Quantitative analysis of the dynamic properties of thoraco-abdominal organs such as lungs during respiration could lead to more accurate surgical planning for disorders such as Thoracic Insufficiency Syndrome (TIS). This analysis can be done from semi-automatic delineations of the aforesaid organs in scans of the thoraco-abdominal body region. Dynamic magnetic resonance imaging (dMRI) is a practical and preferred imaging modality for this application, although automatic segmentation of the organs in these images is very challenging. In this paper, we describe an auto-segmentation system we built and evaluated based on dMRI acquisitions from 95 healthy subjects. For the three recognition approaches, the system achieves a best average location error (LE) of about one voxel for the lungs. The standard deviation (SD) of LE is about one to two voxels. For the delineation approach, the average Dice Coefficient (DC) is about 0.95 for the lungs. The standard deviation of DC is about 0.01 to 0.02 for the lungs. The system seems to be able to cope with the challenges posed by low resolution, motion blur, inadequate contrast, and image intensity non-standardness quite well. We are in the process of testing its effectiveness on TIS patient dMRI data and on other thoraco-abdominal organs including liver, kidneys, and spleen.
Breathing-related movement analysis is important in the study of many disease processes. The analysis of diaphragmatic motion via thoracic imaging in particular is important in a variety of disorders. Compared to computed tomography (CT) and fluoroscopy, dynamic magnetic resonance imaging (dMRI) has several advantages, such as better soft tissue contrast, no ionizing radiation, and greater flexibility in selecting scanning planes. In this paper, we propose a novel method for full diaphragmatic motion analysis via free-breathing dMRI. Firstly, after 4D dMRI image construction in a cohort of 51 normal children, we manually delineated the diaphragm on sagittal plane dMRI images at end-inspiration and end-expiration. Then, 25 points were selected uniformly and homologously on each hemi-diaphragm surface. Based on the inferior-superior displacements of these 25 points between end-expiration (EE) and end-inspiration (EI) time points, we obtained their velocities. We then summarized 13 parameters from these velocities for each hemi-diaphragm to provide a quantitative regional analysis of diaphragmatic motion. We observed that the regional velocities of the right hemi-diaphragm were almost always statistically significantly greater than those of the left hemi-diaphragm in homologous locations. There was a significant difference for sagittal curvatures but not for coronal curvatures between the two hemi-diaphragms. Using this methodology, future larger scale prospective studies may be considered to confirm our findings in the normal state and to quantitatively assess regional diaphragmatic dysfunction when various disease conditions are present.
Lung segmentation in dynamic thoracic magnetic resonance imaging (dMRI) is a critical step for quantitative analysis of thoracic structure and function in patients with respiratory disorders. Some semi-automatic and automatic lung segmentation methods based on traditional image processing models have been proposed mainly for CT with good performance. However, the low efficiency and robustness of these methods and inapplicability to dMRI make them unsuitable to segment the large numbers of dMRI datasets. In this paper, we present a novel automatic lung segmentation approach for dMRI based on two-stage convolutional neural networks (CNNs). In the first stage, we utilize the modified min-max normalization method to pre-process MRI for increasing the contrast between the lung and surrounding tissue and propose a corner-points and CNN based region of interest (ROI) detection strategy to extract the lung ROI from sagittal dMRI slices, which can reduce the negative influence of tissues located far away from the lung. In the second stage, we input the adjacent ROIs of target slices into the modified 2D U-Net to segment the lung tissue. The qualitative and quantitative results demonstrate that our approach achieves high accuracy and stability in terms of lung segmentation for dMRI.
