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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.08185 |
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| _version_ | 1866916485288427520 |
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| author | Mondal, Parmita Shields, Allison Bhurwani, Mohammad Mahdi Shiraz Williams, Kyle A Nagesh, Swetadri Vasan Setlur Siddiqui, Adnan H Ionita, Ciprian N |
| author_facet | Mondal, Parmita Shields, Allison Bhurwani, Mohammad Mahdi Shiraz Williams, Kyle A Nagesh, Swetadri Vasan Setlur Siddiqui, Adnan H Ionita, Ciprian N |
| contents | This study aims to mitigate these biases and enhance QA analysis by applying a path-length correction (PLC) correction, followed by singular value decomposition (SVD)-based deconvolution, to angiograms obtained through both in-silico and in-vitro methods. We utilized DSA data from in-silico and in-vitro patient-specific intracranial aneurysm models. To remove projection bias, PLC for various views were developed by co-registering the pre-existing 3D vascular geometry mask with the DSA projections, followed by ray tracing to determine paths across 3D vessel structures. These maps were used to normalize the logarithmic angiographic images, correcting for projection-induced foreshortening across different angles. Subsequently, we focused on eliminating injection bias by analyzing the corrected angiograms under varied projection views, injection rates, and flow conditions. Regions of interest at the aneurysm dome and inlet were placed to extract Time Density Curves for the lesion and the arterial input function, respectively. Using three standard SVD methodologies, we extracted the aneurysm Impulse Response function (IRF) and its associated parameters Peak Height (PHIRF), Area Under the Curve (AUCIRF), and Mean Transit Time (MTT). Our methodology employing PLC and SVD-based deconvolution ensures reliable quantitative angiographic measurements across varying conditions, supporting consistent assessments of disease severity and treatment efficacy. This approach significantly enhances intrapatient and intraprocedural reliability in neurovascular diagnostics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08185 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analysis of Quantitative Angiography using Projection Foreshortening Correction and Injection Bias Removal Mondal, Parmita Shields, Allison Bhurwani, Mohammad Mahdi Shiraz Williams, Kyle A Nagesh, Swetadri Vasan Setlur Siddiqui, Adnan H Ionita, Ciprian N Medical Physics This study aims to mitigate these biases and enhance QA analysis by applying a path-length correction (PLC) correction, followed by singular value decomposition (SVD)-based deconvolution, to angiograms obtained through both in-silico and in-vitro methods. We utilized DSA data from in-silico and in-vitro patient-specific intracranial aneurysm models. To remove projection bias, PLC for various views were developed by co-registering the pre-existing 3D vascular geometry mask with the DSA projections, followed by ray tracing to determine paths across 3D vessel structures. These maps were used to normalize the logarithmic angiographic images, correcting for projection-induced foreshortening across different angles. Subsequently, we focused on eliminating injection bias by analyzing the corrected angiograms under varied projection views, injection rates, and flow conditions. Regions of interest at the aneurysm dome and inlet were placed to extract Time Density Curves for the lesion and the arterial input function, respectively. Using three standard SVD methodologies, we extracted the aneurysm Impulse Response function (IRF) and its associated parameters Peak Height (PHIRF), Area Under the Curve (AUCIRF), and Mean Transit Time (MTT). Our methodology employing PLC and SVD-based deconvolution ensures reliable quantitative angiographic measurements across varying conditions, supporting consistent assessments of disease severity and treatment efficacy. This approach significantly enhances intrapatient and intraprocedural reliability in neurovascular diagnostics. |
| title | Analysis of Quantitative Angiography using Projection Foreshortening Correction and Injection Bias Removal |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2411.08185 |