Developing the Head CT for stroke imaging

Published:
November 18, 2024
by
Thomas McSkimming

Stroke patients attended by mobile stroke imaging are 46% more likely to have no residual symptoms when assessed 90 days after treatment, compared to patients attended by regular emergency services [1]. However, the cost-effectiveness of current Mobile Stroke Units is closely tied to population density, exacerbating the healthcare equity gap between rural and urban patients [2][3].

Micro-X is helping to close this gap, bringing equity to remote patients, by developing a lightweight mobile stroke imaging solution that can be installed into regular road and air ambulances, without hindering their ability to attend non-stroke-related emergencies. This would help to decouple the cost effectiveness from population density, by allowing continued vehicle productivity during scanner downtime.

Figure 1 - L to R: Our 2019, fully manual prototype device, used in cadaver trials with Melbourne Brain Centre; the single-source, curved-detector test bench built in 2023 at Johns Hopkins University, using a single CNT tube taken from a Micro-X Rover; The 2024 multi-source prototype system in testing at Adelaide, using NEX technology mini-tubes affixed to a curved detector and including full-system rotation.

We developed our first head CT prototype in 2019 and completed cadaver testing at the Melbourne Brain Centre at the RMH. Since then, we have partnered with Johns Hopkins University to refine every aspect of the device, from physical configuration to reconstruction software, building five separate prototype devices along the way (see Figure 1). The latest prototype devices, which include genuine curved-panel detectors and incorporate discrete system motion, have produced high-quality images, capable of differentiating brain structures and detecting small structures in phantom bench studies (See Figure 2).

Figure 2 – Top: reconstructions of a Kyoto Kagaku Anthropomorphic Head CT phantom from our fully manual 2019 prototype device used in the Melbourne Brain Centre cadaver study. The three images represent our initial experimentation with iterative reconstruction methods. Bottom: Reconstruction of the same phantom from the curved-detector test bench at Johns Hopkins University, showing low-contrast brain structures such as ventricles and sulci, as well as contrast-enhanced blood vessels as small as 1mm in diameter.

The performance improvements we have achieved since the first prototype in 2019 are the result of multiple key factors. From a hardware perspective, the addition of 6 mini-CNT x-ray sources in the multi-source array (for a total of 21) and the inclusion of discrete rotational indexing, contribute to greater sampling density and angular coverage of the head without significantly increasing the weight or footprint of the device. Systematic analysis and optimization of geometric parameters [4] achieved greater sampling uniformity, ensuring sufficient resolution of structures in all regions of the brain.

The imaging software pipeline we have developed, alongside Johns Hopkins University’s I-Star Labs, for artifact correction and image reconstruction addresses the imaging challenges associated with our novel configuration. Notably, Adaptive Deep Scatter Estimation (ADSE) surpasses gold-standard scatter correction performance, while achieving a ~1000x reduction in computation time [5], eliminating a major source of risk for implementation in time-critical scenarios.

Phantom imaging on our 2024 prototype is in progress. Once completed and subject to receiving ethics approval, we will proceed to live patient imaging trials.

Figure 3 - Results of our novel Adaptive Deep Scatter Estimation (ADSE) algorithm on the Kyoto Kagaku Head Phantom using the Johns Hopkins curved-panel test bench. ADSE outperforms gold standard, iterative Monte Carlo (iMC) scatter correction, while simultaneously reducing computation time by 3 orders of magnitude.
References

[1] J. C. Grotta et al.,“Prospective, Multicenter, Controlled Trial of Mobile Stroke Units,” NewEngland Journal of Medicine, vol. 385, no. 11, pp. 971–981, Sep. 2021, doi:10.1056/NEJMoa2103879

[2] S. Walter et al., “Mobile Stroke Units - Cost-Effective or Just an Expensive Hype?,” Curr Atheroscler Rep, vol. 20, no. 10, p. 49, Oct. 2018, doi: 10.1007/s11883-018-0751-9.

[3] Z. Schofield, A. Balabanski, A.Place, P. Sharma, F. Gardiner, “The Stroke Report: Equitable care everywhere,”Apr. 2022, doi: 10.13140/RG.2.2.27490.81609.

[4] A. Lopez Montes, T. McSkimming,W. Zbijewski, J. H. Siewerdsen, C. Delnooz, A. Skeats, B. Gonzales, A. Sisniega, "Stationary x-ray tomography for hemorrhagic stroke imaging: sampling and resolution properties," Proc. SPIE 12304, 7thInternational Conference on Image Formation in X-Ray Computed Tomography, 123040P (17 October 2022); https://doi.org/10.1117/12.2646858

[5] T. McSkimming, A. Lopez-Montes,A. Skeats, C. Delnooz, B. Gonzales, E. Perilli, K. Reynolds, J. H. Siewerdsen,W. Zbijewski, A. Sisniega, "Multi-source semi-stationary CT for brain imaging: development and assessment of a prototype system and image formation algorithms," Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129251B (1 April 2024); https://doi.org/10.1117/12.3006970

Developing the Head CT for stroke imaging

Published:
November 18, 2024
by
Thomas McSkimming
Hear from one of the engineers developing the Head CT for mobile stroke diagnosis

Stroke patients attended by mobile stroke imaging are 46% more likely to have no residual symptoms when assessed 90 days after treatment, compared to patients attended by regular emergency services [1]. However, the cost-effectiveness of current Mobile Stroke Units is closely tied to population density, exacerbating the healthcare equity gap between rural and urban patients [2][3].

