Deep learning in rheumatological image interpretation.

Artificial intelligence techniques, specifically deep learning, have already affected daily life in a wide range of areas. Likewise, initial applications have been explored in rheumatology. Deep learning might not easily surpass the accuracy of classic techniques when performing classification or regression on low-dimensional numerical data. With images as input, however, deep learning has become so successful that it has already outperformed the majority of conventional image-processing techniques developed during the past 50 years. As with any new imaging technology, rheumatologists and radiologists need to consider adapting their arsenal of diagnostic, prognostic and monitoring tools, and even their clinical role and collaborations. This adaptation requires a basic understanding of the technical background of deep learning, to efficiently utilize its benefits but also to recognize its drawbacks and pitfalls, as blindly relying on deep learning might be at odds with its capabilities. To facilitate such an understanding, it is necessary to provide an overview of deep-learning techniques for automatic image analysis in detecting, quantifying, predicting and monitoring rheumatic diseases, and of currently published deep-learning applications in radiological imaging for rheumatology, with critical assessment of possible limitations, errors and confounders, and conceivable consequences for rheumatologists and radiologists in clinical practice.

Automated quantification of the pulmonary vasculature in pulmonary embolism and chronic thromboembolic pulmonary hypertension.

The shape and distribution of vascular lesions in pulmonary embolism (PE) and chronic thromboembolic pulmonary hypertension (CTEPH) are different. We investigated whether automated quantification of pulmonary vascular morphology and densitometry in arteries and veins imaged by computed tomographic pulmonary angiography (CTPA) could distinguish PE from CTEPH. We analyzed CTPA images from a cohort of 16 PE patients, 6 CTEPH patients, and 15 controls. Pulmonary vessels were extracted with a graph-cut method, and separated into arteries and veins using deep-learning classification. Vascular morphology was quantified by the slope (α) and intercept (ß) of the vessel radii distribution. To quantify lung perfusion defects, the median pulmonary vascular density was calculated. By combining these measurements with densities measured in parenchymal areas, pulmonary trunk, and descending aorta, a static perfusion curve was constructed. All separate quantifications were compared between the three groups. No vascular morphology differences were detected in contrast to vascular density values. The median vascular density (interquartile range) was -567 (113), -452 (95), and -470 (323) HU, for the control, PE, and CTEPH group. The static perfusion curves showed different patterns between groups, with a statistically significant difference in aorta-pulmonary trunk gradient between the PE and CTEPH groups (p = 0.008). In this proof of concept study, not vasculature morphology but densities differentiated between patients of three groups. Further technical improvements are needed to allow for accurate differentiation between PE and CTEPH, which in this study was only possible statistically by measuring the density gradient between aorta and pulmonary trunk.

MRI of diffuse-type tenosynovial giant cell tumour in the knee: a guide for diagnosis and treatment response assessment.

Tenosynovial giant cell tumour (TGCT) is a rare soft-tissue tumour originating from synovial lining of joints, bursae and tendon sheaths. The tumour comprises two subtypes: the localised-type (L-TGCT) is characterised by a single, well-defined lesion, whereas the diffuse-type (D-TGCT) consists of multiple lesions without clear margins. D-TGCT was previously known as pigmented villonodular synovitis. Although benign, TGCT can behave locally aggressive, especially the diffuse-type. Magnetic resonance imaging (MRI) is the modality of choice to diagnose TGCT and discriminate between subtypes. MRI can also provide a preoperative map before synovectomy, the mainstay of treatment. Finally, since the arrival of colony-stimulating factor 1-receptor inhibitors, a novel systemic therapy for D-TGCT patients with relapsed or inoperable disease, MRI is key in assessing treatment response. As recurrence after treatment of D-TGCT occurs more often than in L-TGCT, follow-up imaging plays an important role in D-TGCT. Reading follow-up MRIs of these diffuse synovial tumours may be a daunting task. Therefore, this educational review focuses on MRI findings in D-TGCT of the knee, which represents the most involved joint site (approximately 70% of patients). We aim to provide a systematic approach to assess the knee synovial recesses, highlight D-TGCT imaging findings, and combine these into a structured report. In addition, differential diagnoses mimicking D-TGCT, potential pitfalls and evaluation of tumour response following systemic therapies are discussed. Finally, we propose automated volumetric quantification of D-TGCT as the next step in quantitative treatment response assessment as an alternative to current radiological assessment criteria.