Quantitative thoracic dynamic magnetic resonance imaging (QdMRI), a recently developed technique, provides a potential solution for evaluating treatment effects in thoracic insufficiency syndrome (TIS). In this paper, we integrate all related algorithms and modules during our work from the past 10 years on TIS into one system, named QdMRI, to address the following questions: (1) How to effectively acquire dynamic images? For many TIS patients, subjects are unable to cooperate with breathing instructions during image acquisition. Image acquisition can only be implemented under freebreathing conditions, and it is not feasible to use a surrogate device for tracing breathing signals. (2) How to assess the thoracic structures from the acquired image, such as lungs, left and right, separately? (3) How to depict the dynamics of thoracic structures due to respiration motion? (4) How to use the structural and functional information for the quantitative evaluation of surgical TIS treatment and for the design of the surgery plan? The QdMRI system includes 4 major modules: dynamic MRI (dMRI) acquisition, 4D image construction, image segmentation (from 4D image), and visualization of segmentation results, dynamic measurements, and comparisons of measurements from TIS patients with those from normal children. Scanning/image acquisition time for one subject is ~20 minutes, 4D image construction time is ~5 minutes, image segmentation of lungs via deep learning is 70 seconds for all time points (with the average DICE 0.96 in healthy children), and measurement computation time is 2 seconds.
Quantitative thoracic dynamic magnetic resonance imaging (QdMRI), a recently developed technique, provides a potential solution for evaluating treatment effects in thoracic insufficiency syndrome (TIS). In this paper, we demonstrate how lung parenchymal characteristics can be assessed via intensity properties in lung dynamic MRI, a modality suitable for use in pediatric patients. The QdMRI-based approach includes dynamic MR image acquisition, 4D image construction, image pre-processing with non-uniformity correction and intensity standardization, and lung segmentation from the 4D constructed image via a deep learning approach, as well as extraction of image parenchymal intensity properties from the segmented lungs and statistical comparisons among different clinical scenarios. We include 22 dMRI scans from 11 TIS patients (each with both pre-operative and post-operative scans) and 23 dMRI scans from healthy children. Two-sided paired t-testing is performed to compare lung intensity properties between end of expiration (EE) and end of inspiration (EI) within TIS patients (pre-operative and post-operative, separately) and normal children. We also compare the lung intensity properties at EE and EI among pre-operative TIS patients, post-operative TIS patients, and normal children. Experimental results show that lung (T2) intensity at EI is significantly lower than that at EE and lung intensity of postoperative TIS patients is significantly lower than that in pre-operative TIS patients and closer to that of normal children than to that of pre-operative TIS patients, indicating improvement in lung aeration. To our knowledge, this is the first study to provide a quantitative dynamic functional method to analyze lung parenchyma during tidal breathing on dynamic MRI in both healthy children and pediatric patients with TIS.
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.
4D thoracic images constructed from free-breathing 2D slice acquisitions based on dynamic magnetic resonance imaging (dMRI) provide clinicians the capability of examining the dynamic function of the left and right lungs, left and right hemidiaphragms, and left and right chest wall separately for thoracic insufficiency syndrome (TIS) treatment [1]. There are two shortcomings of the existing 4D construction methods [2]: a) the respiratory phase corresponding to end expiration (EE) and end inspiration (EI) need to be manually identified in the dMRI sequence; b) abnormal breathing signals due to nontidal breathing cannot be detected automatically which affects the construction process. Since the typical 2D dynamic MRI acquisition contains ~3000 slices per patient, handling these tasks manually is very labor intensive. In this study, we propose a deep-learning-based framework for addressing both problems via convolutional neural networks (CNNs) [3] and Long Short-Term Memory (LSTM) [4] models. A CNN is used to extract the motion characteristics from the respiratory dMRI sequences to automatically identify contiguous sequences of slices representing exhalation and inhalation processes. EE and EI annotations are subsequently completed by comparing the changes in the direction of motion of the diaphragm. A LSTM network is used for detecting abnormal respiratory signals by exploiting the nonuniform motion feature sequence of abnormal breathing motions. Experimental results show the mean error of labeling EE and EI is ~0.3 dMRI time point unit (much less than one time point). The accuracy of abnormal cycle detection reaches 80.0%. The proposed approach achieves results highly comparable to manual labeling in accuracy but with close to full automation of the whole process. The framework proposed here can be readily adapted to other modalities and dynamic imaging applications.
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