Regional Lung Ventilation Analysis Using Temporally Resolved Magnetic Resonance Imaging

Advances in CT-based lung function imaging for thoracic radiotherapy

The objective of this review is to examine the potential benefits and challenges of CT-based lung function imaging in radiotherapy over recent decades. This includes reviewing background information, defining related concepts, classifying and reviewing existing studies, and proposing directions for further investigation. The lung function imaging techniques reviewed herein encompass CT-based methods, specifically utilizing phase-resolved four-dimensional CT (4D-CT) or end-inspiratory and end-expiratory CT scans, to delineate distinct functional regions within the lungs. These methods extract crucial functional parameters, including lung volume and ventilation distribution, pivotal for assessing and characterizing the functional capacity of the lungs. CT-based lung ventilation imaging offers numerous advantages, notably in the realm of thoracic radiotherapy. By utilizing routine CT scans, additional radiation exposure and financial burdens on patients can be avoided. This imaging technique also enables the identification of different functional areas of the lung, which is crucial for minimizing radiation exposure to healthy lung tissue and predicting and detecting lung injury during treatment. In conclusion, CT-based lung function imaging holds significant promise for improving the effectiveness and safety of thoracic radiotherapy. Nevertheless, challenges persist, necessitating further research to address limitations and optimize clinical utilization. Overall, this review highlights the importance of CT-based lung function imaging as a valuable tool in radiotherapy planning and lung injury monitoring.

Overview of MRI for pulmonary functional imaging

Morphological evaluation of the lung is important in the clinical evaluation of pulmonary diseases. However, the disease process, especially in its early phases, may primarily result in changes in pulmonary function without changing the pulmonary structure. In such cases, the traditional imaging approaches to pulmonary morphology may not provide sufficient insight into the underlying pathophysiology. Pulmonary imaging community has therefore tried to assess pulmonary diseases and functions utilizing not only nuclear medicine, but also CT and MR imaging with various technical approaches. In this review, we overview state-of-the art MR methods and the future direction of: (1) ventilation imaging, (2) perfusion imaging and (3) biomechanical evaluation for pulmonary functional imaging.

Delayed contrast dynamics as marker of regional impairment in pulmonary fibrosis using 5D MRI - a pilot study

OBJECTIVE

To analyse delayed contrast dynamics of fibrotic lesions in interstitial lung disease (ILD) using five dimensional (5D) MRI and to correlate contrast dynamics with disease severity.

METHODS

20 patients (mean age: 71 years; M:F, 13:7), with chronic fibrosing ILD: n = 12 idiopathic pulmonary fibrosis (IPF) and n = 8 non-IPF, underwent thin-section multislice CT as part of the standard diagnostic workup and additionally MRI of the lung. 2 min after contrast injection, a radial gradient echo sequence with golden-angle spacing was acquired during 5 min of free-breathing, followed by 5D image reconstruction. Disease was categorized as severe or non-severe according to CT morphological regional severity. For each patient, 10 lesions were analysed.

RESULTS

IPF lesions showed later peak enhancement compared to non-IPF (severe: p = 0.01, non-severe: p = 0.003). Severe lesions showed later peak enhancement compared to non-severe lesions, in non-IPF (p = 0.04), but not in IPF (p = 0.35). There was a tendency towards higher accumulation and washout rates in IPF compared to non-IPF in non-severe disease. Severe lesions had lower washout rate than non-severe ones in both IPF (p = 0.003) and non-IPF (p = 0.005). Continuous contrast agent accumulation, without washout, was found only in IPF lesions.

CONCLUSIONS

Contrast agent dynamics are influenced by type and severity of pulmonary fibrosis, which might enable a more thorough characterisation of disease burden. The regional impairment is of particular interest in the context of antifibrotic treatments and was characterised using a non-invasive, non-irradiating, free-breathing method.

ADVANCES IN KNOWLEDGE

Delayed contrast enhancement patterns allow the assessment of regional lung impairment which could represent different disease stages or phenotypes in ILD.

