Improved Visualization and Quantification for Hyperpolarized 129Xe MRI

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2019

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Abstract

In Pulmonary diseases, such as chronic obstructed pulmonary diseases (COPD), fibrosis, and asthma, are responsible for substantial health and financial burden in the world. In 2016, COPD claimed more than 3 million lives, which is also the 3rd leading cause of mortality. The treatment for pulmonary diseases continues to be hampered by the lack of reliable metrics to diagnose, as well as assess disease progression and therapeutic response. The current tools to diagnose and monitor pulmonary diseases are the pulmonary function tests (PFT) consisting of spirometry and plethysmography, and diffusing capacity of the lungs for carbon monoxide (DLCO). However, these metrics are effort-dependent, tend to have poor reproducibility, and measure lung as a whole, which allow subtle or regional diseases to be ‘hidden’. Alternatively, computed tomography (CT) is capable of characterizing lung structures in exquisite details, which is commonly applied in detecting the presence of both emphysema and pulmonary fibrosis. However, these structure details do not necessarily correlate well to how patients feel, the lung function, and the treatment effect. Thus, this information is much better assessed by characterizing the functions of the lung. Nuclear medicine, employing 133Xe ventilation and 99Tcm-macroaggregated albumin perfusion scan (ventilation/perfusion V/Q scan) can assess the inequality of airflow and blood flow in the lung. However, this V/Q scan evolves the usage of radioactive tracers and is limited by both poor temporal and spatial resolution. Thus, there has been considerable interest in developing methods that can comprehensively evaluate lung function non-invasively and can provide 3D resolution. Therefore, there has been considerable interest in developing methods that can evaluate lung function comprehensively, non-invasively, and 3-dimensionality.

In recent years, the introduction of hyperpolarized (HP) 129Xe magnetic resonance imaging (MRI) into clinical research has provided a robust and non-invasive 3D imaging technique, capable of both high-resolution imaging of pulmonary ventilation and gas exchange. Notably, gas exchange imaging is enabled by the solubility and unique frequency shifts of xenon in interstitial barrier tissues and capillary red blood cells (RBC). These features offer the potential for 129Xe MRI to be used, not only to evaluate lung obstruction, but also interstitial and vascular diseases. With the capability for both ventilation and gas exchange imaging, robust and reproducible strategies are essential for both visualizing and qualifying the resulting images. Before that, a standardized acquisition with a well-understood relationship between 129Xe dose and image quality needs to be established for efficient and cost-effective acquisitions. Moreover, we also seek to understand the origins of ventilation defects as well as alterations in barrier uptake and RBC transfer. Until such fundamental issues are addressed, it will not be possible to disseminate 129Xe MRI for multi-center clinical trials.

The objective of this work is to establish a robust and comprehensive 129Xe ventilation MRI clinical workflow to investigate pulmonary disorders, and to lay the foundation for clinical deployment and multi-center dissemination. To this end, this work describes several milestones toward establishing a routine, high signal-to-noise ratio (SNR) 129Xe ventilation MRI acquisition with the minimum sufficient volume 129Xe gas, and associated robust quantification pipeline for our clinical platforms. Moreover, we compared our quantification pipeline to other approaches in the field, as well as on different types of acquisition strategies (multi-slice GRE vs. 3D-radial).

To date, various quantification methods have been established for 129Xe ventilation MRI, yet no agreement has been reached on how to calculate the ventilation defect percentage (VDP). Thus, this work begins by developing a quantification workflow with semi-automatic delineation of the 1H thoracic cavity images, automatic pulmonary vasculature extraction, and inhomogeneity correction of the 129Xe ventilation images. It employs a robust linear binning classification that characterizes the entire ventilation distribution while being grounded in a healthy reference population. This quantification method can help evaluate, with high repeatability, how aging, diseases, and treatment influence ventilation distribution.

To further evaluate the robustness of this linear binning quantification method, its performance was assessed against another commonly used clustering method – K-means, on quantifying ventilation images. As part of the investigation, the methods were tested on images for which SNR had been artificially degraded. Through this evaluation, the minimum image SNR was established for an adequate quantification. We have also made the SNR-degraded image sets publicly available at Harvard Dataverse. These shared image sets could be used to evaluate the robustness of various quantification methods in the field. This endeavor is intended to help the pulmonary functional MRI community to standardize the analysis methods and laid the groundwork for future multi-center comparison studies.

We further address the fact that 129Xe ventilation MRI can be and has been conducted using a variety of pulse sequences, scan duration, and 129Xe doses. With more acceptance of the general utility of 129Xe MRI, imaging protocols must be standardized to enable multi-center trials. We thus sought to establish a rational basis for understanding the dose requirements and evaluating how different pulse sequences and 129Xe doses can influence 129Xe ventilation quantification. From that, the minimum required 129Xe dose for an adequate 129Xe ventilation quantification can be derived.

Maybe the emergence and development of 129Xe gas transfer MRI has introduced not only the ability to regionally assess gas exchange, but has introduced the interesting problem that it also delivers ventilation data from the same breath. However, the gas phase is acquired differently, with low resolution and isotropically. This raises the question as to how to generalize the ventilation quantification approach previously introduced specifically for multi-slice GRE. Therefore, we sought to generalize the linear binning approach for rescaling the intensity histogram, which enables the application of linear binning analysis to any ventilation MRI acquisition. We also investigated whether, and to what extent, 3D-radial acquisition can provide similar diagnostic information as from a dedicated multi-slice GRE acquisition. Through these efforts, we evaluated the possibility to employ a more efficient scan protocol for future routine clinical application.

During the course of this work, several practical engineering challenges were raised. First, hyperpolarized MRI has so far mostly been demonstrated at 1.5 Tesla (T), while most MRI vendors are transitioning multi-nuclear platforms to 3 T. This transition from 1.5 T to 3 T requires a reconsideration of optimal imaging acquisition and further optimization of quantification method. Moreover, preparation for multi-center dissemination points to the need for future centralized processing. This leads to the interest in cloud-based processing. However, in order to make this possible, manual segmentation of the thoracic cavity must be replaced by automatic methods. This, in turn requires the use of a novel neural network-based approaches. To this end, we first optimized the sequence on the transition to our new 3 T system. After completing the transition, the linear binning quantification method was further optimized with an enhanced vasculature segmentation and a neural network based 1H thoracic cavity segmentation. We also exploited the emergence of RBC transfer and implemented a framework to interpret these images by comparing them to more well-established approaches such as Gd-enhanced dynamic contrast-enhanced (DCE) perfusion MRI. To this end, we also developed a quantitative perfusion imaging pipeline that could be used to interpret the causes of RBC defects in our gas exchange imaging.

Taken together, results presented in this dissertation provide the step by step development of our rapid clinical exam workflow for hyperpolarized 129Xe MRI. This clinical workflow, not only demonstrates a comprehensive image quantification pipeline with applications to the 129Xe ventilation images and Gd-enhanced DCE MRI, but also the considerations for the acquisition sequence and delivered 129Xe dose. Overall, the established quantification pipeline offers a robust and sensitive way for diseases phenotyping, disease monitoring, and treatment planning. Moreover, this thesis work has hopefully laid the groundwork for standardized quantification, that could be deployed for future multi-center clinical trials.

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He, Mu (2019). Improved Visualization and Quantification for Hyperpolarized 129Xe MRI. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/18802.

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