Wykaz obszarów badawczych:
| # | Obszar badawczy | Dziedzina naukowa |
|---|---|---|
| 1 |
“Machine learning-based diagnosis of spine injuries using computed tomography” Computed tomography (CT) is a crucial imaging technique in medical diagnosis and is the preferred modality for assessing spinal trauma. However, the large volume of image data generated during tomographic examinations presents significant challenges for image analysis and diagnosis. Artificial intelligence (AI) offers the potential to enhance the speed and accuracy of diagnosis in such cases. This research aims to explore the application of machine learning (ML) methods and deep neural networks (DNNs) for the automated detection of traumatic vertebral body injuries. The study will focus on classifying vertebral fractures, distinguishing between traumatic and non-traumatic cases. The learning dataset will be constructed using trauma examination records from a clinical hospital in Warsaw. These records will consist of X-ray spine tomography studies in DICOM format, annotated by experienced radiologists. The AO Spine Classification system for thoracolumbar injuries will serve as the framework for categorizing spinal fractures. To address the computational challenges posed by the high dimensionality of tomographic data (i.e., the large number of voxels per examination), methods for reducing data size will be employed. Various deep neural network architectures will be evaluated to determine their efficacy and performance in fracture classification. Furthermore, the interpretability and explainability of the developed ML-based approach will be analyzed using tools and techniques from Explainable AI (XAI). Efforts will be made to validate the reliability of the recommendations generated by the ML models, ensuring they align with clinical expertise and established diagnostic standards.
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Inżynieria Biomedyczna |
| 2 |
“Deep neural networks for image reconstruction in electrical capacitance tomography” Electrical capacitance tomography (ECT) enables the visualization of the spatial distribution of an object’s electrical permittivity. Image reconstruction in ECT presents a significant challenge as it is an ill-posed and ill-conditioned inverse problem. Advanced nonlinear algorithms, such as the Levenberg-Marquardt method, are iterative and computationally intensive, primarily due to the repeated calculation of the Jacobian matrix. Deep neural networks (DNNs) have emerged as a promising alternative for image reconstruction. This work will explore deep network architectures that not only match but significantly outperform classical methods in reconstruction quality. Synthetic data will be employed for supervised learning, and the performance of DNNs will be tested against real measurements. Application-specific training datasets will be analyzed, and their size will be expanded using data augmentation techniques. A comparative evaluation of various training datasets will also be conducted. To optimize the network, different loss functions and solvers will be utilized. Reconstructed images will be generated using DNNs and compared against a baseline provided by the Levenberg-Marquardt algorithm. Results will be assessed using selected image quality metrics. It is anticipated that deep networks will enhance the spatial resolution of ECT scanners. The insights gained from selecting deep networks, analyzing their architectures, and refining training strategies can potentially be applied to solve inverse problems in other fields. |
Inżynieria Biomedyczna |
| 3 |
“Capacitively coupled impedance tomography for anatomical and functional imaging” To date, electrical impedance tomography (EIT) using sinusoidal excitation has been regarded as the most promising electrical imaging technique for diagnostic medical applications. However, the high impedance at the electrode-skin contact remains a significant challenge, hindering both the development and practical implementation of this technology. To address these limitations, an alternative approach utilizing non-contact electrodes and pulse excitation will be explored. This method will involve signal shape analysis to determine both components of admittance. Such data will enable the reconstruction of images depicting electrical permittivity and conductivity. Capacitively coupled electrical tomography will be investigated using numerical and physical lung phantoms, with a focus on regional ventilation distribution. Key metrics such as measurement sensitivity, contrast, and spatial-temporal resolution of the resulting images will be evaluated. Non-linear iterative algorithms and deep learning techniques will be employed for image reconstruction. Real measurements will be conducted using a simplified thorax phantom designed to simulate the respiratory cycle. A prototype flexible sensor incorporating surface electrodes will be developed, along with a mechanical-electrical lung phantom. Measurements will be carried out using the 32-channel electrical capacitance tomograph EVT4, which was designed and constructed at ZEJiM.
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Inżynieria Biomedyczna |