3D Magnetic Resonance Spirometry

Spirometry is today the gold standard technique for assessing pulmonary ventilatory function in humans. From the shape of a flow-volume loop measured while the patient is performing forced respiratory cycles, the Forced Vital Capacity (FVC) and the Forced Expiratory Volume in one second (FEV1) can be inferred, and the pulmonologist is able to detect and characterize common respiratory afflictions. This technique is non-invasive, simple, widely available, robust, repeatable and reproducible. Yet, its outcomes rely on the patient's cooperation and provide only global information over the lung. With 3D Magnetic Resonance (MR) Spirometry, local ventilation can be assessed by MRI anywhere in the lung while the patient is freely breathing. The larger dimensionality of 3D MR Spirometry advantageously allows the extraction of original metrics that characterize the anisotropic and hysteretic regional mechanical behavior of the lung. Here, we demonstrated the potential of this technique on a healthy human volunteer breathing along different respiratory patterns during the MR acquisition. These new results are discussed with lung physiology and recent pulmonary CT data. As respiratory mechanics inherently support lung ventilation, 3D MR Spirometry may open a new way to non-invasively explore lung function while providing improved diagnosis of localized pulmonary diseases.

Predicted airway obstruction distribution based on dynamical lung ventilation data: A coupled modeling-machine learning methodology

In asthma and chronic obstructive pulmonary disease, some airways of the tracheobronchial tree can be constricted, from moderate narrowing up to closure. Those pathological patterns of obstructions affect the lung ventilation distribution. While some imaging techniques enable visualization and quantification of constrictions in proximal generations, no noninvasive technique exists to provide the airway morphology and obstruction distribution in distal areas. In this work, we propose a method that exploits lung ventilation measures to access positions of airway obstructions (restrictions and closures) in the tree. This identification approach combines a lung ventilation model, in which a 0D tree is strongly coupled to a 3D parenchyma description, along with a machine learning approach. On the basis of synthetic data generated with typical temporal and spatial resolutions as well as reconstruction errors, we obtain very encouraging results of the obstruction distribution, with a detection rate higher than 85%.

Holistic segmentation of the lung in cine MRI

Duchenne muscular dystrophy (DMD) is a childhood-onset neuromuscular disease that results in the degeneration of muscle, starting in the extremities, before progressing to more vital areas, such as the lungs. Respiratory failure and pneumonia due to respiratory muscle weakness lead to hospitalization and early mortality. However, tracking the disease in this region can be difficult, as current methods are based on breathing tests and are incapable of distinguishing between muscle involvements. Cine MRI scans give insight into respiratory muscle movements, but the images suffer due to low spatial resolution and poor signal-to-noise ratio. Thus, a robust lung segmentation method is required for accurate analysis of the lung and respiratory muscle movement. We deployed a deep learning approach that utilizes sequence-specific prior information to assist the segmentation of lung in cine MRI. More specifically, we adopt a holistically nested network to conduct image-to-image holistic training and prediction. One frame of the cine MRI is used in the training and applied to the remainder of the sequence ([Formula: see text] frames). We applied this method to cine MRIs of the lung in the axial, sagittal, and coronal planes. Characteristic lung motion patterns during the breathing cycle were then derived from the segmentations and used for diagnosis. Our data set consisted of 31 young boys, age [Formula: see text] years, 15 of whom suffered from DMD. The remaining 16 subjects were age-matched healthy volunteers. For validation, slices from inspiratory and expiratory cycles were manually segmented and compared with results obtained from our method. The Dice similarity coefficient for the deep learning-based method was [Formula: see text] for the sagittal view, [Formula: see text] for the axial view, and [Formula: see text] for the coronal view. The holistic neural network approach was compared with an approach using Demon's registration and showed superior performance. These results suggest that the deep learning-based method reliably and accurately segments the lung across the breathing cycle.