Distinct emphysema subtypes defined by quantitative CT analysis are associated with specific pulmonary matrix metalloproteinases.

BACKGROUND: Emphysema is characterised by distinct pathological sub-types, but little is known about the divergent underlying aetiology. Matrix-metalloproteinases (MMPs) are proteolytic enzymes that can degrade the extracellular matrix and have been identified as potentially important in the development of emphysema. However, the relationship between MMPs and emphysema sub-type is unknown. We investigated the role of MMPs and their inhibitors in the development of emphysema sub-types by quantifying levels and determining relationships with these sub-types in mild-moderate COPD patients and ex/current smokers with preserved lung function. METHODS: Twenty-four mild-moderate COPD and 8 ex/current smokers with preserved lung function underwent high resolution CT and distinct emphysema sub-types were quantified using novel local histogram-based assessment of lung density. We analysed levels of MMPs and tissue inhibitors of MMPs (TIMPs) in bronchoalveolar lavage (BAL) and assessed their relationship with these emphysema sub-types. RESULTS: The most prevalent emphysema subtypes in COPD subjects were mild and moderate centrilobular (CLE) emphysema, while only small amounts of severe centrilobular emphysema, paraseptal emphysema (PSE) and panlobular emphysema (PLE) were present. MMP-3, and -10 associated with all emphysema sub-types other than mild CLE, while MMP-7 and -8 had associations with moderate and severe CLE and PSE. MMP-9 also had associations with moderate CLE and paraseptal emphysema. Mild CLE occurred in substantial quantities irrespective of whether airflow obstruction was present and did not show any associations with MMPs. CONCLUSION: Multiple MMPs are directly associated with emphysema sub-types identified by CT imaging, apart from mild CLE. This suggests that MMPs play a significant role in the tissue destruction seen in the more severe sub-types of emphysema, whereas early emphysematous change may be driven by a different mechanism. TRIAL REGISTRATION: Trial registration number NCT01701869

Automatic synthesis of anthropomorphic pulmonary CT phantoms

The great density and structural complexity of pulmonary vessels and airways impose limitations on the generation of accurate reference standards, which are critical in training and in the validation of image processing methods for features such as pulmonary vessel segmentation or artery–vein (AV) separations. The design of synthetic computed tomography (CT) images of the lung could overcome these difficulties by providing a database of pseudorealistic cases in a constrained and controlled scenario where each part of the image is differentiated unequivocally. This work demonstrates a complete framework to generate computational anthropomorphic CT phantoms of the human lung automatically. Starting from biological and image-based knowledge about the topology and relationships between structures, the system is able to generate synthetic pulmonary arteries, veins, and airways using iterative growth methods that can be merged into a final simulated lung with realistic features. A dataset of 24 labeled anthropomorphic pulmonary CT phantoms were synthesized with the proposed system. Visual examination and quantitative measurements of intensity distributions, dispersion of structures and relationships between pulmonary air and blood flow systems show good correspondence between real and synthetic lungs (p > 0.05 with low Cohen’s d effect size and AUC values), supporting the potentiality of the tool and the usefulness of the generated phantoms in the biomedical image processing field

Automated Axial Right Ventricle to Left Ventricle Diameter Ratio Computation in Computed Tomography Pulmonary Angiography

Background and Purpose Right Ventricular to Left Ventricular (RV/LV) diameter ratio has been shown to be a prognostic biomarker for patients suffering from acute Pulmonary Embolism (PE). While Computed Tomography Pulmonary Angiography (CTPA) images used to confirm a clinical suspicion of PE do include information of the heart, a numerical RV/LV diameter ratio is not universally reported, likely because of lack in training, inter-reader variability in the measurements, and additional effort by the radiologist. This study designs and validates a completely automated Computer Aided Detection (CAD) system to compute the axial RV/LV diameter ratio from CTPA images so that the RV/LV diameter ratio can be a more objective metric that is consistently reported in patients for whom CTPA diagnoses PE. Materials and Methods The CAD system was designed specifically for RV/LV measurements. The system was tested in 198 consecutive CTPA patients with acute PE. Its accuracy was evaluated using reference standard RV/LV radiologist measurements and its prognostic value was established for 30-day PE-specific mortality and a composite outcome of 30-day PE-specific mortality or the need for intensive therapies. The study was Institutional Review Board (IRB) approved and HIPAA compliant. Results The CAD system analyzed correctly 92.4% (183/198) of CTPA studies. The mean difference between automated and manually computed axial RV/LV ratios was 0.03±0.22. The correlation between the RV/LV diameter ratio obtained by the CAD system and that obtained by the radiologist was high (r=0.81). Compared to the radiologist, the CAD system equally achieved high accuracy for the composite outcome, with areas under the receiver operating characteristic curves of 0.75 vs. 0.78. Similar results were found for 30-days PE-specific mortality, with areas under the curve of 0.72 vs. 0.75. Conclusions An automated CAD system for determining the CT derived RV/LV diameter ratio in patients with acute PE has high accuracy when compared to manual measurements and similar prognostic significance for two clinical outcomes.Madrid-MIT M+Vision Consortiu

