated rates of VAT loss are associated with reduced survival. Identifying associated features of cachexia, such as diabetes and anaemia, is essential for the early detection of cachexia and may facilitate the attenuation of complications associated with cachexia.

Pan-Canadian Evaluation of Irreversible Compression Ratios (“Lossy” Compression) for Development of National Guidelines

New technological advancements including multislice CT scanners and functional MRI, have dramatically increased the size and number of digital images generated by medical imaging departments. Despite the fact that the cost of storage is dropping, the savings are largely surpassed by the increasing volume of data being generated. While local area network bandwidth within a hospital is adequate for timely access to imaging data, efficiently moving the data between institutions requires wide area network bandwidth, which has a limited availability at a national level. A solution to address those issues is the use of lossy compression as long as there is no loss of relevant information. The goal of this study was to determine levels at which lossy compression can be confidently used in diagnostic imaging applications. In order to provide a fair assessment of existing compression tools, we tested and compared the two most commonly adopted DISCOM compression algorithms: JPEG and JPEG-2000. We conducted an extensive pan-Canadian evaluation of lossy compression applied to seven anatomical areas and five modalities using two recognized techniques: objective methods or diagnostic accuracy and subjective assessment based on Just Noticeable Difference. By incorporating both diagnostic accuracy and subjective evaluation techniques, enabled us to define a range of compression for each modality and body part tested. The results of our study suggest that at low levels of compression, there was no significant difference between the performance of lossy JPEG and lossy JPEG 2000, and that they are both appropriate to use for reporting on medical images. At higher levels, lossy JPEG proved to be more effective than JPEG 2000 in some cases, mainly neuro CT. More evaluation is required to assess the effect of compression on thin slice CT. We provide a table of recommended compression ratios for each modality and anatomical area investigated, to be integrated in the Canadian Association of Radiologists standard for the use of lossy compression in medical imaging.

Evidence against the Involvement of Chronic Cerebrospinal Venous Abnormalities in Multiple Sclerosis. A Case-Control Study

Objective Multiple sclerosis (MS) is a chronic neurodegenerative disease of the CNS. Recently a controversial vascular hypothesis for MS, termed chronic cerebrospinal venous insufficiency (CCSVI), has been advanced. The objective of this study was to evaluate the relative prevalence of the venous abnormalities that define CCSVI. Methods A case-control study was conducted in which 100 MS patients aged between 18–65 y meeting the revised McDonald criteria were randomly selected and stratified into one of four MS subtypes: relapsing/remitting, secondary progressive, primary progressive and benign. Control subjects (16–70 y) with no known history of MS or other neurological condition were matched with the MS cases. All cases and controls underwent ultrasound imaging of the veins of the neck plus the deep cerebral veins, and magnetic resonance imaging of the neck veins and brain. These procedures were performed on each participant on the same day. Results On ultrasound we found no evidence of reflux, stenosis or blockage in the internal jugular veins (IJV) or vertebral veins (VV) in any study participant. Similarly, there was no evidence of either reflux or cessation of flow in the deep cerebral veins in any subject. Flow was detected in the IJV and VV in all study participants. Amongst 199 participants there was one MS subject who fulfilled the minimum two ultrasound criteria for CCSVI. Using MRI we found no significant differences in either the intra- or extra-cranial venous flow velocity or venous architecture between cases and controls. Conclusion This case-control study provides compelling evidence against the involvement of CCSVI in multiple sclerosis.

Comparison of dual energy subtraction chest radiography and traditional chest X-rays in the detection of pulmonary nodules

BACKGROUND Dual energy subtraction (DES) radiography is a powerful but underutilized technique which aims to improve the diagnostic value of an X-ray by separating soft tissue from bones, producing two different images. Compared to traditional chest X-rays, DES requires exposure to higher doses of radiation but may achieve higher accuracy. The objective of this study was to assess the clinical benefits of DES radiography by comparing the speed and accuracy of diagnosis of pulmonary nodules with DES versus traditional chest X-rays. METHODS Five radiologists and five radiology residents read the DES and traditional chest X-rays of 51 patients, 34 with pulmonary nodules and 17 without. Their accuracy and speed in the detection of nodules were measured using specialized image display software. RESULTS DES radiography reduced reading time from 13 to 10 sec (P<0.0001) in staff and from 21 to 15 sec in residents (P<0.0001). There was also a small increase in sensitivity 0.58 to 0.67 overall (P<0.10) with no change in specificity (0.85 overall). CONCLUSIONS By eliminating rib shadows in soft tissue images, DES improved the speed and accuracy of radiologists in the diagnosis of pulmonary nodules.

Lung nodule segmentation in chest computed tomography using a novel background estimation method

BACKGROUND Lung cancer results in the highest number of cancer deaths worldwide. The segmentation of lung nodules is an important task in computer systems to help physicians differentiate malignant lesions from benign lesions. However, it has already been observed that this may be a difficult task, especially when nodules are connected to an anatomical structure. METHODS This paper proposes a method to estimate the background of the nodule area and how this estimation is used to facilitate the segmentation task. RESULTS Our experiments indicate more than 99% of accuracy with less than 1% of false positive rate (FPR). CONCLUSIONS The proposed methods achieved better results than a state-of-the-art approach, indicating potential to be used in medical image processing systems.

