Time to get serious about the detection and monitoring of early lung disease in cystic fibrosis.

Structural and functional defects within the lungs of children with cystic fibrosis (CF) are detectable soon after birth and progress throughout preschool years often without overt clinical signs or symptoms. By school age, most children have structural changes such as bronchiectasis or gas trapping/hypoperfusion and lung function abnormalities that persist into later life. Despite improved survival, gains in forced expiratory volume in one second (FEV1) achieved across successive birth cohorts during childhood have plateaued, and rates of FEV1 decline in adolescence and adulthood have not slowed. This suggests that interventions aimed at preventing lung disease should be targeted to mild disease and commence in early life. Spirometry-based classifications of 'normal' (FEV1≥90% predicted) and 'mild lung disease' (FEV1 70%-89% predicted) are inappropriate, given the failure of spirometry to detect significant structural or functional abnormalities shown by more sensitive imaging and lung function techniques. The state and readiness of two imaging (CT and MRI) and two functional (multiple breath washout and oscillometry) tools for the detection and monitoring of early lung disease in children and adults with CF are discussed in this article.Prospective research programmes and technological advances in these techniques mean that well-designed interventional trials in early lung disease, particularly in young children and infants, are possible. Age appropriate, randomised controlled trials are critical to determine the safety, efficacy and best use of new therapies in young children. Regulatory bodies continue to approve medications in young children based on safety data alone and extrapolation of efficacy results from older age groups. Harnessing the complementary information from structural and functional tools, with measures of inflammation and infection, will significantly advance our understanding of early CF lung disease pathophysiology and responses to therapy. Defining clinical utility for these novel techniques will require effective collaboration across multiple disciplines to address important remaining research questions. Future impact on existing management burden for patients with CF and their family must be considered, assessed and minimised.To address the possible role of these techniques in early lung disease, a meeting of international leaders and experts in the field was convened in August 2019 at the Australiasian Cystic Fibrosis Conference. The meeting entitiled 'Shaping imaging and functional testing for early disease detection of lung disease in Cystic Fibrosis', was attended by representatives across the range of disciplines involved in modern CF care. This document summarises the proceedings, key priorities and important research questions highlighted.

A novel mesh processing based technique for 3D plant analysis.

BACKGROUND: In recent years, imaging based, automated, non-invasive, and non-destructive high-throughput plant phenotyping platforms have become popular tools for plant biology, underpinning the field of plant phenomics. Such platforms acquire and record large amounts of raw data that must be accurately and robustly calibrated, reconstructed, and analysed, requiring the development of sophisticated image understanding and quantification algorithms. The raw data can be processed in different ways, and the past few years have seen the emergence of two main approaches: 2D image processing and 3D mesh processing algorithms. Direct image quantification methods (usually 2D) dominate the current literature due to comparative simplicity. However, 3D mesh analysis provides the tremendous potential to accurately estimate specific morphological features cross-sectionally and monitor them over-time. RESULT: In this paper, we present a novel 3D mesh based technique developed for temporal high-throughput plant phenomics and perform initial tests for the analysis of Gossypium hirsutum vegetative growth. Based on plant meshes previously reconstructed from multi-view images, the methodology involves several stages, including morphological mesh segmentation, phenotypic parameters estimation, and plant organs tracking over time. The initial study focuses on presenting and validating the accuracy of the methodology on dicotyledons such as cotton but we believe the approach will be more broadly applicable. This study involved applying our technique to a set of six Gossypium hirsutum (cotton) plants studied over four time-points. Manual measurements, performed for each plant at every time-point, were used to assess the accuracy of our pipeline and quantify the error on the morphological parameters estimated. CONCLUSION: By directly comparing our automated mesh based quantitative data with manual measurements of individual stem height, leaf width and leaf length, we obtained the mean absolute errors of 9.34%, 5.75%, 8.78%, and correlation coefficients 0.88, 0.96, and 0.95 respectively. The temporal matching of leaves was accurate in 95% of the cases and the average execution time required to analyse a plant over four time-points was 4.9 minutes. The mesh processing based methodology is thus considered suitable for quantitative 4D monitoring of plant phenotypic features.

A lightweight rapid application development framework for biomedical image analysis.

