MRI of the lung (3/3)-current applications and future perspectives

BACKGROUND: MRI of the lung is recommended in a number of clinical indications. Having a non-radiation alternative is particularly attractive in children and young subjects, or pregnant women. METHODS: Provided there is sufficient expertise, magnetic resonance imaging (MRI) may be considered as the preferential modality in specific clinical conditions such as cystic fibrosis and acute pulmonary embolism, since additional functional information on respiratory mechanics and regional lung perfusion is provided. In other cases, such as tumours and pneumonia in children, lung MRI may be considered an alternative or adjunct to other modalities with at least similar diagnostic value. RESULTS: In interstitial lung disease, the clinical utility of MRI remains to be proven, but it could provide additional information that will be beneficial in research, or at some stage in clinical practice. Customised protocols for chest imaging combine fast breath-hold acquisitions from a "buffet" of sequences. Having introduced details of imaging protocols in previous articles, the aim of this manuscript is to discuss the advantages and limitations of lung MRI in current clinical practice. CONCLUSION: New developments and future perspectives such as motion-compensated imaging with self-navigated sequences or fast Fourier decomposition MRI for non-contrast enhanced ventilation- and perfusion-weighted imaging of the lung are discussed. Main Messages • MRI evolves as a third lung imaging modality, combining morphological and functional information. • It may be considered first choice in cystic fibrosis and pulmonary embolism of young and pregnant patients. • In other cases (tumours, pneumonia in children), it is an alternative or adjunct to X-ray and CT. • In interstitial lung disease, it serves for research, but the clinical value remains to be proven. • New users are advised to make themselves familiar with the particular advantages and limitations

Evaluating and Improving 4D-CT Image Segmentation for Lung Cancer Radiotherapy

Lung cancer is a high-incidence disease with low survival despite surgical advances and concurrent chemo-radiotherapy strategies. Image-guided radiotherapy provides for treatment measures, however, significant challenges exist for imaging, treatment planning, and delivery of radiation due to the influence of respiratory motion. 4D-CT imaging is capable of improving image quality of thoracic target volumes influenced by respiratory motion. 4D-CT-based treatment planning strategies requires highly accurate anatomical segmentation of tumour volumes for radiotherapy treatment plan optimization. Variable segmentation of tumour volumes significantly contributes to uncertainty in radiotherapy planning due to a lack of knowledge regarding the exact shape of the lesion and difficulty in quantifying variability. As image-segmentation is one of the earliest tasks in the radiotherapy process, inherent geometric uncertainties affect subsequent stages, potentially jeopardizing patient outcomes. Thus, this work assesses and suggests strategies for mitigation of segmentation-related geometric uncertainties in 4D-CT-based lung cancer radiotherapy at pre- and post-treatment planning stages

Improving the Accuracy of CT-derived Attenuation Correction in Respiratory-Gated PET/CT Imaging

The effect of respiratory motion on attenuation correction in Fludeoxyglucose (18F) positron emission tomography (FDG-PET) was investigated. Improvements to the accuracy of computed tomography (CT) derived attenuation correction were obtained through the alignment of the attenuation map to each emission image in a respiratory gated PET scan. Attenuation misalignment leads to artefacts in the reconstructed PET image and several methods were devised for evaluating the attenuation inaccuracies caused by this. These methods of evaluation were extended to finding the frame in the respiratory gated PET which best matched the CT. This frame was then used as a reference frame in mono-modality compensation for misalignment. Attenuation correction was found to affect the quantification of tumour volumes; thus a regional analysis was used to evaluate the impact of mismatch and the benefits of compensating for misalignment. Deformable image registration was used to compensate for misalignment, however, there were inaccuracies caused by the poor signal-to-noise ratio (SNR) in PET images. Two models were developed that were robust to a poor SNR allowing for the estimation of deformation from very noisy images. Firstly, a cross population model was developed by statistically analysing the respiratory motion in 10 4DCT scans. Secondly, a 1D model of respiration was developed based on the physiological function of respiration. The 1D approach correctly modelled the expansion and contraction of the lungs and the differences in the compressibility of lungs and surrounding tissues. Several additional models were considered but were ruled out based on their poor goodness of fit to 4DCT scans. Approaches to evaluating the developed models were also used to assist with optimising for the most accurate attenuation correction. It was found that the multimodality registration of the CT image to the PET image was the most accurate approach to compensating for attenuation correction mismatch. Mono-modality image registration was found to be the least accurate approach, however, incorporating a motion model improved the accuracy of image registration. The significance of these findings is twofold. Firstly, it was found that motion models are required to improve the accuracy in compensating for attenuation correction mismatch and secondly, a validation method was found for comparing approaches to compensating for attenuation mismatch

Characterisation and correction of respiratory-motion artefacts in cardiac PET-CT