Distinct emphysema subtypes defined by quantitative CT analysis are associated with specific pulmonary matrix metalloproteinases

BACKGROUND: Emphysema is characterised by distinct pathological sub-types, but little is known about the divergent underlying aetiology. Matrix-metalloproteinases (MMPs) are proteolytic enzymes that can degrade the extracellular matrix and have been identified as potentially important in the development of emphysema. However, the relationship between MMPs and emphysema sub-type is unknown. We investigated the role of MMPs and their inhibitors in the development of emphysema sub-types by quantifying levels and determining relationships with these sub-types in mild-moderate COPD patients and ex/current smokers with preserved lung function.METHODS: Twenty-four mild-moderate COPD and 8 ex/current smokers with preserved lung function underwent high resolution CT and distinct emphysema sub-types were quantified using novel local histogram-based assessment of lung density. We analysed levels of MMPs and tissue inhibitors of MMPs (TIMPs) in bronchoalveolar lavage (BAL) and assessed their relationship with these emphysema sub-types.RESULTS: The most prevalent emphysema subtypes in COPD subjects were mild and moderate centrilobular (CLE) emphysema, while only small amounts of severe centrilobular emphysema, paraseptal emphysema (PSE) and panlobular emphysema (PLE) were present. MMP-3, and -10 associated with all emphysema sub-types other than mild CLE, while MMP-7 and -8 had associations with moderate and severe CLE and PSE. MMP-9 also had associations with moderate CLE and paraseptal emphysema. Mild CLE occurred in substantial quantities irrespective of whether airflow obstruction was present and did not show any associations with MMPs.CONCLUSION: Multiple MMPs are directly associated with emphysema sub-types identified by CT imaging, apart from mild CLE. This suggests that MMPs play a significant role in the tissue destruction seen in the more severe sub-types of emphysema, whereas early emphysematous change may be driven by a different mechanism.TRIAL REGISTRATION: Trial registration number NCT01701869

Optimizing parameters of an open-source airway segmentation algorithm using different CT images.

Background: Computed tomography (CT) helps physicians locate and diagnose pathological conditions. In some conditions, having an airway segmentation method which facilitates reconstruction of the airway from chest CT images can help hugely in the assessment of lung diseases. Many efforts have been made to develop airway segmentation algorithms, but methods are usually not optimized to be reliable across different CT scan parameters. Methods: In this paper, we present a simple and reliable semi-automatic algorithm which can segment tracheal and bronchial anatomy using the open-source 3D Slicer platform. The method is based on a region growing approach where trachea, right and left bronchi are cropped and segmented independently using three different thresholds. The algorithm and its parameters have been optimized to be efficient across different CT scan acquisition parameters. The performance of the proposed method has been evaluated on EXACT’09 cases and local clinical cases as well as on a breathing pig lung phantom using multiple scans and changing parameters. In particular, to investigate multiple scan parameters reconstruction kernel, radiation dose and slice thickness have been considered. Volume, branch count, branch length and leakage presence have been evaluated. A new method for leakage evaluation has been developed and correlation between segmentation metrics and CT acquisition parameters has been considered. Results: All the considered cases have been segmented successfully with good results in terms of leakage presence. Results on clinical data are comparable to other teams’ methods, as obtained by evaluation against the EXACT09 challenge, whereas results obtained from the phantom prove the reliability of the method across multiple CT platforms and acquisition parameters. As expected, slice thickness is the parameter affecting the results the most, whereas reconstruction kernel and radiation dose seem not to particularly affect airway segmentation. Conclusion: The system represents the first open-source airway segmentation platform. The quantitative evaluation approach presented represents the first repeatable system evaluation tool for like-for-like comparison between different airway segmentation platforms. Results suggest that the algorithm can be considered stable across multiple CT platforms and acquisition parameters and can be considered as a starting point for the development of a complete airway segmentation algorithm

Differences in Respiratory Symptoms and Lung Structure Between Hispanic and Non-Hispanic White Smokers: A Comparative Study

