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Modelling and Simulation
Up one levelThe following CMIV projects conducts research related to Modelling and Simulation.
1H-MRS in ALS and Unverricht-Lundborgs disease
ALS and Unverricht Lundborg myoclonic epilepsy are two neurodegenerative diseases without curing treatment. Two pts with ALS and two with U-L are investigated with 1H-MRS of the white matter over a two year period in order to analyse the character of the neurodegenerative course. NAA, Cho, myo-Ins and Lac are analysed according to the LC model. Clinical status is checked and compared. Pharmacological intervention is tried.
An uncertainty-aware visualization pipeline for volume rendering
Within Direct Volume Rendering (DVR) there is one important aspect of knowledge and data representation that has been largely overlooked - Uncertainty. In all measurement and simulations there are inherent inaccuracies and throughout the visualization pipeline additional uncertainties in processing, rendering and interaction are introduced. If these uncertainties are not conveyed the result may be misinterpretations and false conclusions. In medical visualization the problem is particularly pertinent with the hazards of wrong diagnosis and mistreatment of medical conditions. The overall goal in this project is: To develop an uncertainty aware real-time Direct Volume Rendering pipeline based on domain knowledge.
Application of novel image filters to MR image data
Background: Although time taken for completing MR exams has decreased substantially in recent past, it is still considerably longer than some other imaging examinations such as CT scanning. Radiologists have to manage a careful balance between examination duration and image quality. Image filters have been applied in past to CT, plain film radiography, and ultrasound. Purpose and scientific questions: The aim of our study is to assess if special 3D image filters can help quality of MR images compared to unprocessed and 2D filtered images. Another scientific question pertains to reduction of time required for performing MR examination with 3D filters. Most important variables: Image quality of MR images with and without application of the image filter will be compared by multiple independent radiologists. Assessment of image quality will include both the standard deviation of the MR signal as well as the subjective assessment of the image quality by multiple radiologists. Time saved with the use of 3D filters will be estimated for each subject. Advances in Knowledge and significance: Use of 3D filters for enhancing MR capabilities has not described before. Our study intends to determine if 3D filters for MR exams can help improve image quality and/or aid in reducing MR examination duration compared to use of either no filter or 2D filters. If found useful, the 3D filters will help us improve patient throughput in MRI and at the same time will enhance image quality of MR images.
Application of novel iterative reconstruction algorithms (IRA) and noise reduction filters (NRF) for reducing CT radiation dose
Background: Worldwide there have been concerns about increased risk of cancer with radiation dose from CT scanning. Reduction of radiation dose from CT will also decrease the risk of radiation induced cancer. Therefore, several techniques such as automatic exposure control techniques, and bow tie filters have been developed and assessed to reduce radiation dose with CT. Despite these developments, the radiation dose with CT scanning continues to increase each year as number of CT examinations performed each year keeps on increasing. Purpose and scientific questions: The aim of our study is to acquire low radiation dose CT data and assess if iterative reconstruction algorithms (IRA) and advanced noise reduction filters (ANRF) techniques can help improve image quality and acceptability of low dose CT images. Most important variables: CMIV has recently obtained access to novel IRA and NRF techniques for improving image quality of low dose CT images. Dose reduced CT images typically have higher noise and lower signal to noise ratio. We believe that IRA and ANRF, which work in different data domains to improve image quality and enable acquisition of low radiation dose CT. After acquisition of CT image data at different lower dose levels, we will independently process the data with IRA and ANRF and see if there is an improvement in the image quality with these techniques. The intent will be to see if variables such as image noise, artifacts, image contrast, sharpness as well as lesion conspicuity on low dose post processed images are similar to unprocessed higher dose images. In addition, quantitative measures of image quality such as quantitative image noise, and contrast to noise ratio will be performed. Advances in Knowledge and significance: This study will give information on use of IRA and ANRF for reducing radiation dose to patients undergoing CT scanning and quantify need and advantage of IRA and ANRF over unprocessed CT images reconstructed using conventional filtered back projection technique. If found useful, these techniques will help cut the radiation dose without sacrificing image quality, a result that may help save radiation dose from CT scanning.