OpenID Connect as a security service in cloud-based medical imaging systems

Abstract. The evolution of cloud computing is driving the next generation of medical imaging systems. However, privacy and security concerns have been consistently regarded as the major obstacles for adoption of cloud computing by healthcare domains. OpenID Connect, combining OpenID and OAuth together, is an emerging representational state transfer-based federated identity solution. It is one of the most adopted open standards to potentially become the de facto standard for securing cloud computing and mobile applications, which is also regarded as “Kerberos of cloud.” We introduce OpenID Connect as an authentication and authorization service in cloud-based diagnostic imaging (DI) systems, and propose enhancements that allow for incorporating this technology within distributed enterprise environments. The objective of this study is to offer solutions for secure sharing of medical images among diagnostic imaging repository (DI-r) and heterogeneous picture archiving and communication systems (PACS) as well as Web-based and mobile clients in the cloud ecosystem. The main objective is to use OpenID Connect open-source single sign-on and authorization service and in a user-centric manner, while deploying DI-r and PACS to private or community clouds should provide equivalent security levels to traditional computing model.

The impact of skull bone intensity on the quality of compressed CT neuro images

The increasing use of technologies such as CT and MRI, along with a continuing improvement in their resolution, has contributed to the explosive growth of digital image data being generated. Medical communities around the world have recognized the need for efficient storage, transmission and display of medical images. For example, the Canadian Association of Radiologists (CAR) has recommended compression ratios for various modalities and anatomical regions to be employed by lossy JPEG and JPEG2000 compression in order to preserve diagnostic quality. Here we investigate the effects of the sharp skull edges present in CT neuro images on JPEG and JPEG2000 lossy compression. We conjecture that this atypical effect is caused by the sharp edges between the skull bone and the background regions as well as between the skull bone and the interior regions. These strong edges create large wavelet coefficients that consume an unnecessarily large number of bits in JPEG2000 compression because of its bitplane coding scheme, and thus result in reduced quality at the interior region, which contains most diagnostic information in the image. To validate the conjecture, we investigate a segmentation based compression algorithm based on simple thresholding and morphological operators. As expected, quality is improved in terms of PSNR as well as the structural similarity (SSIM) image quality measure, and its multiscale (MS-SSIM) and informationweighted (IW-SSIM) versions. This study not only supports our conjecture, but also provides a solution to improve the performance of JPEG and JPEG2000 compression for specific types of CT images.

Interactive Modeling and Evaluation of Tumor Growth

This paper addresses the need to quantify tumor growth and detect changes as this information is relevant to manage the patient treatment and to aid biotechnological efforts to cure cancer (Silva et al. 2008). An interactive tumor segmentation technique is used to recover the shape and size of tumors without imposing shape constraints. This segmentation algorithm provides good convergence, is robust to the initialization conditions, and requires simple and intuitive user interactions. A parametric approach to model tumor growth analytically is proposed in this paper. The preliminary experimental results are encouraging. The segmentation method is shown to be robust and simple to use, even in situations where the tumor boundary definition is challenging. Also, the experiments indicate that the proposed model potentially can be used to extrapolate the available data and help predict the tumor size (assuming unconstrained growth). Additionally, the proposed method potentially can provide a quantitative reference to compare the tumor shrinkage rate in cancer treatments.

Modelling of Subjective Radiological Assessments with Objective Image Quality Measures of Brain and Body CT Images

In this work we determine how well the common objective image quality measures (Mean Squared Error (MSE), local MSE, Signal-to-Noise Ratio (SNR), Structural Similarity Index (SSIM), Visual Signal-to-Noise Ratio (VSNR) and Visual Information Fidelity (VIF)) predict subjective radiologists’ assessments for brain and body computed tomography (CT) images.

OpenID connect as a security service in Cloud-based diagnostic imaging systems

The evolution of cloud computing is driving the next generation of diagnostic imaging (DI) systems. Cloud-based DI systems are able to deliver better services to patients without constraining to their own physical facilities. However, privacy and security concerns have been consistently regarded as the major obstacle for adoption of cloud computing by healthcare domains. Furthermore, traditional computing models and interfaces employed by DI systems are not ready for accessing diagnostic images through mobile devices. RESTful is an ideal technology for provisioning both mobile services and cloud computing. OpenID Connect, combining OpenID and OAuth together, is an emerging REST-based federated identity solution. It is one of the most perspective open standards to potentially become the de-facto standard for securing cloud computing and mobile applications, which has ever been regarded as “Kerberos of Cloud”. We introduce OpenID Connect as an identity and authentication service in cloud-based DI systems and propose enhancements that allow for incorporating this technology within distributed enterprise environment. The objective of this study is to offer solutions for secure radiology image sharing among DI-r (Diagnostic Imaging Repository) and heterogeneous PACS (Picture Archiving and Communication Systems) as well as mobile clients in the cloud ecosystem. Through using OpenID Connect as an open-source identity and authentication service, deploying DI-r and PACS to private or community clouds should obtain equivalent security level to traditional computing model.