Background: Prior studies have demonstrated that U.S. Hispanic smokers have a lower risk of decline in lung function and chronic obstructive pulmonary disease (COPD) compared with non-Hispanic whites (NHW). This suggests there might be racial-ethnic differences in susceptibility in cigarette smoke-induced respiratory symptoms, lung parenchymal destruction, and airway and vascular disease, as well as in extra-pulmonary manifestations of COPD. Therefore, we aimed to explore respiratory symptoms, lung function, and pulmonary and extra-pulmonary structural changes in Hispanic and NHW smokers. Methods: We compared respiratory symptoms, lung function, and computed tomography (CT) measures of emphysema-like tissue, airway disease, the branching generation number (BGN) to reach a 2-mm-lumen-diameter airway, and vascular pruning as well as muscle and fat mass between 39 Hispanic and 39 sex-, age- and smoking exposure-matched NHW smokers. Results: Hispanic smokers had higher odds of dyspnea than NHW after adjustment for COPD and asthma statuses (odds ratio[OR] = 2.96; 95% confidence interval [CI] 1.09-8.04), but no significant differences were found in lung function and CT measurements. Conclusions: While lung function and CT measures of the lung structure were similar, dyspnea is reported more frequently by Hispanic than matched-NHW smokers. It seems to be an impossible puzzle but it's easy to solve a Rubik' Cube using a few algorithms

Utility of Peripheral Protein Biomarkers for the Prediction of Incident Interstitial Features: A Multicentre Retrospective Cohort Study

INTRODUCTION/RATIONALE: Protein biomarkers may help enable the prediction of incident interstitial features on chest CT. METHODS: We identified which protein biomarkers in a cohort of smokers (COPDGene) differed between those with and without objectively measured interstitial features at baseline using a univariate screen (t-test false discovery rate, FDR p RESULTS: In COPDGene, 1305 biomarkers were available and 20 differed between those with and without interstitial features at baseline. Of these, 11 were associated with feature progression over a mean of 5.5 years of follow-up, and of these 4 were available in PLuSS, (angiopoietin-2, matrix metalloproteinase 7, macrophage inflammatory protein 1 alpha) over a mean of 8.8 years of follow-up. The area under the curve (AUC) of classifiers using demographics and imaging features in COPDGene and PLuSS were 0.69 and 0.59, respectively. In COPDGene, the AUC of the univariate screen classifier was 0.78 and of the multivariable confirmation classifier was 0.76. The AUC of the final classifier in COPDGene was 0.75 and in PLuSS was 0.76. The outcome for all of the models was the development of incident interstitial features. CONCLUSIONS: Multiple novel and previously identified proteomic biomarkers are associated with interstitial features on chest CT and may enable the prediction of incident interstitial diseases such as idiopathic pulmonary fibrosis

CARL: A Framework for Equivariant Image Registration

Image registration estimates spatial correspondences between a pair of images. These estimates are typically obtained via numerical optimization or regression by a deep network. A desirable property of such estimators is that a correspondence estimate (e.g., the true oracle correspondence) for an image pair is maintained under deformations of the input images. Formally, the estimator should be equivariant to a desired class of image transformations. In this work, we present careful analyses of the desired equivariance properties in the context of multi-step deep registration networks. Based on these analyses we 1) introduce the notions of $[U,U]$ equivariance (network equivariance to the same deformations of the input images) and $[W,U]$ equivariance (where input images can undergo different deformations); we 2) show that in a suitable multi-step registration setup it is sufficient for overall $[W,U]$ equivariance if the first step has $[W,U]$ equivariance and all others have $[U,U]$ equivariance; we 3) show that common displacement-predicting networks only exhibit $[U,U]$ equivariance to translations instead of the more powerful $[W,U]$ equivariance; and we 4) show how to achieve multi-step $[W,U]$ equivariance via a coordinate-attention mechanism combined with displacement-predicting refinement layers (CARL). Overall, our approach obtains excellent practical registration performance on several 3D medical image registration tasks and outperforms existing unsupervised approaches for the challenging problem of abdomen registration

uniGradICON: A Foundation Model for Medical Image Registration

Conventional medical image registration approaches directly optimize over the parameters of a transformation model. These approaches have been highly successful and are used generically for registrations of different anatomical regions. Recent deep registration networks are incredibly fast and accurate but are only trained for specific tasks. Hence, they are no longer generic registration approaches. We therefore propose uniGradICON, a first step toward a foundation model for registration providing 1) great performance \emph{across} multiple datasets which is not feasible for current learning-based registration methods, 2) zero-shot capabilities for new registration tasks suitable for different acquisitions, anatomical regions, and modalities compared to the training dataset, and 3) a strong initialization for finetuning on out-of-distribution registration tasks. UniGradICON unifies the speed and accuracy benefits of learning-based registration algorithms with the generic applicability of conventional non-deep-learning approaches. We extensively trained and evaluated uniGradICON on twelve different public datasets. Our code and the uniGradICON model are available at https://github.com/uncbiag/uniGradICON

Inverse Consistency by Construction for Multistep Deep Registration

Inverse consistency is a desirable property for image registration. We propose a simple technique to make a neural registration network inverse consistent by construction, as a consequence of its structure, as long as it parameterizes its output transform by a Lie group. We extend this technique to multi-step neural registration by composing many such networks in a way that preserves inverse consistency. This multi-step approach also allows for inverse-consistent coarse to fine registration. We evaluate our technique on synthetic 2-D data and four 3-D medical image registration tasks and obtain excellent registration accuracy while assuring inverse consistency