MRI-based 3D retinal shape determination.

OBJECTIVE: To establish a good method to determine the retinal shape from MRI using three-dimensional (3D) ellipsoids as well as evaluate its reproducibility. METHODS AND ANALYSIS: The left eyes of 31 volunteers were imaged using high-resolution ocular MRI. The 3D MR-images were segmented and ellipsoids were fitted to the resulting contours. The dependency of the resulting ellipsoid parameters on the evaluated fraction of the retinal contour was assessed by fitting ellipsoids to 41 different fractions. Furthermore, the reproducibility of the complete procedure was evaluated in four subjects. Finally, a comparison with conventional two-dimensional (2D) methods was made. RESULTS: The mean distance between the fitted ellipsoids and the segmented retinal contour was 0.03±0.01 mm (mean±SD) for the central retina and 0.13±0.03 mm for the peripheral retina. For the central retina, the resulting ellipsoid radii were 12.9±0.9, 13.7±1.5 and 12.2±1.2 mm along the horizontal, vertical and central axes. For the peripheral retina, these radii decreased to 11.9±0.6, 11.6±0.4 and 10.4±0.7 mm, which was accompanied by a mean 1.8 mm posterior shift of the ellipsoid centre. The reproducibility of the ellipsoid fitting was 0.3±1.2 mm for the central retina and 0.0±0.1 mm for the peripheral retina. When 2D methods were used to fit the peripheral retina, the fitted radii differed a mean 0.1±0.1 mm from the 3D method. CONCLUSION: An accurate and reproducible determination of the 3D retinal shape based on MRI is provided together with 2D alternatives, enabling wider use of this method in the field of ophthalmology.

An Automatic Framework to Create Patient-specific Eye Models From 3D Magnetic Resonance Images for Treatment Selection in Patients With Uveal Melanoma.

PURPOSE: The optimal treatment strategy for uveal melanoma (UM) relies on many factors, the most important being tumor size and location. Building on recent developments in high-resolution 3D ocular magnetic resonance imaging (MRI), we developed an automatic image-processing framework to create patient-specific eye models and to subsequently determine the full 3D tumor shape and size automatically. METHODS AND MATERIALS: From 15 patients with UM, 3D inversion-recovery gradient-echo (T1-weighted) and 3D fat-suppressed spin-echo (T2-weighted) images were acquired with a 7T MRI scanner. First, the sclera and cornea were segmented from the T2-weighted image by mesh-fitting. The T1- and T2-weighted images were then coregistered. From the registered T1-weighted image, the lens, vitreous body, retinal detachment, and tumor were segmented. Fuzzy C-means clustering was used to differentiate the tumor from retinal detachments. The tumor model was verified and (if needed) edited by an ophthalmic MRI specialist. Subsequently, the prominence and largest basal diameter of the tumor were measured automatically based on the verified contours. These results were compared with manual assessments on the original images and with ultrasound measurements to show the errors in manual analysis. RESULTS: The framework successfully created an eye model fully automatically in 12 cases. In these cases, a Dice similarity coefficient (mean surface distance) of 97.7%±0.84% (0.17±0.11 mm) was achieved for the sclera, 96.8%±1.05% (0.20±0.06 mm) for the vitreous body, 91.6%±4.83% (0.15±0.06 mm) for the lens, and 86.0%±7.4% (0.35±0.27 mm) for the tumor. The manual assessments deviated, on average, 0.39±0.31 mm in prominence and 1.7±1.22 mm in basal diameter from the automatic measurements. CONCLUSIONS: The described framework combined information from T1- and T2-weighted images to accurately determine tumor boundaries in 3D. The proposed process may have a direct effect on clinical workflow, as it enables an accurate 3D assessment of tumor dimensions, which directly influences therapy selection.

Evaluation of intraocular lens position and retinal shape in negative dysphotopsia using high-resolution magnetic resonance imaging.

