Super-Resolution Musculoskeletal MRI Using Deep Learning

By A. Chaudhari, Zhongnan Fang, Feliks Kogan, Jeff P Wood, K. Stevens, Eric K. Gibbons, Jin Hyung Lee, Garry Gold, Brian A. Hargreaves. 2018

357 citations recorded by the source corpus.

To develop a super‐resolution technique using convolutional neural networks for generating thin‐slice knee MR images from thicker input slices, and compare this method with alternative through‐plane interpolation methods.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Surgery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Shenghong Ju
2. Arvid Lundervold
3. Chenyu You
4. Ge Wang
5. Guang Li
6. Zhen Zhao
7. Wenxiang Cong
8. Punam Kumar Saha

In Radiology, Nuclear Medicine and Imaging

1. Yi Zhang
2. Wenxiang Cong
3. Ge Wang
4. Arvid Lundervold
5. Punam Kumar Saha
6. Jong Chul Ye
7. Hengyong Yu
8. Yanbo Zhang

In Surgery

1. Arvid Lundervold

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “generating super-resolution single (no reference information) and multi-contrast (applying a high-resolution image of another modality as reference) brain MR images using CNNs [196]; constructing superresolution brain MRI by a CNN stacked by multi-scale fusion units [197]; and super-resolution muscu”
• Deep learning in generating radiology reports: A survey (2020, neutral): “So far, DL has been successfully applied to medical image analysis and acknowledged as a powerful tool for image classification [44], lesion detection [45], segmentation [46], content-based image retrieval (CBIR) [47], report generation from images, and image generation and enhancement [48].”
• CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE) (2018, methodology): “For several imaging modalities, DL-based SR methods were successfully developed [38]–[41].”