Projects / Programmes
Automated analysis of angiographic images for early diagnosis, monitoring and treatment of intracranial aneurysms
| Code |
Science |
Field |
Subfield |
| 2.06.07 |
Engineering sciences and technologies |
Systems and cybernetics |
Biomedical technics |
| Code |
Science |
Field |
| T111 |
Technological sciences |
Imaging, image processing |
| Code |
Science |
Field |
| 2.06 |
Engineering and Technology |
Medical engineering
|
intracranial aneurysm, computer aided diagnosis, image analysis, machine learning, quantification of morphology, optimal treatment selection
Researchers (30)
Organisations (2)
Abstract
Cardiovascular and circulatory diseases are the world's leading cause of disability and mortality with their impact having increased at an alarming rate of 22.6% over the past two decades. The WHO statistics from 2008 show that cerebral vessels are among the most affected with 30% of all deaths caused by cerebrovascular pathologies. One such pathology is an intracranial aneurysm (IA), which is formed when a weakened part of cerebral arterial wall bulges into a balloon-like structure. The IA may eventually rupture and lead to subarachnoid hemorrhage, a serious health condition with a high mortality rate. Rupture is fatal in about 40% of cases, of those who survive about 66% suffer from permanent neurological deficit. Rupture is still rather rare as it is estimated that 50 to 80 percent of IAs do not rupture during a lifetime, but a staggering 3.2% prevalence of unruptured IAs (1 in 30 people) still leads 500,000 people to die worldwide each year due to rupture and half are younger than 50. The estimated overall direct and indirect costs of the treatment are 138 million USD per year. Clearly, there is a huge demand for constant improvement of tools and methods for clinical management of IAs.
Although treatment options like neurosurgical clipping or endovascular coiling are well established for large (dome height)10 mm) and symptomatic IAs, there is an urgent need to improve clinical management of smaller IAs. These are often asymptomatic and discovered incidentally using 3D-DSA, CTA or MRA imaging, whereas corresponding treatment risk/benefit ratio is strongly in favor of the "no treatment" option, since small IAs rupture more frequently during treatment than larger ones. Similar considerations arise in the management of treated IAs irrespective of their size, where the prognostic factors and clinical guidelines to prevent a rare, but potentially fatal recurrence or rebleeding are yet to be established.
Recent studies indicate that in-vivo 3D-DSA, CTA or MRA based morphologic measurements such as aneurysm size, aspect ratio (dome height/neck width), aneurysm-to-vessel size ratio and other shape indices are important independent factors contributing to high risk of rupture. Compared to hemodynamic indices like wall shear stress and pulsatility index, the morphologic indices proved more reliable for estimating rupture risk of large aneurysms. The morphologic indices mainly focus on large saccular IAs, while they are rather unspecific for small IAs due to their gross shape description. A recent study indicated that the risk is much higher for IAs that grow over time, irrespective of the initial size. Thus, novel and better risk factors may be established from longitudinal 3D-DSA, CTA or MRA images by quantifying subtle morphologic changes of the observed IA.
The main goal of the proposed project is to develop innovative methods and systems based on in-vivo imaging in order to detect and diagnose IAs and perform pre- and post-treatment assessment and follow-up using quantitative morphologic descriptors. All the theoretical, computational and translational activities will be concentrated around the following themes: 1) develop accurate and reliable modality-independent (3D-DSA, CTA or MRA) detector using advanced convolutional neural networks so as to capture small IAs as early as possible; 2) develop novel methods for vasculature segmentation and IA isolation from parent vessels, and novel, more descriptive morphologic measures; 3) develop novel multi-modality deformable registration for normalization of follow-up images, and novel morphologic measures that quantify IA growth; 4) develop standardized validation datasets using real 3D-DSA, CTA or MRA images and perfom objective and rigorous validation of novel methods, and prospectively validate them in clinical screening studies; 5) translate the developed methods and systems into clinical environment and disseminate results into relevant scientific communities. Ultimately, the
Significance for science
The proposed interdisciplinary project addresses important challenges in the fields of medical image analysis and computer-aided diagnosis, neurology, neuroradiology and neuroimaging, clinical radiology, and clinical disease management.