PURPOSE: To assess potential relationships of intraocular lens (IOL) position and retinal shape in negative dysphotopsia (ND). SETTING: Department of Ophthalmology, Leiden University Medical Center, Leiden, the Netherlands. DESIGN: Case-control study. METHODS: High-resolution ocular magnetic resonance imaging (MRI) scans were performed in patients with ND and pseudophakic controls, and subsequently used to determine the displacement and tilt of the in-the-bag IOL about the pupil and iris. In addition, anterior segment tomography was used to assess the iris-IOL distance. Furthermore, the retinal shape was quantified from the MRI scans by fitting an ellipse to the segmented inner boundary of the retina. Both the IOL position and retinal shape were compared between groups to assess their potential role in the etiology of ND. RESULTS: In total, 37 patients with ND and 26 pseudophakic controls were included in the study. The mean displacement and tilt of the IOL were less than 0.1 mm and 0.5 degrees, respectively, in both groups and all directions. The corresponding mean iris-IOL distance was 1.1 mm in both groups. Neither of these values differed statistically significantly between groups (all P values >.6). The retinal shape showed large variations but was not statistically significantly different between the groups in both the left-right (P = .10) and the anterior-posterior (P = .56) directions. CONCLUSIONS: In this study, the in-the-bag IOL position and retinal shape did not statistically significantly differ between patients with ND and the general pseudophakic population. Given the large variation in retinal shape between subjects, however, it could still be an important factor in a multifactorial origin of ND.

Prediction of lung function and lung density of young adults who had bronchopulmonary dysplasia.

COPD risk is jointly determined by fetal lung development, lung growth rate and lung growth duration leading to the maximally attained level of lung function in early adulthood. Bronchopulmonary dysplasia (BPD) is considered a developmental arrest of alveolarisation. Long-term outcome studies of adult survivors born before the introduction of surfactant therapy ("old BPD") showed impaired lung function. We aimed to predict adult lung function and lung density in a cohort of premature infants born in the surfactant era, representing "new BPD". We studied a cohort of young adults born between 1987 and 1998, with (n=36) and without (n=28) BPD, treated in a single centre. Their perinatal characteristics and pulmonary function in infancy were studied by regression analysis for correlation with adult lung function and tissue lung density, all expressed by z-scores, at a mean age of 19.7±1.1 and 21±2.2 years, respectively. Although BPD adults had on average lower forced expiratory volume in 1 s (zFEV1)/forced vital capacity (FVC) and zFEV1 than those without, 55% of the BPD group had zFEV1/FVC values above the lower limit of normal (LLN). Moreover, above LLN values of diffusing capacity of the lung for carbon monoxide (zD LCO) was present in 89% of BPD adults and lung density in 71%. Only higher oxygen supply (F IO2) at 36 weeks post-conception of BPD subjects had a trend with lower zFEV1 (B=-6.4; p=0.053) and lower zD LCO (B=-4.1; p=0.023) at adulthood. No statistically significant predictors of new BPD were identified.

Use of artificial intelligence in imaging in rheumatology - current status and future perspectives.

After decades of basic research with many setbacks, artificial intelligence (AI) has recently obtained significant breakthroughs, enabling computer programs to outperform human interpretation of medical images in very specific areas. After this shock wave that probably exceeds the impact of the first AI victory of defeating the world chess champion in 1997, some reflection may be appropriate on the consequences for clinical imaging in rheumatology. In this narrative review, a short explanation is given about the various AI techniques, including 'deep learning', and how these have been applied to rheumatological imaging, focussing on rheumatoid arthritis and systemic sclerosis as examples. By discussing the principle limitations of AI and deep learning, this review aims to give insight into possible future perspectives of AI applications in rheumatology.

Artificial intelligence in detecting early RA.

To prevent chronicity of Rheumatoid Arthritis (RA) by early treatment, detecting inflammatory signs in an early phase is essential. Since Magnetic Resonance Imaging (MRI) of the wrist, hand and foot can detect inflammation before it is clinically detectable, this modality may play an important role in achieving very early diagnoses. By collecting large amounts of MRI data from healthy controls and patients with arthralgia suspicious for progression to RA, patterns can be studied that are most specific for early development of RA. Furthermore, MRI can be used as outcome parameter for randomized placebo-controlled trials on early RA treatment, by detecting subtle changes in image intensities originating from natural progression or treatment effects. Very large amounts of MRI data, however, make manual quantification impractical and the coarse scale used in visual scoring systems (i.e. whole values between 0 and 3) limits its sensitivity to detect changes that are likely to be very subtle in such an early phase. In recent years, advances in artificial intelligence and especially 'deep learning' in interpreting medical images have shown that -in specific areas- a computerized analysis can outperform human observers. Therefore, research has been initiated into applying these artificial intelligence techniques to the quantification of early RA from MRI data. In this paper, an overview is given on the background and history of artificial intelligence, with a special focus on recent developments in 'deep learning', and how these techniques could be applied to detect subtle inflammatory changes in MRI data.

