Dynamic lung volumetric parameters are useful for clinical assessment of many thoracic disorders, given that respiration is a dynamic process. Estimation of such parameters based on imaging and analysis is an important goal to achieve if implementation in routine clinical practice is to become a reality. Compared to CT, dynamic thoracic MRI has several advantages including better soft tissue contrast, lack of ionizing radiation, and flexibility in selecting scanning planes. 4D dynamic MRI seems to be the best choice for some clinical applications, notwithstanding the major limitation of a long image acquisition time (~45 minutes). Therefore, approaches to acquire images and estimate volumetric parameters rapidly is highly desirable in dynamic MRI-based clinical applications. In this paper, we present a technique for estimating lung volumetric parameters from limited-slices dynamic thoracic MRI, greatly reducing the number of slices to be scanned and therefore also the time required for image acquisition. We demonstrate a relative RMS error of predicted lung volumes of less than 5% by utilizing only 5 sagittal MRI slices through each lung compared to the current full scan involving about 20 slices per lung. As such, this approach can lead to time-saving during scan acquisition and therefore increased patient comfort and convenience for practical real-world clinical applications. This may potentially also improve image quality and usability due to the reduction of patient motion, abnormal breathing patterns, etc. ensuing from improved patient comfort and scan duration.
Retrospective 4D image construction from continuously acquired 2D slices is a necessary step to achieve high-quality 4D images. Self-gating methods, which extract breathing signals only from image information without any external gating technology, have much potential, such as in pediatric patients with thoracic insufficiency syndrome (TIS) who suffer from extreme malformations of the chest wall, diaphragm, and spine, leading to breathing that is very complex with lots of abnormal respiration cycles, including very deep or shallow cycles. Existing methods do not work well in this clinical scenario and most are not fully automatic, requiring some manual interactive operations. In this paper, we propose a fully automatic 4D dMRI construction method based on the concept of flux to address the 4D image construction from 2D slices of subjects with complex respiration. Firstly, we extract the breathing signal for each location based on the flux of the optical flow vector field of the body region from the image series. Then, we give a full analysis for all cycles and extract several normal ones and map them to one cosine respiration model for each location. After that, we re-sample one normal cycle from the respiration model for each location independently. All of these resampled normal cycles form the final constructed 4D image. Qualitative and quantitative evaluations on 25 subjects show that the proposed method can handle datasets from subjects with more complex respiration and achieves good self consistency results while maintaining time and space continuity.
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