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Oncology
Up one levelThe following CMIV projects conducts research related to Oncology.
Absolute Quantification of Multinuclear Magnetic Resonance Spectroscopy
The major aim of this project is in collaboration with several clinics to expand the scope of medical magnetic resonance methods of water to a large number of metabolites and other functional tissue properties in order to significantly enhance the level of todays applications of clinical MR. The work covers developing novel acquisition technologies and hardware, as well as clinical applications of quantitative molecular spectroscopy and imaging. A major long-term aim is to shift MR-applications from a qualitative to a quantitative mode.
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.
Clinical Application of Synthetic MRI on Patients with Malignant Gliomas
Patients with malignant gliomas are treated with surgery, chemo- and radiotherapy and then followed with MRI-examinations to detect early signs of tumour recurrence. Almost 25% of patients react to the treatment with oedema and contrast-enhancement in the affected area of the brain, and this image is difficult to distinguish from tumour recurrence. Since the image is non-specific, radiologists in these cases look to more quantitative methods such as MR Spectroscopy and PET-CT, but there are still cases where it is unclear if the patient should be treated with further surgery or just followed with further imaging. Synthetic MRI is a quantitative MR-method that enables quantitative measurement of the tissue. If it is possible to find tumour specific quantitative values, it might be possible to distinguish tumour from treatment effects and thereby improving the diagnostic arsenal in these difficult cases.
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.
Medical applications of electron paramagnetic resonance imaging (EPRI)
Radicals play crucial roles in a wide variety of physiological functions such as inflammatory response and regulation of blood flow, but are also involved in the pathology of various diseases such as atherosclerosis, cancer and Alzheimer´s disease. EPRI has also successfully been used to quantitatively image oxygen pressure in tumours in small animals (e.g. mice). Imaging of radical distributions and identification of radical species is therefore valuable for applications in a large number of medical disciplines. The first Scandinavian electron paramagnetic resonance imaging (EPRI) spectrometer, with unique possibilities for imaging of radical distributions in vivo and in vitro, was installed at Linköping University during 2010. The specific aim of the present proposal is to use this new equipment for medical applications. Three specific applications are proposed: Imaging of radical distributions in atherosclerotic plaques for a better understanding of the evolving inflammatory reaction leading to atherosclerosis, imaging of radical distributions in matter irradiated with ionizing radiation for dosimetry and imaging of oxygen pressure in tumours in mice.
Optimized flows and IT tools for digital pathology
Diagnostic pathology is of crucial importance for the health care in Sweden and globally. While there is a great lack of pathologists the demands are increasing due to an ageing population and more specialized diagnostic methods. In pathology there is an imminent need to significantly increase efficiency in parallel with quality of care improvements. Digitization of the pathological images is one of few major possibilities to achieve this. The benefit foreseen with digital pathology is shorter waiting times, higher diagnostic quality, more cost-efficient resource use and improved education. The project will design the workflows needed to achive these goals and also develop a demonstrator, an IT system showing how the workflow could be solved in practice. The project will also build a foundation for extending CMIV into a national research center in digital pathology.
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.
Dissertation by Daniel Forsberg
