Deep learning in medical image super resolution: a review

Yang, Hujun; Wang, Zhongyang; Liu, Xinyao; Li, Chuangang; Xin, Junchang; Wang, Zhiqiong

doi:10.1007/s10489-023-04566-9

Cited by 12 publications

(2 citation statements)

References 141 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, 7T MRI may show improved performance in other downstream tasks such as segmentation, real‐time tumor tracking, and the generation of other synthetic scans like synthetic CT. Synthetic 7T ADC maps not only mitigate artifacts in original images, but also suppress imprecision in heterogeneous regions for segmentation 28–30 …”

Section: Discussionmentioning

confidence: 99%

High‐resolution 3T to 7T ADC map synthesis with a hybrid CNN‐transformer model

Eidex,

Wang,

Safari

et al. 2024

Medical Physics

View full text Add to dashboard Cite

Background7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion‐weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifacts.PurposeTo address these issues, we propose a hybrid CNN‐transformer model to synthesize high‐resolution 7T ADC maps from multimodal 3T MRI.MethodsThe Vision CNN‐Transformer (VCT), composed of both Vision Transformer (ViT) blocks and convolutional layers, is proposed to produce high‐resolution synthetic 7T ADC maps from 3T ADC maps and 3T T1‐weighted (T1w) MRI. ViT blocks enabled global image context while convolutional layers efficiently captured fine detail. The VCT model was validated on the publicly available Human Connectome Project Young Adult dataset, comprising 3T T1w, 3T DWI, and 7T DWI brain scans. The Diffusion Imaging in Python library was used to compute ADC maps from the DWI scans. A total of 171 patient cases were randomly divided into 130 training cases, 20 validation cases, and 21 test cases. The synthetic ADC maps were evaluated by comparing their similarity to the ground truth volumes with the following metrics: peak signal‐to‐noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE). In addition,ResultsThe results are as follows: PSNR: 27.0 ± 0.9 dB, SSIM: 0.945 ± 0.010, and MSE: 2.0E‐3 ± 0.4E‐3. Both qualitative and quantitative results demonstrate that VCT performs favorably against other state‐of‐the‐art methods. We have introduced various efficiency improvements, including the implementation of flash attention and training on 176×208 resolution images. These enhancements have resulted in the reduction of parameters and training time per epoch by 50% in comparison to ResViT. Specifically, the training time per epoch has been shortened from 7.67 min to 3.86 min.ConclusionWe propose a novel method to predict high‐resolution 7T ADC maps from low‐resolution 3T ADC maps and T1w MRI. Our predicted images demonstrate better spatial resolution and contrast compared to 3T MRI and prediction results made by ResViT and pix2pix. These high‐quality synthetic 7T MR images could be beneficial for disease diagnosis and intervention, producing higher resolution and conformal contours, and as an intermediate step in generating synthetic CT for radiation therapy, especially when 7T MRI scanners are unavailable.

show abstract

Section: Discussionmentioning

confidence: 99%

High‐resolution 3T to 7T ADC map synthesis with a hybrid CNN‐transformer model

Eidex,

Wang,

Safari

et al. 2024

Medical Physics

View full text Add to dashboard Cite

show abstract

“…Medical images often suffer from noise, artifacts, and limited resolution due to the physical constraints of the imaging devices. Therefore, developing effective and efficient methods for medical image super-resolution is a challenging and promising research topic, searching to obtain previously unachievable details and resolution [116,117].…”

Section: Image and Model Enhancement For Improved Analysismentioning

confidence: 99%

How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications

Pinto-Coelho

2023

Bioengineering

View full text Add to dashboard Cite

The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

show abstract