Automated Generation of Patient Specific Models for Visual and Haptic Simulation of Hip Fracture Surgery
The goal of this project is an auto-generated patient specific model for haptic and visual simulation of hip fracture surgery. Osteoporotic fractures constitute a problem of increasing clinical importance. A problem with the cervical type of hip fracture is the great risk of complications. A patient-specific simulation model would enable the surgeon to perform simulated surgery on the patient. Instead of discussing alternative techniques using plain X-ray films, the surgeon would have the chance to test several operative approaches, resulting in a safer and more rapid real operation. In addition, these models would be useful in the training of surgeons and development of new techniques. The first step in the generation of the model is segmentation of the bone in a CT-volume. Then, the local bone density will be estimated from the CT-data. The resulting information will then be converted to fit models suitable for visual and haptic simulation.
Characterisation and optimisation of medical x-ray imaging systems
In spite of the long use of diagnostic x-rays, the choice of imaging parameters is largely based on experience rather than scientific knowledge. Due to the increased risk of cancer induction, the user must minimise the patient dose but obtain sufficient clinical diagnostic information. The aim of this project is to reach a situation where scientifically based methods and tools can be used to optimise x-ray examinations. Physical image quality descriptors are derived using Monte Carlo (MC) simulation of the x-ray imaging system including a 3D model of the patient. The MC simulation also allows calculation of patient effective dose (radiation risk) and hence can be used for optimisation. The MC simulation realistically models the fundamental quantum noise but not the other noise sources influencing the detectability of lesions in the radiograph. A series of clinical trials in digital chest and breast imaging are designed to quantify these other noise sources (anatomical noise, structure noise). This will be achieved using hybrid images where pathological lesions are digitally included in patient images to achieve a realistic diagnostic task for the radiologist. The results from the trials are incorporated into the MC model, which is then used for optimising the imaging parameters. The project will lead to increased understanding of factors influencing both physical and clinical image quality, a prerequisite for efficient use of medical x-rays.
Functional MRI studies of normal and impaired language function
Language ability plays an important role when communicating with others. Before a-typical language activation can be detected in patient populations, normal language function has to be explored. In this project we intend to study the influence of performance and difficulty related language ability on cortical activation in healthy subjects and in patients with language disability.
HEART4FLOW - Improved Diagnosis and Management of Heart Disease by 4D Blood Flow Assessment
The primary purpose of the cardiovascular system is to drive, control and maintain blood flow to all parts of the body. Despite the primacy of flow, cardiac diagnostics still rely almost exclusively on tools focused on morphological assessment. The objective of the HEART4FLOW project is to develop the next generation of methods for the non-invasive quantitative assessment of cardiac diseases and therapies by focusing on blood flow dynamics, with the goals of earlier and more accurate detection and improved management of cardiac diseases.
Identification of cognitive processes with fMRI and auditory stimulation in hearing impaired with and without hearing aids
Within this project, we will investigate the neural correlates of cognitive processes during speech intelligibility in noise. The data will be analysed according to the working memory framework of Ease of Language Understanding (ELU) developed by Rönnberg and colleagues. This model states that the demands on cognitive (‘explicit’) processing increase when speech comprehension is impaired by background noise, hearing loss, or altered by the type of signal processing in the hearing instrument.
Image Quality and Radiation Dose in Computerised Tomography (CT): The Virtual Tomograph - a tool for optimization
Novel techniques for computerised tomography (CT) are being developed, which allow fast scanning and 3D visualisation of patients. Improper design and use of equipment for volume CT may cause unnecessarily high patient dose. The aim of the project is to use a computational model (‘the virtual tomograph’) to optimise CT imaging systems to give advice on how to design and use such systems to minimise patient dose and still achieve sufficient image quality. In particular, to implement realistic models of scattered radiation and noise in simulated projection data; to standardise methods to evaluate how different reconstruction algorithms influence image quality; to develop methods to assess clinical image quality and to search for correlation between clinical and physical image quality measures. Monte Carlo techniques will be used to model CT systems including anthropomorphic phantoms and realistic image detectors, allowing simultaneous derivation of projection data and patient dose. Clinical image quality will be assessed using various methods: ROC-analysis of lesions, Visual Grading Analysis using European Image Criteria. Efficient image generation is vital to optimisation. Information, which has been lost due to poor image generation cannot be regained by post-processing of image data. The virtual tomograph makes it possible to analyse in detail the effects of varying imaging parameters, including patient physique on image quality and patient dose.