Respiratory motion during cardiac Positron Emission Tomography (PET) Computed Tomography (CT) imaging results in blurring of the PET data and can induce mismatches between the PET and CT datasets, leading to attenuation-correction artefacts. The aim of this project was to develop a method of motion-correction to overcome both of these problems. The approach implemented was to transform a single CT to match the frames of a gated PET study, to facilitate respiratory-matched attenuation-correction, without the need for a gated CT. This is benecial for lowering the radiation dose to the patient and in reducing PETCT mismatches, which can arise even in gated studies. The heart and diaphragm were identied through phantom studies as the structures responsible for generating attenuation-correction artefacts in the heart and their motions therefore needed to be considered in transforming the CT. Estimating heart motion was straight-forward, due to its high contrast in PET, however the poor diaphragm contrast meant that additional information was required to track its position. Therefore a diaphragm shape model was constructed using segmented diaphragm surfaces, enabling complete diaphragm surfaces to be produced from incomplete and noisy initial estimates. These complete surfaces, in combination with the estimated heart motions were used to transform the CT. The PET frames were then attenuation-corrected with the transformed CT, reconstructed, aligned and summed, to produce motion-free images. It was found that motion-blurring was reduced through alignment, although benets were marginal in the presence of small respiratory motions. Quantitative accuracy was improved from use of the transformed CT for attenuation-correction (compared with no CT transformation), which was attributed to both the heart and the diaphragm transformations. In comparison to a gated CT, a substantial dose saving and a reduced dependence on gating techniques were achieved, indicating the potential value of the technique in routine clinical procedures

Improving the Accuracy of CT-derived Attenuation Correction in Respiratory-Gated PET/CT Imaging

The effect of respiratory motion on attenuation correction in Fludeoxyglucose (18F) positron emission tomography (FDG-PET) was investigated. Improvements to the accuracy of computed tomography (CT) derived attenuation correction were obtained through the alignment of the attenuation map to each emission image in a respiratory gated PET scan. Attenuation misalignment leads to artefacts in the reconstructed PET image and several methods were devised for evaluating the attenuation inaccuracies caused by this. These methods of evaluation were extended to finding the frame in the respiratory gated PET which best matched the CT. This frame was then used as a reference frame in mono-modality compensation for misalignment. Attenuation correction was found to affect the quantification of tumour volumes; thus a regional analysis was used to evaluate the impact of mismatch and the benefits of compensating for misalignment. Deformable image registration was used to compensate for misalignment, however, there were inaccuracies caused by the poor signal-to-noise ratio (SNR) in PET images. Two models were developed that were robust to a poor SNR allowing for the estimation of deformation from very noisy images. Firstly, a cross population model was developed by statistically analysing the respiratory motion in 10 4DCT scans. Secondly, a 1D model of respiration was developed based on the physiological function of respiration. The 1D approach correctly modelled the expansion and contraction of the lungs and the differences in the compressibility of lungs and surrounding tissues. Several additional models were considered but were ruled out based on their poor goodness of fit to 4DCT scans. Approaches to evaluating the developed models were also used to assist with optimising for the most accurate attenuation correction. It was found that the multimodality registration of the CT image to the PET image was the most accurate approach to compensating for attenuation correction mismatch. Mono-modality image registration was found to be the least accurate approach, however, incorporating a motion model improved the accuracy of image registration. The significance of these findings is twofold. Firstly, it was found that motion models are required to improve the accuracy in compensating for attenuation correction mismatch and secondly, a validation method was found for comparing approaches to compensating for attenuation mismatch

Pulmonary Image Segmentation and Registration Algorithms: Towards Regional Evaluation of Obstructive Lung Disease

Pulmonary imaging, including pulmonary magnetic resonance imaging (MRI) and computed tomography (CT), provides a way to sensitively and regionally measure spatially heterogeneous lung structural-functional abnormalities. These unique imaging biomarkers offer the potential for better understanding pulmonary disease mechanisms, monitoring disease progression and response to therapy, and developing novel treatments for improved patient care. To generate these regional lung structure-function measurements and enable broad clinical applications of quantitative pulmonary MRI and CT biomarkers, as a first step, accurate, reproducible and rapid lung segmentation and registration methods are required. In this regard, we first developed a 1H MRI lung segmentation algorithm that employs complementary hyperpolarized 3He MRI functional information for improved lung segmentation. The 1H-3He MRI joint segmentation algorithm was formulated as a coupled continuous min-cut model and solved through convex relaxation, for which a dual coupled continuous max-flow model was proposed and a max-flow-based efficient numerical solver was developed. Experimental results on a clinical dataset of 25 chronic obstructive pulmonary disease (COPD) patients ranging in disease severity demonstrated that the algorithm provided rapid lung segmentation with high accuracy, reproducibility and diminished user interaction. We then developed a general 1H MRI left-right lung segmentation approach by exploring the left-to-right lung volume proportion prior. The challenging volume proportion-constrained multi-region segmentation problem was approximated through convex relaxation and equivalently represented by a max-flow model with bounded flow conservation conditions. This gave rise to a multiplier-based high performance numerical implementation based on convex optimization theories. In 20 patients with mild- to-moderate and severe asthma, the approach demonstrated high agreement with manual segmentation, excellent reproducibility and computational efficiency. Finally, we developed a CT-3He MRI deformable registration approach that coupled the complementary CT-1H MRI registration. The joint registration problem was solved by exploring optical-flow techniques, primal-dual analyses and convex optimization theories. In a diverse group of patients with asthma and COPD, the registration approach demonstrated lower target registration error than single registration and provided fast regional lung structure-function measurements that were strongly correlated with a reference method. Collectively, these lung segmentation and registration algorithms demonstrated accuracy, reproducibility and workflow efficiency that all may be clinically-acceptable. All of this is consistent with the need for broad and large-scale clinical applications of pulmonary MRI and CT