Relevant contributions to automated medical image analysis, resulting mainly from Activities 1-7:
- increased understanding and contribution to the awareness of the potentials and pitfalls of each of the tested image analysis method, achieved through scientifically rigorous and objective validation of existing state of the art and novel image analysis methods
- promotion of further research and development of image analysis methods, and facilitation of objective evaluation and comparison studies, by making standard validation image data sets with benchmark results publicly available to the research community
- formulation of guidelines for quantitative measurements in cross-sectional and longitudinal studies
Relevant contributions to neurology, neuroradiology, neuroimaging, and clinical radiology, resulting mainly from Activities 8-12:
- early imaging-based detection and diagnosis of small aneurysms to enable timely clinical management of the disease
- advancement of understanding and monitoring of progression of aneurysm growth, as one of key prognostic factors of often fatal rupture event, achieved through objective and reproducible in vivo 3D-DSA, CTA and MRA imaging-based measurements
- further evolution and improvement of related clinical guidelines and treatment plans, by promoting objective and quantitative measurements in addition to the more subjective assessment
- more reliable characterization of individual patients for personalized treatment
Relevant contributions translational, multidisciplinary research, resulting mainly from Activities 13-15:
- introduction of innovative paraclinical methods into real clinical environment
- publications in international high-impact factor cross-disciplinary journals
The topic of the proposed research project also opens a number of possibilities for multidisciplinary research collaboration with academic and healthcare institutions, and industry partners home and abroad. Besides the University Medical Center Ljubljana, the project involves an eminent partners from USA (prof. dr. Aichi Chien, University of California Los Angeles (UCLA), Chien Lab, CA, USA) has extensive experience and rich publication track in the developement and validation of morphologic and hemodynamic analyses of intracranial aneurysms. We expect that mutual exchange of knowledge, views, findings, needs and innovative solutions, will lead to novel discoveries and will reveal new and challenging research opportunities.
Significance for the country
Social impact indicator: improved medical treatment and health
The innovative methods, approaches, technologies and systems for quantitative medical image analysis developed as part of the proposed project will have an immediate impact on those involved in the research and treatment of cerebrovascular diseases, enabling them to help patients quicker and better. The implementation and validation of quantitative measurements based on automated image analysis will enable the clinicians to better understand and monitor the progression of cerebrovascular diseases, to devise independent and objective clinical guidelines for objective and fully-informed diagnosis and treatment decisions, to improve the education and training processes and to foster the use of new and innovative research methodologies.
Clinicians involved in the treatment of patients with intracranial aneurysm will benefit from a computer-aided detection and quantification of the aneurysm in three important ways:
- detect aneurysms early, while they are small and before a rupture, which is otherwise often a fatal event
- the ability to estimate the risk of rupture and select best treatment strategy based on accurate quantitative assessment of aneurysm morphology
- the ability to quantitatively monitor the progression of aneurysm growth and predict rupture at least two years ahead
Economic/commercial indicator: new products/services, increased employment, reduced costs
The increased use of novel quantitative imaging methods has the potential to reduce both the time and cost of treatment. In case of intracranial aneurysms, the cost of clipping via open brain surgery more than doubles in cost after the aneurysm has ruptured. The cost of a brain aneurysm treated by coiling, which is less invasive and is done through a catheter, increases by about 70% after the aneurysm has ruptured. Hence, detecting aneurysms early, before they rupture, may immediately impact the relatively high costs associated with treatment of ruptured aneurysms, not to mentioned the indirect socio-economical costs for the loss of patient productivity and the costs of post-treatment care in case of poor outcome.
Quantitative analysis of angiographic images is an innovative paraclinical method for clinical management of cerebrovascular diseases. Such methods are increasingly more demanded by the healthcare providers since they enable objective and fully-informed decision-making. Large providers of healthcare systems are not highly specialized into niche and emerging markets, thus several start-up companies have focused onto such niche technologies (e.g. HeartFlow, www.heartflow.com). Based on our past experience in providing quantitative image reading services to clinicians (https://ms.quantim.eu) and in founding and developing high-tech companies, like Sensum (www.sensum.eu), which is considered to be the best and largest high-tech company founded by university faculty in Slovenia, we will definitely explore every opportunity to go beyond academic endeavor and write another successful story.
Most important scientific results
Interim report,
final report
Most important socioeconomically and culturally relevant results
Interim report,
final report