Implementation of Synthetic MRI into the clinical workflow.
Synthetic MRI is the approach of rapid quantification of MRI parameters and the subsequent synthesis of a whole range of contrast images based on the quantified data. This implies that a single scan is sufficient to generate any conventional T1- or T2 weighted image. It is even possible to visualize far stronger, non-physical contrast such as tissue specific imaging. Application of Synthetic MRI might save up to a third of the patient examination time and will make MRI more reliable and quantitative. The project aims at the clinical implementation of the approach into the PACS system. The technique of rapid quantification is more or less mature but the general use of Synthetic MRI in daily clinic needs to be introduced and validated. In addition to the investigation of the quality of the resulting images the specification of the time-saving aspect will be important. Cardiac Late Enhancement is implemented first and the validation is on-going. Synthetic Brain imaging is implemented at the moment. Future directions will concern the liver.
Manifold-Valued Signal Proocessing
A manifold is a mathematical concept which generalizes surfaces to higher dimensions. Examples of 2-dimensional manifolds are for instance the surface of a sphere and the the surface of a torus, both being examples of non-linear manifolds. Locally however, manifolds are flat and equivalent to the an Euclidean space. Features found in signals can often be described using manifolds. This is often not stated explicitly, but instead various parameterizations of manifolds are used. In order to describe a quantity which can be seen as a point on a sphere, spherical coordinates are commonly used. This has some drawbacks however which we wish to avoid if possible. We see a need for manifolds in the field of medical image analysis. Medical doctors express a wish to objectively quantify various features in medical images, such as local texture, shape and orientation of organs. We know from previous research that manifolds can do the job, but we lack a generic framework for dealing with manifold-valued signals in signal processing. In fact, we believe that such a framework will be useful in other areas signal processing too. The goal of this project is to explore a specific flavour of signal processing and continue the development of methods 1) to learn or identify manifold-valued representations from examples and 2) apply signal processing on manifold-valued signals which is analogous to filtering and interpolation using convolution operators in classic signal processing.
Mental Energy – an fMRI study of the core control network for goal-oriented behavior
Our research plans address the mental energy concept, by which we mean the mental resources an individual can recruit in order to perform optimally. We propose that a common resource pool is available in the human brain for any task that requires mental effort, or focused awareness and willful action. Several recent reports identified the anterior insula and the anterior cingulate cortices (AIC and ACC) as a core control network for goal-oriented behavior, and it has recently been proposed that the AIC engenders awareness and the ACC engenders volitional action. Our central hypothesis is that this core neural network provides the “mental energy” needed for any effortful task.
Morphology Guided fMRI
As the potentials for treating neurological disorders have increased tremendously the last decades, there is also a growing need for safe, reliable and cost-effective diagnostic tools. fMRI is valuable both for an improved description of normal brain function and for assessment of patients with neurological disorders. The core theoretical idea in the project is that by including/developing tools for reconstruction of the brains cortical surface new and highly significant local spatial priors can be included in the fMRI data analysis and in this way significantly improve detection performance.
MOVIII Demonstrator Project: Bio-Feedback Using Real-Time fMRI
Despite the enormous complexity of the human mind, fMRI techniques are able to partially observe the state of a brain in action. In this paper we describe an experimental setup for real-time fMRI in a bio-feedback loop. One of the main challenges in the project is to reach a detection speed, accuracy and spatial resolution necessary to attain sufficient bandwidth of communication to close the bio-feedback loop. To this end we have banked on our previous work on real-time filtering for fMRI and system identification, which has been tailored for use in the experiment setup. In the experiments presented the system is trained to estimate where a person in the MRI scanner is looking from signals derived from the visual cortex only. We have been able to demonstrate that the user can induce an action and perform simple tasks with her mind sensed using real-time fMRI.The technique may have several clinical applications, for instance to allow paralyzed and "locked in" people to communicate with the outside world. In the meanwhile, the need for improved fMRI performance and brain state detection poses a challenge to the signal processing community. We also expect that the setup will serve as an invaluable tool for neuro science research in general.
