Feasibility of online non-rigid motion correction for high-resolution supine breast MRI.

PURPOSE: Conventional breast MRI is performed in the prone position with a dedicated coil. This allows high-resolution images without breast motion, but the patient position is inconsistent with that of other breast imaging modalities or interventions. Supine breast MRI may be an interesting alternative, but respiratory motion becomes an issue. Motion correction methods have typically been performed offline, for instance, the corrected images were not directly accessible from the scanner console. In this work, we seek to show the feasibility of a fast, online, motion-corrected reconstruction integrated into the clinical workflow. METHODS: Fully sampled T2 -weighted (T2 w) and accelerated T1 -weighted (T1 w) breast supine MR images were acquired during free-breathing and were reconstructed using a non-rigid motion correction technique (generalized reconstruction by inversion of coupled systems). Online reconstruction was implemented using a dedicated system combining the MR raw data and respiratory signals from an external motion sensor. Reconstruction parameters were optimized on a parallel computing platform, and image quality was assessed by objective metrics and by radiologist scoring. RESULTS: Online reconstruction time was 2 to 2.5 min. The metrics and the scores related to the motion artifacts significantly improved for both T2 w and T1 w sequences. The overall quality of T2 w images was approaching that of the prone images, whereas the quality of T1 w images remained significantly lower. CONCLUSION: The proposed online algorithm allows a noticeable reduction of motion artifacts and an improvement of the diagnostic quality for supine breast imaging with a clinically acceptable reconstruction time. These findings serve as a starting point for further development aimed at improving the quality of T1 w images.

A hardware and software system for MRI applications requiring external device data.

PURPOSE: Numerous MRI applications require data from external devices. Such devices are often independent of the MRI system, so synchronizing these data with the MRI data is often tedious and limited to offline use. In this work, a hardware and software system is proposed for acquiring data from external devices during MR imaging, for use online (in real-time) or offline. METHODS: The hardware includes a set of external devices - electrocardiography (ECG) devices, respiration sensors, microphone, electronics of the MR system etc. - using various channels for data transmission (analog, digital, optical fibers), all connected to a server through a universal serial bus (USB) hub. The software is based on a flexible client-server architecture, allowing real-time processing pipelines to be configured and executed. Communication protocols and data formats are proposed, in particular for transferring the external device data to an open-source reconstruction software (Gadgetron), for online image reconstruction using external physiological data. The system performance is evaluated in terms of accuracy of the recorded signals and delays involved in the real-time processing tasks. Its flexibility is shown with various applications. RESULTS: The real-time system had low delays and jitters (on the order of 1 ms). Example MRI applications using external devices included: prospectively gated cardiac cine imaging, multi-modal acquisition of the vocal tract (image, sound, and respiration) and online image reconstruction with nonrigid motion correction. CONCLUSION: The performance of the system and its versatile architecture make it suitable for a wide range of MRI applications requiring online or offline use of external device data.

Assessing the neuroanatomy knowledge and spatial ability of radiotherapy technologist undergraduates using an interactive volumetric simulation tool-the RadioLOG project.

OBJECTIVE: To assess the use of a volumetric image display simulation tool (VDST) for the evaluation of applied radiological neuroanatomy knowledge and spatial understanding of radiotherapy technologist (RTT) undergraduates. METHODS: Ninety-two third-year RTT students from three French RTT schools took an examination using software that allows visualization of multiple volumetric image series. To serve as a reference, 77 first- and second-year undergraduates, as well as ten senior neuroradiologists, took the same examination. The test included 13 very-short-answer questions (VSAQ) and 21 exercises in which examinees positioned markers onto preloaded brain MR images from a healthy volunteer. The response time was limited. Each correct answer scored 100 points, with a maximum possible test score of 3,400 (VSAQ = 1,300; marker exercise = 2,100). Answers were marked automatically for the marker positioning exercise and semi-automatically for the VSAQs against prerecorded expected answers. RESULTS: Overall, the mean test score was 1,787 (150-3,300) and the standard deviation was 781. Scores were highly significantly different between all evaluated groups (p < 0.001). The interoperator reproducibility was 0.90. All the evaluated groups could be discriminated by VSAQ, marker, and overall total scores independently (p ≤ 0.0001 to 0.001). The test was able to discriminate between the three schools either by VSAQ scores (p < 0.001 to 0.02) or by overall total score (p < 0.001 to 0.05). CONCLUSION: This software is a high-quality evaluation tool for the assessment of radiological neuroanatomy knowledge and spatial understanding in RTT undergraduates. KEY POINTS: • This VDST allows volumetric image analysis of MR studies. • A high reliability test could be created with this tool. • Test scores were strongly associated with the examinee expertise level.