Micro-X is helping to close this gap, bringing equity to remote patients, by developing a lightweight mobile stroke imaging solution that can be installed into regular road and air ambulances, without hindering their ability to attend non-stroke-related emergencies. This would help to decouple the cost effectiveness from population density, by allowing continued vehicle productivity during scanner downtime.

Figure 1 - L to R: Our 2019, fully manual prototype device, used in cadaver trials with Melbourne Brain Centre; the single-source, curved-detector test bench built in 2023 at Johns Hopkins University, using a single CNT tube taken from a Micro-X Rover; The 2024 multi-source prototype system in testing at Adelaide, using NEX technology mini-tubes affixed to a curved detector and including full-system rotation.

We developed our first head CT prototype in 2019 and completed cadaver testing at the Melbourne Brain Centre at the RMH. Since then, we have partnered with Johns Hopkins University to refine every aspect of the device, from physical configuration to reconstruction software, building five separate prototype devices along the way (see Figure 1). The latest prototype devices, which include genuine curved-panel detectors and incorporate discrete system motion, have produced high-quality images, capable of differentiating brain structures and detecting small structures in phantom bench studies (See Figure 2).

Figure 2 – Top: reconstructions of a Kyoto Kagaku Anthropomorphic Head CT phantom from our fully manual 2019 prototype device used in the Melbourne Brain Centre cadaver study. The three images represent our initial experimentation with iterative reconstruction methods. Bottom: Reconstruction of the same phantom from the curved-detector test bench at Johns Hopkins University, showing low-contrast brain structures such as ventricles and sulci, as well as contrast-enhanced blood vessels as small as 1mm in diameter.

The performance improvements we have achieved since the first prototype in 2019 are the result of multiple key factors. From a hardware perspective, the addition of 6 mini-CNT x-ray sources in the multi-source array (for a total of 21) and the inclusion of discrete rotational indexing, contribute to greater sampling density and angular coverage of the head without significantly increasing the weight or footprint of the device. Systematic analysis and optimization of geometric parameters [4] achieved greater sampling uniformity, ensuring sufficient resolution of structures in all regions of the brain.

The imaging software pipeline we have developed, alongside Johns Hopkins University’s I-Star Labs, for artifact correction and image reconstruction addresses the imaging challenges associated with our novel configuration. Notably, Adaptive Deep Scatter Estimation (ADSE) surpasses gold-standard scatter correction performance, while achieving a ~1000x reduction in computation time [5], eliminating a major source of risk for implementation in time-critical scenarios.

Phantom imaging on our 2024 prototype is in progress. Once completed and subject to receiving ethics approval, we will proceed to live patient imaging trials.

Figure 3 - Results of our novel Adaptive Deep Scatter Estimation (ADSE) algorithm on the Kyoto Kagaku Head Phantom using the Johns Hopkins curved-panel test bench. ADSE outperforms gold standard, iterative Monte Carlo (iMC) scatter correction, while simultaneously reducing computation time by 3 orders of magnitude.
References

[1] J. C. Grotta et al.,“Prospective, Multicenter, Controlled Trial of Mobile Stroke Units,” NewEngland Journal of Medicine, vol. 385, no. 11, pp. 971–981, Sep. 2021, doi:10.1056/NEJMoa2103879

[2] S. Walter et al., “Mobile Stroke Units - Cost-Effective or Just an Expensive Hype?,” Curr Atheroscler Rep, vol. 20, no. 10, p. 49, Oct. 2018, doi: 10.1007/s11883-018-0751-9.

[3] Z. Schofield, A. Balabanski, A.Place, P. Sharma, F. Gardiner, “The Stroke Report: Equitable care everywhere,”Apr. 2022, doi: 10.13140/RG.2.2.27490.81609.

[4] A. Lopez Montes, T. McSkimming,W. Zbijewski, J. H. Siewerdsen, C. Delnooz, A. Skeats, B. Gonzales, A. Sisniega, "Stationary x-ray tomography for hemorrhagic stroke imaging: sampling and resolution properties," Proc. SPIE 12304, 7thInternational Conference on Image Formation in X-Ray Computed Tomography, 123040P (17 October 2022); https://doi.org/10.1117/12.2646858

[5] T. McSkimming, A. Lopez-Montes,A. Skeats, C. Delnooz, B. Gonzales, E. Perilli, K. Reynolds, J. H. Siewerdsen,W. Zbijewski, A. Sisniega, "Multi-source semi-stationary CT for brain imaging: development and assessment of a prototype system and image formation algorithms," Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129251B (1 April 2024); https://doi.org/10.1117/12.3006970

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