Publications by authors named "Yernar Zhetpissov"
In minimally invasive procedures such as biopsies and prostate cancer brachytherapy, accurate needle placement remains challenging due to limitations in current tracking methods related to interference, reliability, resolution or image contrast. This often leads to frequent needle adjustments and reinsertions. To address these shortcomings, we introduce an optimized needle shape-sensing method using a fully distributed grating-based sensor.
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- Needle insertion using flexible bevel tip needles is a key minimally-invasive technique for prostate cancer surgery, which allows for precise navigation around sensitive anatomy.
- The study introduces a hybrid deep learning and model-based method for predicting needle trajectory during insertion using a validated Lie-group theory approach, addressing the existing challenges in this area.
- The proposed method shows promising results, achieving an average prediction error of 1.03 mm in needle shape across various tissue models, and demonstrates the ability to further refine the predictive model through self-supervised learning and transfer learning techniques.
Article Synopsis
- Flexible needle insertion is critical in minimally-invasive prostate cancer surgeries, allowing for better navigation around sensitive structures and reduced patient discomfort.
- Sensors embedded in bevel-tip needles provide real-time feedback on needle shape, enhancing the accuracy of needle placements during procedures without direct visualization.
- The study compares single-core and multicore fiber-based shape-sensing in needles, finding both perform similarly in phantom tissue, but the single-core fiber significantly outperforms in real tissue, suggesting avenues for future research and optimization in sensorized needles.
Article Synopsis
- Flexible needle insertion techniques are widely used in minimally-invasive prostate cancer procedures to enhance precision and patient comfort by avoiding sensitive areas.
- Embedded optical sensors in bevel-tip needles allow real-time feedback on the needle’s 3D shape during insertion without needing direct visualization.
- The study found that while single-core and multicore fiber sensors performed similarly in phantom tissue, the single-core sensor significantly outperformed the multicore in real tissue, suggesting future improvements in sensor design for enhanced needle placement accuracy.