Texture Analysis and Machine Learning to Predict Pulmonary Ventilation from Thoracic Computed Tomography

Chronic obstructive pulmonary disease (COPD) leads to persistent airflow limitation, causing a large burden to patients and the health care system. Thoracic CT provides an opportunity to observe the structural pathophysiology of COPD, whereas hyperpolarized gas MRI provides images of the consequential ventilation heterogeneity. However, hyperpolarized gas MRI is currently limited to research centres, due to the high cost of gas and polarization equipment. Therefore, I developed a pipeline using texture analysis and machine learning methods to create predicted ventilation maps based on non-contrast enhanced, single-volume thoracic CT. In a COPD cohort, predicted ventilation maps were qualitatively and quantitatively related to ground-truth MRI ventilation, and both maps were related to important patient lung function and quality-of-life measures. This study is the first to demonstrate the feasibility of predicting hyperpolarized MRI-based ventilation from single-volume, breath-hold thoracic CT, which has potential to translate pulmonary ventilation information to widely available thoracic CT imaging

Incorporation of prior knowledge of the signal behavior into the reconstruction to accelerate the acquisition of MR diffusion data

International audienceDiffusion MRI measurements using hyperpolarized gases are generally acquired during patient breath hold, which yields a compromise between achievable image resolution, lung coverage and number of b-values. In this work, we propose a novel method that accelerates the acquisition of MR diffusion data by undersampling in both spatial and b-value dimensions, thanks to incorporating knowledge about the signal decay into the reconstruction (SIDER). SIDER is compared to total variation (TV) reconstruction by assessing their effect on both the recovery of ventilation images and estimated mean alveolar dimensions (MAD). Both methods are assessed by retrospectively undersampling diffusion datasets of normal volunteers and COPD patients (n=8) for acceleration factors between x2 and x10. TV led to large errors and artefacts for acceleration factors equal or larger than x5. SIDER improved TV, presenting lower errors and histograms of MAD closer to those obtained from fully sampled data for accelerations factors up to x10. SIDER preserved image quality at all acceleration factors but images were slightly smoothed and some details were lost at x10. In conclusion, we have developed and validated a novel compressed sensing method for lung MRI imaging and achieved high acceleration factors, which can be used to increase the amount of data acquired during a breath-hold. This methodology is expected to improve the accuracy of estimated lung microstructure dimensions and widen the possibilities of studying lung diseases with MRI

In Vivo Magnetic Resonance Imaging Morphometry Measurements of Pulmonary Airspace Enlargement

Diffusion-weighted magnetic resonance imaging (MRI) provides unparalleled information and measurements of lung structure and function without the burden of ionizing radiation. In particular, diffusion-weighted MRI provides estimates of airspace enlargement, which is a hallmark characteristic of emphysema. MRI provides a way to measure in vivo mean-linear-intercept (Lm) and this is a promising measurement for clinical evaluation of disease progression in patients with Alpha-1 Antitrypsin Deficiency (AATD) in which airspace enlargement begins early in life. As such, our objective was to evaluate MRI measurements of airspace enlargement in AATD patients and compare these measurements to ex-smokers with chronic obstructive pulmonary disease (COPD) and healthy never-smokers. We compared these measurements with standard clinical measurements provided by spirometry, plethysmography and computed tomography; we also demonstrated that MRI detected differences in disease severity in patients with clinically similar measurements