Automatic quantitative analysis of pulmonary vascular morphology in CT images.

PURPOSE: Vascular remodeling is a significant pathological feature of various pulmonary diseases, which may be assessed by quantitative computed tomography (CT) imaging. The purpose of this study was therefore to develop and validate an automatic method for quantifying pulmonary vascular morphology in CT images. METHODS: The proposed method consists of pulmonary vessel extraction and quantification. For extracting pulmonary vessels, a graph-cuts-based method is proposed which considers appearance (CT intensity) and shape (vesselness from a Hessian-based filter) features, and incorporates distance to the airways into the cost function to prevent false detection of airway walls. For quantifying the extracted pulmonary vessels, a radius histogram is generated by counting the occurrence of vessel radii, calculated from a distance transform-based method. Subsequently, two biomarkers, slope α and intercept ß, are calculated by linear regression on the radius histogram. A public data set from the VESSEL12 challenge was used to independently evaluate the vessel extraction. The quantitative analysis method was validated using images of a three-dimensional (3D) printed vessel phantom, scanned by a clinical CT scanner and a micro-CT scanner (to obtain a gold standard). To confirm the association between imaging biomarkers and pulmonary function, 77 scleroderma patients were investigated with the proposed method. RESULTS: In the independent evaluation with the public data set, our vessel segmentation method obtained an area under the receiver operating characteristic (ROC) curve of 0.976. The median radius difference between clinical and micro-CT scans of a 3D printed vessel phantom was 0.062 ± 0.020 mm, with interquartile range of 0.199 ± 0.050 mm. In the studied patient group, a significant correlation between diffusion capacity for carbon monoxide and the biomarkers, α (R = -0.27, P = 0.018) and ß (R = 0.321, P = 0.004), was obtained. CONCLUSION: In conclusion, the proposed method was validated independently using a public data set resulting in an area under the ROC curve of 0.976 and using a 3D printed vessel phantom data set, showing a vessel sizing error of 0.062 mm (0.16 in-plane pixel units). The correlation between imaging biomarkers and diffusion capacity in a clinical data set confirmed an association between lung structure and function. This quantification of pulmonary vascular morphology may be helpful in understanding the pathophysiology of pulmonary vascular diseases.

Registration of vascular structures using a hybrid mixture model.

PURPOSE: Morphological changes to anatomy resulting from invasive surgical procedures or pathology, typically alter the surrounding vasculature. This makes it useful as a descriptor for feature-driven image registration in various clinical applications. However, registration of vasculature remains challenging, as vessels often differ in size and shape, and may even miss branches, due to surgical interventions or pathological changes. Furthermore, existing vessel registration methods are typically designed for a specific application. To address this limitation, we propose a generic vessel registration approach useful for a variety of clinical applications, involving different anatomical regions. METHODS: A probabilistic registration framework based on a hybrid mixture model, with a refinement mechanism to identify missing branches (denoted as HdMM+) during vasculature matching, is introduced. Vascular structures are represented as 6-dimensional hybrid point sets comprising spatial positions and centerline orientations, using Student's t-distributions to model the former and Watson distributions for the latter. RESULTS: The proposed framework is evaluated for intraoperative brain shift compensation, and monitoring changes in pulmonary vasculature resulting from chronic lung disease. Registration accuracy is validated using both synthetic and patient data. Our results demonstrate, HdMM+ is able to reduce more than [Formula: see text] of the initial error for both applications, and outperforms the state-of-the-art point-based registration methods such as coherent point drift and Student's t-distribution mixture model, in terms of mean surface distance, modified Hausdorff distance, Dice and Jaccard scores. CONCLUSION: The proposed registration framework models complex vascular structures using a hybrid representation of vessel centerlines, and accommodates intricate variations in vascular morphology. Furthermore, it is generic and flexible in its design, enabling its use in a variety of clinical applications.

Pulmonary Vascular Morphology Associated With Gas Exchange in Systemic Sclerosis Without Lung Fibrosis.

