An Efficient and Robust Method for Chest X-Ray Rib Suppression that Improves Pulmonary Abnormality Diagnosis

Xü, Dong; Xu, Qifan; Kevin, Nhieu,; Ruan, Dan; Sheng, Ke

doi:10.48550/arxiv.2302.09696

Cited by 2 publications

(1 citation statement)

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“…The scanning parameters were-TE = 1.35 ms, TR = 4.05 ms, matrix size = 260 × 320, in-plane resolution = 1.09 mm × 1.09 mm and slice thickness = 3 mm. Mask-RCNN (He et al 2022) has been used for various types of tumor detection and segmentation (Xu et al 2023b, 2023c, Sogancioglu et al 2024. We employed this framework for the current task of liver GTV detection + segmentation with reconstructed accelerated images.…”

Section: Discussionmentioning

confidence: 99%

Paired conditional generative adversarial network for highly accelerated liver 4D MRI

Xu,

Miao,

Liu

et al. 2024

Phys. Med. Biol.

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Purpose: 4D MRI with high spatiotemporal resolution is desired for image-guided liver radiotherapy. Acquiring densely sampled k-space data is time-consuming. Accelerated acquisition with sparse sampling is desirable but often causes degraded image quality or long reconstruction time. We propose the Reconstruct Paired Conditional Generative Adversarial Network (Re-Con-GAN) for shortening the 4D MRI reconstruction time while maintaining the reconstruction quality. Methods: Patients underwent free-breathing liver 4D MRI were included in the study. Fully- and retrospectively under-sampled data at 3, 6 and 10 times (3x, 6x and 10x) were first reconstructed using the nuFFT algorithm. Re-Con-GAN then trained input and output in pairs. Three types of networks, ResNet9, UNet and reconstruction swin transformer, were explored as generators. PatchGAN was selected as the discriminator. Re-Con-GAN processed the data (3D+t) as temporal slices (2D+t). A total of 48 patients with 12332 temporal slices were split into training (37 patients with 10721 slices) and test (11 patients with 1611 slices). Compressed sensing (CS) reconstruction with spatiotemporal sparsity constraint was used as a benchmark. Reconstructed image quality was further evaluated with a liver gross tumor volume (GTV) localization task using Mask-RCNN trained from a separate 3D static liver MRI dataset (70 patients; 103 GTV contours).Results: Re-Con-GAN consistently achieved comparable/better PSNR, SSIM, and RMSE scores compared to CS/UNet models. The inference time of Re-Con-GAN, UNet and CS are 0.15s, 0.16s, and 120s. The GTV detection task showed that Re-Con-GAN and CS, compared to UNet, better improved the dice score (3x Re-Con-GAN 80.98%; 3x CS 80.74%; 3x UNet 79.88%) of unprocessed under-sampled images (3x 69.61%). Conclusion: A generative network with adversarial training is proposed with promising and efficient reconstruction results demonstrated on an in-house dataset. The rapid and qualitative reconstruction of 4D liver MR has the potential to facilitate online adaptive MR-guided radiotherapy for liver cancer.

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Section: Discussionmentioning

confidence: 99%