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

• URL: http://arxiv.org/abs/2603.26764v1
• Date: Tue, 24 Mar 2026 00:18:21 GMT
• Title: Low Dose CT for Stroke Diagnosis: A Dual Pipeline Deep Learning Framework for Portable Neuroimaging
• Abstract summary: We present a deep learning framework for stroke classification from simulated low-dose CT (LDCT) brain scans for AI-assisted triage in mobile clinical environments.<n>We compare two pipelines: (1) direct classification of noisy LDCT images and (2) denoising followed by classification.<n>The best denoise-then-classify pipeline achieves 0.94 AUC and 0.91 accuracy at moderate dose levels, outperforming direct classification by up to 6% in select cases.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Portable CT scanners enable early stroke detection in prehospital and low-resource settings but require reduced radiation doses, introducing noise that degrades diagnostic reliability. We present a deep learning framework for stroke classification from simulated low-dose CT (LDCT) brain scans for AI-assisted triage in mobile clinical environments. Controlled Poisson noise is applied to high-dose CT images to simulate realistic LDCT conditions. We compare two pipelines: (1) direct classification of noisy LDCT images and (2) denoising followed by classification. Performance is evaluated across multiple dose levels using accuracy, sensitivity, and AUC. While denoising improves perceptual image quality, it does not consistently improve classification. In several settings, direct classification yields higher sensitivity, revealing a trade-off between perceptual quality and diagnostic utility. The best denoise-then-classify pipeline achieves 0.94 AUC and 0.91 accuracy at moderate dose levels, outperforming direct classification by up to 6% in select cases. This work establishes a reproducible baseline for LDCT stroke triage using hemorrhagic stroke data (RSNA dataset) and highlights the need for validation on ischemic cohorts and real-world portable CT systems.

Related papers

• Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling [51.330452704464925]
  We train a latent diffusion model on 1,046 PCCTs and 405,379 EICTs from 145 hospitals.<n>We construct a large-scale dataset of over 17,316 publicly available EICTs enhanced to PCCT-like quality.<n>Our results suggest emerging imaging advances can be systematically distilled into routine EICT using limited high-quality scans as reference.
  arXiv  Detail & Related papers  (2026-04-08T17:47:35Z)
• Neural Discrete Representation Learning for Sparse-View CBCT Reconstruction: From Algorithm Design to Prospective Multicenter Clinical Evaluation [64.42236775544579]
  Cone beam computed tomography (CBCT)-guided puncture has become an established approach for diagnosing and treating thoracic tumours.<n>DeepPriorCBCT is a three-stage deep learning framework that achieves diagnostic-grade reconstruction using only one-sixth of the conventional radiation dose.
  arXiv  Detail & Related papers  (2025-11-30T12:45:02Z)
• Assessing the performance of CT image denoisers using Laguerre-Gauss Channelized Hotelling Observer for lesion detection [1.0523436939538895]
  Deep learning methods are proposed to recover denoised images from noisy images acquired at low radiation.<n>In this work, we assessed the image quality of deep learning denoising algorithms by making use of visual perception- and data fidelity-based task-agnostic metrics.<n>When compared against normal-dose CT images, the deep learning denoisers outperformed low-dose CT based on metrics like the PSNR (by 2.4 to 3.8 dB) and SSIM (by 0.05 to 0.11)
  arXiv  Detail & Related papers  (2024-12-04T00:11:19Z)
• WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising [74.14134385961775]
  We introduce a novel self-supervised CT image denoising method called WIA-LD2ND, only using NDCT data. WIA-LD2ND comprises two modules: Wavelet-based Image Alignment (WIA) and Frequency-Aware Multi-scale Loss (FAM)
  arXiv  Detail & Related papers  (2024-03-18T11:20:11Z)
• Rotational Augmented Noise2Inverse for Low-dose Computed Tomography Reconstruction [83.73429628413773]
  Supervised deep learning methods have shown the ability to remove noise in images but require accurate ground truth. We propose a novel self-supervised framework for LDCT, in which ground truth is not required for training the convolutional neural network (CNN) Numerical and experimental results show that the reconstruction accuracy of N2I with sparse views is degrading while the proposed rotational augmented Noise2Inverse (RAN2I) method keeps better image quality over a different range of sampling angles.
  arXiv  Detail & Related papers  (2023-12-19T22:40:51Z)
• Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT [7.879949714759592]
  Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions. Deep learning(DL)-based methods were introduced, outperforming conventional denoising algorithms on this task due to their high model capacity. We propose a hybrid denoising approach consisting of a set of trainable joint bilateral filters (JBFs) combined with a convolutional DL-based denoising network.
  arXiv  Detail & Related papers  (2022-07-15T09:30:32Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• Generative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study [3.040206021972938]
  Radiomics is an area of research focusing on high throughput feature extraction from medical images. Generative models can improve the performance of low dose CT-based radiomics in different tasks.
  arXiv  Detail & Related papers  (2021-09-06T06:01:21Z)
• Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images [58.85481248101611]
  We propose a novel PCa detection network that incorporates a lesion-level cost-sensitive loss and an additional slice-level loss based on a lesion-to-slice mapping function. Our experiments based on 290 clinical patients concludes that 1) The lesion-level FNR was effectively reduced from 0.19 to 0.10 and the lesion-level FPR was reduced from 1.03 to 0.66 by changing the lesion-level cost.
  arXiv  Detail & Related papers  (2021-06-04T09:51:27Z)
• Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation Study [4.7849095200575045]
  Radiomic features are calculated from noisy images, such as low dose computed tomography (CT) scans. In this article, we investigate the possibility of improving the radiomic features calculated on noisy CTs by using generative models for denoising. The results show that denoising using encoder-decoder networks (EDN) and conditional generative adversarial networks (CGANs) can improve the radiomic features calculated on noisy CTs.
  arXiv  Detail & Related papers  (2021-04-30T15:18:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.