PURPOSE: Gas exchange in systemic sclerosis (SSc) is known to be affected by fibrotic changes in the pulmonary parenchyma. However, SSc patients without detectable fibrosis can still have impaired gas transfer. We aim to investigate whether pulmonary vascular changes could partly explain a reduction in gas transfer of SSc patients without fibrosis. MATERIALS AND METHODS: We selected 77 patients whose visual computed tomography (CT) scoring showed no fibrosis. Pulmonary vessels were detected automatically in CT images, and their local radii were calculated. The frequency of occurrence for each radius was calculated, and, from this radius histogram, 2 imaging biomarkers (α and ß) were extracted, wherein α reflects the relative contribution of small vessels compared with large vessels, and ß represents the vessel tree capacity. Correlations between imaging biomarkers and gas transfer [single-breath diffusion capacity for carbon monoxide corrected for hemoglobin concentration (DLCOc) %predicted] were evaluated with Spearman correlation. Multivariable stepwise linear regression was performed with DLCOc %predicted as the dependent variable and age, BMI, sPAP, FEV1 %predicted, TLC %predicted, FVC %predicted, α, ß, voxel size, and CT-derived lung volume as independent variables. RESULTS: Both α and ß were significantly correlated with gas transfer (R=-0.29, P-value=0.011 and R=0.32, P-value=0.004, respectively). The multivariable stepwise linear regression analysis selected sPAP [coefficient=-0.78; 95% confidence interval (CI)=-1.07, -0.49; P-value<0.001], ß (coefficient=8.6; 95% CI=4.07, 13.1; P-value<0.001), and FEV1% predicted (coefficient=0.3; 95% CI=0.12, 0.48; P-value=0.001) as significant independent predictors of DLCOc %predicted (R=0.71, P-value<0.001). CONCLUSIONS: In SSc patients without detectable pulmonary fibrosis, impaired gas exchange is associated with alterations in pulmonary vascular morphology.

Regional lung densities in alpha-1 antitrypsin deficiency compared to predicted values.

BACKGROUND: We developed a method to calculate a standard score for lung tissue mass derived from CT scan images from a control group without respiratory disease. We applied the method to images from subjects with emphysema associated with alpha-1 antitrypsin deficiency (AATD) and used it to study regional patterns of differential tissue mass. METHODS: We explored different covariates in 76 controls. Standardization was applied to facilitate comparability between different CT scanners and a standard Z-score (Standard Mass Score, SMS) was developed, representing lung tissue loss compared to normal lung mass. This normative data was defined for the entire lungs and for delineated apical, central and basal regions. The agreement with DLCO%pred was explored in a data set of 180 patients with emphysema who participated in a trial of alpha-1-antitrypsin augmentation treatment (RAPID). RESULTS: Large differences between emphysematous and normal tissue of more than 10 standard deviations were found. There was reasonable agreement between SMS and DLCO%pred for the global densitometry (κ = 0.252, p < 0.001), varying from κ = 0.138 to κ = 0.219 and 0.264 (p < 0.001), in the apical, central and basal region, respectively. SMS and DLCO%pred correlated consistently across apical, central and basal regions. The SMS distribution over the different lung regions showed a distinct pattern suggesting that emphysema due to severe AATD develops from basal to central and ultimately apical regions. CONCLUSIONS: Standardization and normalization of lung densitometry is feasible and the adoption of the developed principles helps to characterize the distribution of emphysema, required for clinical decision making.

Identifying MRI-detected inflammatory features specific for rheumatoid arthritis: two-fold feature reduction maintains predictive accuracy in clinically suspect arthralgia patients.