Neuro-imaging in Kleine Levin syndrome
Kleine Levin syndrome is a rare disease affecting young people giving periodic hypersomnia combined with hyperphagia and other psychological symptoms. Our research has demonstrated hypoperfusion in temporofrontal regions on SPECT and a short term memory dysfuntion. We have a material of 15 patients and are examining them using cerebral fMRI with a cognitive paradigm for verbal memory and concentration. Preliminary results have revealed a thalamic dysfunction when compared to healthy ctrls. We also use 1H-MRS of hippocampus in search of an organic substrate for the memory dysfunction. The study aims at finding the biological substrate of this disease.
New dimensions in ultrasound contrast imaging - Visualization of myocardial blood flow
Aims are: 1. To find new bubble excitation strategies for improved contrast/tissue ratio of the ultrasound image 2. To perform ultrasound pulse field and contrast bubble response simulations 3. To peruse corresponding in vitro experiments 4. To deliver the contrast optimized pulse sequences for implementation in echocardiographs (for clinical studies)
Patient specific simulation of RF-treatment of liver tumors
The liver is the most common target for metastases from cancers in the abdominal organs. If possible, the liver tumors are removed by surgical resection. This,however, is often not possible due to a poor general condition of the patient or the liver. In recent years, radio frequency ablation (RFA) has become an important adjunct to modern treatment. RFA uses high-frequency electrical current to destroy tissue cells by heating them. A special needle-like electrode is inserted into the tumor and the RF current heats the surrounding tissue in order to destroy the tumor. The aim of this proposal is to develop a patient specific simulator for RFA of liver tumors. The purpose of the simulator is to simulate the thermal effect of the intervention in order to optimize the treatment and to avoid thermal damage on healthy tissue and sensitive structures such as small blood vessels and the bile ducts. Such a simulator would be useful in increasing the efficacy and safety of RFA. The project involves three major steps: Image acquisition and processing, Bio-heat modelling and, finally, evaluation. The main scientific challenges are: Development of methods for segmentation of relevant anatomical structures from MRI data; development of a heat transfer model for the liver that takes into account the cooling effect from the blood vessels; integration of the heat transfer model and the anatomic model into a patient specific RFA simulator.
Reduction of Motion Artifacts in MRI
Cardiac Magnetic Resonance Imaging (MRI) is known to be degraded by respiratory motion during the scan. Previous methods to cope with these problems either impose a short scan time limit, prolong the scan or do not reduce the artefacts sufficiently. For time-resolved 3D phase contrast measurements of the cardiac flow and wall motion, the imaging time is already long, and image quality is of great importance. This projects aims to construct a reconstruction algorithm that is able to reduce the artefacts caused by respiration without prolonging the scan. This might be done by using a generalized reconstruction transform combined with an iterative optimization of an image quality metric.
SIMILAR - The European Taskforce Creating Human-Machine Interfaces Similar to Human-Human Communication - WP10 Medical Applications
* SIMILAR will create an integrated task force on multimodal interfaces that respond efficiently to speech, gestures, vision, haptics and direct brain connections by merging into a single research group excellent European laboratories in Human-Computer Interaction (HCI) and Signal Processing. * SIMILAR will develop a common theoretical framework for fusion and fission of multimodal information using the most advanced Signal Processing tools constrained by Human Computer Interaction rules. * SIMILAR will develop a network of usability test facilities and establish an assessment methodology. * SIMILAR will develop a common distributed software platform available for researchers and the public at large through www.openinterface.org. * SIMILAR will establish a scientific foundation which will manage an International Journal, Special Sessions in existing conferences, organise summer schools, interact with key European industrial partners and promote new research activities at the European level. * SIMILAR will address a series of great challenges in the field of edutainment, interfaces for disabled people and interfaces for medical applications. Natural immersive interfaces for education purposes and interfaces for environments where the user is unable to use his hands and a keyboard (like Surgical Operation Rooms, or cars) will be dealt with a stronger focus.
