Self-calibrated correlation imaging with k-space variant correlation functions

AI-assisted compressed sensing and parallel imaging sequences for MRI of patients with nasopharyngeal carcinoma: comparison of their capabilities in terms of examination time and image quality

OBJECTIVE

To compare examination time and image quality between artificial intelligence (AI)-assisted compressed sensing (ACS) technique and parallel imaging (PI) technique in MRI of patients with nasopharyngeal carcinoma (NPC).

METHODS

Sixty-six patients with pathologically confirmed NPC underwent nasopharynx and neck examination using a 3.0-T MRI system. Transverse T2-weighted fast spin-echo (FSE) sequence, transverse T1-weighted FSE sequence, post-contrast transverse T1-weighted FSE sequence, and post-contrast coronal T1-weighted FSE were obtained by both ACS and PI techniques, respectively. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and duration of scanning of both sets of images analyzed by ACS and PI techniques were compared. The images from the ACS and PI techniques were scored for lesion detection, margin sharpness of lesions, artifacts, and overall image quality using the 5-point Likert scale.

RESULTS

The examination time with ACS technique was significantly shorter than that with PI technique (p < 0.0001). The comparison of SNR and CNR showed that ACS technique was significantly superior with PI technique (p < 0.005). Qualitative image analysis showed that the scores of lesion detection, margin sharpness of lesions, artifacts, and overall image quality were higher in the ACS sequences than those in the PI sequences (p < 0.0001). Inter-observer agreement was evaluated for all qualitative indicators for each method, in which the results showed satisfactory-to-excellent agreement (p < 0.0001).

CONCLUSION

Compared with the PI technique, the ACS technique for MR examination of NPC can not only shorten scanning time but also improve image quality.

CLINICAL RELEVANCE STATEMENT

The artificial intelligence (AI)-assisted compressed sensing (ACS) technique shortens examination time for patients with nasopharyngeal carcinoma, while improving the image quality and examination success rate, which will benefit more patients.

KEY POINTS

• Compared with the parallel imaging (PI) technique, the artificial intelligence (AI)-assisted compressed sensing (ACS) technique not only reduced examination time, but also improved image quality. • Artificial intelligence (AI)-assisted compressed sensing (ACS) pulls the state-of-the-art deep learning technique into the reconstruction procedure and helps find an optimal balance of imaging speed and image quality.

Optical Flow Analysis of Left Ventricle Wall Motion with Real-Time Cardiac Magnetic Resonance Imaging in Healthy Subjects and Heart Failure Patients

In cardiology, magnetic resonance imaging (MRI) provides a clinical standard for measuring ventricular volumes. Owing to their reliability, volumetric measurements with cardiac MRI have become an essential tool for quantitative assessment of ventricular function. However, as volumetric indices are indirectly related to myocardial motion that drives ventricular filling and ejection, cardiac MRI cannot provide comprehensive evaluation of ventricular performance. To overcome this limitation, the presented work sought to measure ventricular wall motion directly with optical flow analysis of real-time cardiac MRI. By modeling left ventricle (LV) walls in real-time images based on myocardial architecture, we developed an optical flow approach to analyzing LV radial and circumferential wall motion for improved quantitative assessment of ventricular function. For proof-of-concept, a cardiac MRI study was conducted with healthy volunteers and heart failure (HF) patients. It was found that, as real-time images provided sufficient temporal information for correlation analysis between different LV wall motion velocity components, optical flow assessment detected the difference of ventricular performance between the HF patients and the healthy volunteers more effectively than volumetric measurements. We expect that this model-based optical flow assessment with real-time cardiac MRI would offer intricate analysis of ventricular function beyond conventional volumetric measurements.

Real-time cardiac MRI with radial acquisition and k-space variant reduced-FOV reconstruction

This work aims to demonstrate that radial acquisition with k-space variant reduced-FOV reconstruction can enable real-time cardiac MRI with an affordable computation cost. Due to non-uniform sampling, radial imaging requires k-space variant reconstruction for optimal performance. By converting radial parallel imaging reconstruction into the estimation of correlation functions with a previously-developed correlation imaging framework, Cartesian k-space may be reconstructed point-wisely based on parallel imaging relationship between every Cartesian datum and its neighboring radial samples. Furthermore, reduced-FOV correlation functions may be used to calculate a subset of Cartesian k-space data for image reconstruction within a small region of interest, making it possible to run real-time cardiac MRI with an affordable computation cost. In a stress cardiac test where the subject is imaged during biking with a heart rate of >100 bpm, this k-space variant reduced-FOV reconstruction is demonstrated in reference to several radial imaging techniques including gridding, GROG and SPIRiT. It is found that the k-space variant reconstruction outperforms gridding, GROG and SPIRiT in real-time imaging. The computation cost of reduced-FOV reconstruction is ~2 times higher than that of GROG. The presented work provides a practical solution to real-time cardiac MRI with radial acquisition and k-space variant reduced-FOV reconstruction in clinical settings.