OBJECTIVE: MRI-detected inflammation is considered of diagnostic value for rheumatoid arthritis (RA), but its evaluation involves a time-consuming scoring of 61 joint-level features. It is not clear, however, which of these features are specific for RA and whether evaluating a subset of specific features is sufficient to differentiate RA patients. This study aimed to identify a subset of RA-specific features in a case-control setting and validate them in a longitudinal cohort of arthralgia patients. METHODS: The difference in frequency of MRI-detected inflammation (bone marrow edema, synovitis, and tenosynovitis) between 199 RA patients and 193 controls was studied in 61 features across the wrist, metacarpophalangeal, and metatarsophalangeal joints. A subset of RA-specific features was obtained by applying a cutoff on the frequency difference while maximizing discriminative performance. For validation, this subset was used to predict arthritis development in 225 clinically suspect arthralgia (CSA) patients. Diagnostic performance was compared to a reference method that uses the complete set of 61 features normalized for inflammation levels in age-matched controls. RESULTS: Subset of 30 features, mainly (teno)synovitis, was obtained from the case-control setting. Validation in CSA patients yielded an area of 0.69 (95% CI: 0.59-0.78) under the ROC curve and a positive predictive value (PPV) of 31%, compared to 0.68 (95% CI: 0.60-0.77) and 29% PPV of the reference method with 61 features. CONCLUSION: Subset of 30 MRI-detected inflammatory features, dominated by (teno)synovitis, offers a considerable reduction of scoring efforts without compromising accuracy for prediction of arthritis development in CSA patients.

Automatic quantification of tenosynovitis on MRI of the wrist in patients with early arthritis: a feasibility study.

OBJECTIVES: Tenosynovitis (inflammation of the synovial lining of the sheath surrounding tendons) is frequently observed on MRI of early arthritis patients. Since visual assessment of tenosynovitis is a laborious task, we investigated the feasibility of automatic quantification of tenosynovitis on MRI of the wrist in a large cohort of early arthritis patients. METHODS: For 563 consecutive early arthritis patients (clinically confirmed arthritis ≥ 1 joint, symptoms < 2 years), MR scans of the wrist were processed in three automatic stages. First, super-resolution reconstruction was applied to fuse coronal and axial scans into a single high-resolution three-dimensional image. Next, 10 extensor/flexor tendon regions were segmented using atlas-based segmentation and marker-based watershed. A measurement region of interest (ROI) was defined around the tendons. Finally, tenosynovitis was quantified by identifying image intensity values associated with tenosynovial inflammation using fuzzy clustering and measuring the fraction of voxels with these characteristic intensities within the measurement ROI. A subset of 60 patients was used for training and the remaining 503 patients for validation. Correlation between quantitative measurements and visual scores was assessed through Pearson correlation coefficient. RESULTS: Pearson correlation between quantitative measurements and visual scores across 503 patients was r = 0.90, p < 0.001. False detections due to blood vessels and synovitis present within the measurement ROI contributed to a median offset from zero equivalent to 13.8% of the largest measurement value. CONCLUSION: Quantitative measurement of tenosynovitis on MRI of the wrist is feasible and largely consistent with visual scores. Further improvements in segmentation and exclusion of false detections are warranted. KEY POINTS: • Automatic measurement of tenosynovitis on MRI of the wrist is feasible and largely consistent with visual scores. • Blood vessels and synovitis in the vicinity of evaluated tendons can contribute to false detections in automatic measurements. • Further improvements in segmentation and exclusion of false detections are important directions of future work on the path to a robust quantification framework.

Lung function and CT lung densitometry in 37- to 39-year-old individuals with alpha-1-antitrypsin deficiency.

BACKGROUND: Alpha-1-antitrypsin (AAT) deficiency is a hereditary disorder that predisposes to emphysema. A cohort of severe (PiZZ) and moderate (PiSZ) AAT-deficient newborn infants was identified by the Swedish national neonatal AAT screening program in 1972-1974 and has been followed-up since birth. Our aim was to study whether the cohort has signs of emphysema in pulmonary function tests (PFTs) and computed tomography (CT) densitometry at 38 years of age in comparison with an age-matched control group, randomly selected from the population registry. METHODS: Forty-one PiZZ, 18 PiSZ, and 61 control subjects (PiMM) underwent complete PFTs, measurement of resistance and reactance in the respiratory system by impulse oscillometry (IOS)/forced oscillation technique (FOT), and CT densitometry. The results were related to self-reported smoking habits. RESULTS: The total lung capacity (TLC) % of the predicted value was significantly higher in the PiZZ ever-smokers than in the PiZZ never-smokers (P<0.05), PiSZ never-smokers (P=0.01) and the PiMM never-smokers (P=0.01). The residual volume (RV) % of the predicted value was significantly higher in the PiZZ ever-smokers compared to the PiMM never-smokers (P<0.01). The PiZZ ever-smokers had a significantly lower carbon monoxide transfer coefficient (Kco) than the PiSZ never-smokers (P<0.01) and PiMM never-smokers (P<0.01). Respiratory system resistance at 5 Hz (P<0.01), at 20 Hz (P<0.01), and the area of low reactance (Alx; P<0.05) were significantly lower and respiratory system reactance at 5 Hz (P<0.05) was significantly higher in PiZZ subjects compared to the PiMM subjects. No statistically significant differences in the CT densitometry parameters were found between the Pi subgroups. CONCLUSION: The physiological parameters in the PiZZ ever-smokers showed evidence of hyperinflation and emphysema before the age of 40 years.

