UTE‐SENCEFUL: first results for 3D high‐resolution lung ventilation imaging

Abstract

Purpose This study aimed to develop a 3D MRI technique to assess lung ventilation in free‐breathing and without the administration of contrast agent. Methods A 3D‐UTE sequence with a koosh ball trajectory was developed for a 3 Tesla scanner. An oversampled k‐space was acquired, and the direct current signal from the k‐space center was used as a navigator to sort the acquired data into 8 individual breathing phases. Gradient delays were corrected, and iterative SENSE was used to reconstruct the individual timeframes. Subsequently, the signal changes caused by motion were eliminated using a 3D image registration technique, and ventilation‐weighted maps were created by analyzing the signal changes in the lung tissue. Six healthy volunteers and 1 patient with lung cancer were scanned with the new 3D‐UTE and the standard 2D technique. Image quality and quantitative ventilation values were compared between both methods. Results UTE‐based self‐gated noncontrast‐enhanced functional lung (SENCEFUL) MRI provided a time‐resolved reconstruction of the breathing motion, with a 49% increase of the SNR. Ventilation quantification for healthy subjects was in statistical agreement with 2D‐SENCEFUL and the literature, with a mean value of 0.11 ± 0.08 mL/mL for the whole lung. UTE‐SENCEFUL was able to visualize and quantify ventilation deficits in a patient with lung tumor that were not properly depicted by 2D‐SENCEFUL. Conclusion UTE‐SENCEFUL represents a robust MRI method to assess both morphological and functional information of the lungs in 3D. When compared to the 2D approach, 3D‐UTE offered ventilation maps with higher resolution, improved SNR, and reduced ventilation artifacts.

Validation of MRI for Volumetric Quantification of Atelectasis in the Perioperative Period: An Experimental Study in Swine

“…Quantitative assessment of lung aeration based on MRI has been very limited, mainly because of technical challenges of MR imaging (Ball et al, 2017b), for example, the prolonged time required for image acquisition, and the low proton density within the lung parenchyma. However, recent experimental data, ex vivo (Ball et al, 2018) and in-vivo (Kuethe et al, 2018) have suggested that lung MRI may be feasible and produce clinically valuable information (Ozcan et al, 2017), including functional imaging (Mendes Pereira et al, 2018). Ball et al observed in ex-vivo pig lungs that lung density was a linear function of MRI T2 signal intensity normalized to muscle intensity.…”

Free‐breathing self‐gated 4D lung MRI using wave‐CAIPI

“…The measured data were binned into partially overlapping breathing states, with the requirement that all breathing states contain about 21% of all acquired data. The respiratory cycle was resolved in eight breathing states, which is a typical range for dynamic 3D lung acquisitions 7,15 . This approach ensures that all k‐space bins (corresponding to the breathing states) contain a sufficient amount of data for a stable parallel imaging reconstruction.…”

“…In addition, the center of k‐space can be exploited as a navigator signal 4‐6 . Although some non‐Cartesian k‐space trajectories inherently measure this (direct current) DC signal in each repetition, 7 a small increase in measurement time has to be invested in Cartesian and in wave‐CAIPI (controlled aliasing in parallel imaging). The big advantage of these self‐gating techniques is that they do not require any additional hardware, which saves time during patient preparation and increases patient comfort.…”

Iterative motion‐compensation reconstruction ultra‐short TE (iMoCo UTE) for high‐resolution free‐breathing pulmonary MRI

“…External respiratory belts are widely used to indirectly track the motion by measuring the respiratory-induced abdomen stretching. An alternative way is to use the repeatedly acquired k-space center (DC), which is feasible for center-out UTE sequences, measuring the signal change caused by respiratory motion 7,11,13,14 . To more accurately and directly characterize subject motion, low spatial but high temporal resolution 2D/3D images could be reconstructed and used as a self-navigator 6,[15][16][17] .…”