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arXiv · 2603.26764

Low-Dose CT for Stroke Diagnosis: A Dual-Pipeline Deep Learning Framework for Portable Neuroimaging

Rhea Ghosal·Ronok Ghosal·Eileen Lou
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Abstract

Portable CT scanners may support earlier stroke assessment, but reduced photon counts introduce noise that affects image quality and may alter automated classification. We compared direct classification of simulated low-dose slices with residual U-Net denoising followed by the same fixed classifier. Poisson noise was generated at photon-count scaling factors of 1, 5, 10, 20, and 40 using three deterministic seeds. The held-out set contained 809 slices, including 242 positive and 567 negative slices. Direct classification reached its highest mean ROC-AUC at a scaling factor of 20 (0.937 +/- 0.002). Denoising followed by classification reached 0.842 +/- 0.005 at a scaling factor of 1 and declined to 0.655 +/- 0.002 at a scaling factor of 40. Meanwhile, reconstruction quality rose steadily from 21.92 to 40.91 dB PSNR and from 0.761 to 0.987 SSIM. Both pathways had poor sensitivity at a fixed 0.5 cutoff because their classification scores were concentrated near zero. Patient-level analysis was limited by the test-set composition: all 10 patients were positive under the mask-derived patient label, preventing estimation of patient-level AUC and specificity. Overall, higher reconstruction fidelity did not translate into better discrimination by the fixed classifier except at the lowest photon-count setting.

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Rhea Ghosal, Ronok Ghosal, Eileen Lou. 2026-03-24. Low-Dose CT for Stroke Diagnosis: A Dual-Pipeline Deep Learning Framework for Portable Neuroimaging. https://arxiv.org/abs/2603.26764

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