Single projection driven real-time multi-contrast (SPIDERM) MR imaging using pre-learned spatial subspace and linear transformation.
Pei HanJunzhou ChenJiayu XiaoFei HanZhehao HuWensha YangMinsong CaoDiane C LingDebiao LiAnthony G ChristodoulouZhaoyang FanPublished in: Physics in medicine and biology (2022)
Objective. To develop and test the feasibility of a novel Single ProjectIon DrivEn Real-time Multi-contrast (SPIDERM) MR imaging technique that can generate real-time 3D images on-the-fly with flexible contrast weightings and a low latency. Approach. In SPIDERM, a 'prep' scan is first performed, with sparse k-space sampling periodically interleaved with the central k-space line (navigator data), to learn a subject-specific model, incorporating a spatial subspace and a linear transformation between navigator data and subspace coordinates. A 'live' scan is then performed by repeatedly acquiring the central k-space line only to dynamically determine subspace coordinates. With the 'prep'-learned subspace and 'live' coordinates, real-time 3D images are generated on-the-fly with computationally efficient matrix multiplication. When implemented based on a multi-contrast pulse sequence, SPIDERM further allows for data-driven image contrast regeneration to convert real-time contrast-varying images into contrast-frozen images at user's discretion while maintaining motion states. Both digital phantom and in-vivo experiments were performed to evaluate the technical feasibility of SPIDERM. Main results. The elapsed time from the input of the central k-space line to the generation of real-time contrast-frozen 3D images was approximately 45 ms, permitting a latency of 55 ms or less. Motion displacement measured from SPIDERM and reference images showed excellent correlation (R2≥0.983). Geometric variation from the ground truth in the digital phantom was acceptable as demonstrated by pancreas contour analysis (Dice ≥ 0.84, mean surface distance ≤ 0.95 mm). Quantitative image quality metrics showed good consistency between reference images and contrast-varying SPIDREM images in in-vivo studies (meanNMRSE=0.141,PSNR=30.12,SSIM=0.88). Significance. SPIDERM is capable of generating real-time multi-contrast 3D images with a low latency. An imaging framework based on SPIDERM has the potential to serve as a standalone package for MR-guided radiation therapy by offering adaptive simulation through a 'prep' scan and real-time image guidance through a 'live' scan.
Keyphrases
- deep learning
- magnetic resonance
- contrast enhanced
- convolutional neural network
- computed tomography
- optical coherence tomography
- image quality
- radiation therapy
- magnetic resonance imaging
- stem cells
- men who have sex with men
- multiple sclerosis
- artificial intelligence
- machine learning
- mass spectrometry
- risk assessment
- human health
- radiation induced
- virtual reality
- drosophila melanogaster
- locally advanced