Automatic quantification of bone marrow edema on MRI of the wrist in patients with early arthritis: A feasibility study.

PURPOSE: To investigate the feasibility of automatic quantification of bone marrow edema (BME) on MRI of the wrist in patients with early arthritis. METHODS: For 485 early arthritis patients (clinically confirmed arthritis of one or more joints, symptoms for less than 2 years), MR scans of the wrist were processed in three automatic stages. First, super-resolution reconstruction was applied to fuse coronal and axial scans into a single high-resolution 3D image. Next, the carpal bones were located and delineated using atlas-based segmentation. Finally, the extent of BME within each bone was quantified by identifying image intensity values characteristic of BME by fuzzy clustering and measuring the fraction of voxels with these characteristic intensities within each bone. Correlation with visual BME scores was assessed through Pearson correlation coefficient. RESULTS: Pearson correlation between quantitative and visual BME scores across 485 patients was r=0.83, P<0.001. CONCLUSIONS: Quantitative measurement of BME on MRI of the wrist has the potential to provide a feasible alternative to visual scoring. Complete automation requires automatic detection and compensation of acquisition artifacts. Magn Reson Med 79:1127-1134, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Treatment Effect of Balloon Pulmonary Angioplasty in Chronic Thromboembolic Pulmonary Hypertension Quantified by Automatic Comparative Imaging in Computed Tomography Pulmonary Angiography.

OBJECTIVES: Balloon pulmonary angioplasty (BPA) in patients with inoperable chronic thromboembolic pulmonary hypertension (CTEPH) can have variable outcomes. To gain more insight into this variation, we designed a method for visualizing and quantifying changes in pulmonary perfusion by automatically comparing computed tomography (CT) pulmonary angiography before and after BPA treatment. We validated these quantifications of perfusion changes against hemodynamic changes measured with right-sided heart catheterization. MATERIALS AND METHODS: We studied 14 consecutive CTEPH patients (12 women; age, 70.5 ± 24), who underwent CT pulmonary angiography and right-sided heart catheterization, before and after BPA. Posttreatment images were registered to pretreatment CT scans (using the Elastix toolbox) to obtain corresponding locations. Pulmonary vascular trees and their centerlines were detected using a graph cuts method and a distance transform method, respectively. Areas distal from vessels were defined as pulmonary parenchyma. Subsequently, the density changes within the vascular centerlines and parenchymal areas were calculated and corrected for inspiration level differences. For visualization, the densitometric changes were displayed in color-coded overlays. For quantification, the median and interquartile range of the density changes in the vascular and parenchymal areas (ΔVD and ΔPD) were calculated. The recorded changes in hemodynamic parameters, including changes in systolic, diastolic, and mean pulmonary artery pressure (ΔsPAP, ΔdPAP, and ΔmPAP, respectively) and vascular resistance (ΔPVR), were used as reference assessments of the treatment effect. Spearman correlation coefficients were employed to investigate the correlations between changes in perfusion and hemodynamic changes. RESULTS: Comparative imaging maps showed distinct patterns in perfusion changes among patients. Within pulmonary vessels, the interquartile range of ΔVD correlated significantly with ΔsPAP (R = -0.58, P = 0.03), ΔdPAP (R = -0.71, P = 0.005), ΔmPAP (R = -0.71, P = 0.005), and ΔPVR (R = -0.77, P = 0.001). In the parenchyma, the median of ΔPD had significant correlations with ΔdPAP (R = -0.58, P = 0.030) and ΔmPAP (R = -0.59, P = 0.025). CONCLUSIONS: Comparative imaging analysis in CTEPH patients offers insight into differences in BPA treatment effect. Quantification of perfusion changes provides noninvasive measures that reflect hemodynamic changes.