Curvelet Transform-based volume fusion for correcting signal loss artifacts in Time-of-Flight Magnetic Resonance Angiography data

Deep learning methods for medical image fusion: A review

The image fusion methods based on deep learning has become a research hotspot in the field of computer vision in recent years. This paper reviews these methods from five aspects: Firstly, the principle and advantages of image fusion methods based on deep learning are expounded; Secondly, the image fusion methods are summarized in two aspects: End-to-End and Non-End-to-End, according to the different tasks of deep learning in the feature processing stage, the non-end-to-end image fusion methods are divided into two categories: deep learning for decision mapping and deep learning for feature extraction. According to the different types of the networks, the end-to-end image fusion methods are divided into three categories: image fusion methods based on Convolutional Neural Network, Generative Adversarial Network, and Encoder-Decoder Network; Thirdly, the application of the image fusion methods based on deep learning in medical image field is summarized from two aspects: method and data set; Fourthly, evaluation metrics commonly used in the field of medical image fusion are sorted out from 14 aspects; Fifthly, the main challenges faced by the medical image fusion are discussed from two aspects: data sets and fusion methods. And the future development direction is prospected. This paper systematically summarizes the image fusion methods based on the deep learning, which has a positive guiding significance for the in-depth study of multi modal medical images.

Accelerated free-breathing 3D whole-heart magnetic resonance angiography with a radial phyllotaxis trajectory, compressed sensing, and curvelet transform

PURPOSE

To develop and validate an accelerated free-breathing 3D whole-heart magnetic resonance angiography (MRA) technique using a radial k-space trajectory with compressed sensing and curvelet transform.

METHOD

A 3D radial phyllotaxis trajectory was implemented to traverse the centerline of k-space immediately before the segmented whole-heart MRA data acquisition at each cardiac cycle. The k-space centerlines were used to correct the respiratory-induced heart motion in the acquired MRA data. The corrected MRA data were then reconstructed by a novel compressed sensing algorithm using curvelets as the sparsifying domain. The proposed 3D whole-heart MRA technique (radial CS curvelet) was then prospectively validated against compressed sensing with a conventional wavelet transform (radial CS wavelet) and a standard Cartesian acquisition in terms of scan time and border sharpness.

RESULTS

Fifteen patients (females 10, median age 34-year-old) underwent 3D whole-heart MRA imaging using a standard Cartesian trajectory and our proposed radial phyllotaxis trajectory. Scan time for radial phyllotaxis was significantly shorter than Cartesian (4.88 ± 0.86 min. vs. 6.84 ± 1.79 min., P-value = 0.004). Radial CS curvelet border sharpness was slightly lower than Cartesian and, for the majority of vessels, was significantly better than radial CS wavelet (P-value < 0.050).

CONCLUSION

The proposed technique of 3D whole-heart MRA acquisition with a radial CS curvelet has a shorter scan time and slightly lower vessel sharpness compared to the Cartesian acquisition with radial profile ordering, and has slightly better sharpness than radial CS wavelet. Future work on this technique includes additional clinical trials and extending this technique to 3D cine imaging.