Biomedical imaging analysis typically comprises a variety of complex tasks requiring sophisticated algorithms and visualising high dimensional data. The successful integration and deployment of the enabling software to clinical (research) partners, for rigorous evaluation and testing, is a crucial step to facilitate adoption of research innovations within medical settings. In this paper, we introduce the Simple Medical Imaging Library Interface (SMILI), an object oriented open-source framework with a compact suite of objects geared for rapid biomedical imaging (cross-platform) application development and deployment. SMILI supports the development of both command-line (shell and Python scripting) and graphical applications utilising the same set of processing algorithms. It provides a substantial subset of features when compared to more complex packages, yet it is small enough to ship with clinical applications with limited overhead and has a license suitable for commercial use. After describing where SMILI fits within the existing biomedical imaging software ecosystem, by comparing it to other state-of-the-art offerings, we demonstrate its capabilities in creating a clinical application for manual measurement of cam-type lesions of the femoral head-neck region for the investigation of femoro-acetabular impingement (FAI) from three dimensional (3D) magnetic resonance (MR) images of the hip. This application for the investigation of FAI proved to be convenient for radiological analyses and resulted in high intra (ICC=0.97) and inter-observer (ICC=0.95) reliabilities for measurement of α-angles of the femoral head-neck region. We believe that SMILI is particularly well suited for prototyping biomedical imaging applications requiring user interaction and/or visualisation of 3D mesh, scalar, vector or tensor data.

Automated T2-mapping of the Menisci From Magnetic Resonance Images in Patients with Acute Knee Injury.

RATIONALE AND OBJECTIVES: This study aimed to evaluate the accuracy of an automated method for segmentation and T2 mapping of the medial meniscus (MM) and lateral meniscus (LM) in clinical magnetic resonance images from patients with acute knee injury. MATERIALS AND METHODS: Eighty patients scheduled for surgery of an anterior cruciate ligament or meniscal injury underwent magnetic resonance imaging of the knee (multiplanar two-dimensional [2D] turbo spin echo [TSE] or three-dimensional [3D]-TSE examinations, T2 mapping). Each meniscus was automatically segmented from the 2D-TSE (composite volume) or 3D-TSE images, auto-partitioned into anterior, mid, and posterior regions, and co-registered onto the T2 maps. The Dice similarity index (spatial overlap) was calculated between automated and manual segmentations of 2D-TSE (15 patients), 3D-TSE (16 patients), and corresponding T2 maps (31 patients). Pearson and intraclass correlation coefficients (ICC) were calculated between automated and manual T2 values. T2 values were compared (Wilcoxon rank sum tests) between torn and non-torn menisci for the subset of patients with both manual and automated segmentations to compare statistical outcomes of both methods. RESULTS: The Dice similarity index values for the 2D-TSE, 3D-TSE, and T2 map volumes, respectively, were 76.4%, 84.3%, and 75.2% for the MM and 76.4%, 85.1%, and 76.1% for the LM. There were strong correlations between automated and manual T2 values (rMM = 0.95, ICCMM = 0.94; rLM = 0.97, ICCLM = 0.97). For both the manual and the automated methods, T2 values were significantly higher in torn than in non-torn MM for the full meniscus and its subregions (P < .05). Non-torn LM had higher T2 values than non-torn MM (P < .05). CONCLUSIONS: The present automated method offers a promising alternative to manual T2 mapping analyses of the menisci and a considerable advance for integration into clinical workflows.

Comparison of 3D bone models of the knee joint derived from CT and 3T MR imaging.

PURPOSE: To examine whether magnetic resonance (MR) imaging can offer a viable alternative to computed tomography (CT) based 3D bone modeling. METHODS: CT and MR (SPACE, TrueFISP, VIBE) images were acquired from the left knee joint of a fresh-frozen cadaver. The distal femur, proximal tibia, proximal fibula and patella were manually segmented from the MR and CT examinations. The MR bone models obtained from manual segmentations of all three sequences were compared to CT models using a similarity measure based on absolute mesh differences. RESULTS: The average absolute distance between the CT and the various MR-based bone models were all below 1mm across all bones. The VIBE sequence provided the best agreement with the CT model, followed by the SPACE, then the TrueFISP data. The most notable difference was for the proximal tibia (VIBE 0.45mm, SPACE 0.82mm, TrueFISP 0.83mm). CONCLUSIONS: The study indicates that 3D MR bone models may offer a feasible alternative to traditional CT-based modeling. A single radiological examination using the MR imaging would allow simultaneous assessment of both bones and soft-tissues, providing anatomically comprehensive joint models for clinical evaluation, without the ionizing radiation of CT imaging.

Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images.

We present a statistical shape model approach for automated segmentation of the proximal humerus and scapula with subsequent bone-cartilage interface (BCI) extraction from 3D magnetic resonance (MR) images of the shoulder region. Manual and automated bone segmentations from shoulder MR examinations from 25 healthy subjects acquired using steady-state free precession sequences were compared with the Dice similarity coefficient (DSC). The mean DSC scores between the manual and automated segmentations of the humerus and scapula bone volumes surrounding the BCI region were 0.926 ± 0.050 and 0.837 ± 0.059, respectively. The mean DSC values obtained for BCI extraction were 0.806 ± 0.133 for the humerus and 0.795 ± 0.117 for the scapula. The current model-based approach successfully provided automated bone segmentation and BCI extraction from MR images of the shoulder. In future work, this framework appears to provide a promising avenue for automated segmentation and quantitative analysis of cartilage in the glenohumeral joint.