MRI wrist in early rheumatoid arthritis: reduction in inflammation assessed quantitatively during treatment period correlates best with clinical improvement.

OBJECTIVE: To investigate (a) which MR features of inflammation (synovitis, tenosynovitis, perfusion) correlate with clinical/serological features in early rheumatoid arthritis (ERA) before, during and after 1 year of treatment and (b) whether quantitative or semi-quantitative measures of inflammation on magnetic resonance imaging (MRI) provides the highest correlation in this regard. METHOD: One hundred one ERA patients (76 females, 25 males, mean age, 53 ± 12 years) underwent clinical/serological testing and 3 T dynamic contrast-enhanced MRI of the most symptomatic wrist. Seventy-seven of the 101 patients completed 1 year of treatment, followed by repeat MR examination. Clinical/serological parameters were correlated with semi-quantitative/quantitative MR measures of inflammation at baseline, during and after 1 year of treatment. Spearman's correlation was applied. RESULTS: Quantitative measures of inflammation correlated better with clinical/serological parameters than semi-quantitative measures, with the highest correlations being for relative change during treatment. Pain reduction correlated with reduced tenosynovitis volume (r = 0.41). Reduction in disease activity correlated with reduction in synovitis volume (r = 0.66) or synovial perfusion parameters (r = 0.58). Decrease in early morning stiffness correlated with decrease in perfusion parameters (r = 0.46). Reduction in ESR and CRP correlated with decrease in synovial volume (r = 0.40 and r = 0.41, respectively). CONCLUSION: In ERA patients, quantitative assessment of inflammation on MRI correlated better with clinical parameters than semi-quantitative assessment. Relative change during treatment yielded the highest correlation. Decrease in tenosynovitis correlated best with reduction in pain while decrease in synovitis volume and perfusion correlated best with reduction in disease activity, early morning stiffness (perfusion), or serological parameters (synovitis volume).

Quantification of brown and white adipose tissue based on Gaussian mixture model using water-fat and T2* MRI in adolescents.

PURPOSE: To develop a technique for the separation and quantification of brown adipose tissue (BAT) and white adipose tissue (WAT) using fat fraction and T2* intensity based on the Gaussian mixture model (GMM). MATERIALS AND METHODS: Chemical-shift water-fat and T2* images were acquired at the neck, supraclavicular, interscapular, and paravertebral regions in 24 volunteers (Obese: n = 12, female/male = 6/6, body mass index [BMI] = 31.3 ± 2.3 kg/m2 , age = 16.1 ± 0.6; Normal weight: n = 12, female/male = 6/6, BMI = 21.2 ± 2.4 kg/m2 , age = 12.9 ± 2.4) using a 3T scanner with the chemical-shift water-fat mDixon sequence. BAT and WAT were clustered based on the Gaussian mixture model using the expectation-maximization algorithm. Results and reproducibility were compared and assessed using independent t-tests and intraclass correlation coefficient. RESULTS: BAT in obese participants was predominately found at the supraclavicular region and in normal-weight participants it was more scattered and distributed in interscapular-supraclavicular, axillary, and spine regions. Absolute volume of BAT was higher in the obese group (Obese: 315.2 mL [±89.1], Normal weight: 248.5 mL [±86.4]), but BAT/WAT ratios were significantly higher (P = 0.029) in the normal group. T2* of BAT (P = 0.04) and volume of WAT (P < 0.001) were significantly lower in the normals. Within-group comparison between male and female indicated no significant differences were found in volume (P = 0.776 (normal), 0.501 [obese]), T2* (P = 0.908 [normal], 0.249 [obese]) and fat-fraction of BAT (P = 0.985 [normal], 0.108 [obese]). The intraclass correlation coefficient showed a good reproducibility in volume (BAT: 0.997, WAT: 0.948), T2* (BAT: 0.969, WAT: 0.983), and fat-fraction (BAT: 0.952, WAT: 0.517). CONCLUSION: BAT identified by this method was in agreement with other studies in terms of location, fat-fraction value, and T2* intensity. The proposed GMM-based segmentation could be a useful nonradiation imaging method for assessment of adipose tissue, in particular for serial follow-up of volume changes after drug or lifestyle interventions for obesity. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:758-768.

Computational medical imaging and hemodynamics framework for functional analysis and assessment of cardiovascular structures.

Cardiac dysfunction constitutes common cardiovascular health issues in the society, and has been an investigation topic of strong focus by researchers in the medical imaging community. Diagnostic modalities based on echocardiography, magnetic resonance imaging, chest radiography and computed tomography are common techniques that provide cardiovascular structural information to diagnose heart defects. However, functional information of cardiovascular flow, which can in fact be used to support the diagnosis of many cardiovascular diseases with a myriad of hemodynamics performance indicators, remains unexplored to its full potential. Some of these indicators constitute important cardiac functional parameters affecting the cardiovascular abnormalities. With the advancement of computer technology that facilitates high speed computational fluid dynamics, the realization of a support diagnostic platform of hemodynamics quantification and analysis can be achieved. This article reviews the state-of-the-art medical imaging and high fidelity multi-physics computational analyses that together enable reconstruction of cardiovascular structures and hemodynamic flow patterns within them, such as of the left ventricle (LV) and carotid bifurcations. The combined medical imaging and hemodynamic analysis enables us to study the mechanisms of cardiovascular disease-causing dysfunctions, such as how (1) cardiomyopathy causes left ventricular remodeling and loss of contractility leading to heart failure, and (2) modeling of LV construction and simulation of intra-LV hemodynamics can enable us to determine the optimum procedure of surgical ventriculation to restore its contractility and health This combined medical imaging and hemodynamics framework can potentially extend medical knowledge of cardiovascular defects and associated hemodynamic behavior and their surgical restoration, by means of an integrated medical image diagnostics and hemodynamic performance analysis framework.

Myocardial Iron Loading Assessment by Automatic Left Ventricle Segmentation with Morphological Operations and Geodesic Active Contour on T2* images.

Myocardial iron loading thalassemia patients could be identified using T2* magnetic resonance images (MRI). To quantitatively assess cardiac iron loading, we proposed an effective algorithm to segment aligned free induction decay sequential myocardium images based on morphological operations and geodesic active contour (GAC). Nine patients with thalassemia major were recruited (10 male and 16 female) to undergo a thoracic MRI scan in the short axis view. Free induction decay images were registered for T2* mapping. The GAC were utilized to segment aligned MR images with a robust initialization. Segmented myocardium regions were divided into sectors for a region-based quantification of cardiac iron loading. Our proposed automatic segmentation approach achieve a true positive rate at 84.6% and false positive rate at 53.8%. The area difference between manual and automatic segmentation was 25.5% after 1000 iterations. Results from T2* analysis indicated that regions with intensity lower than 20 ms were suffered from heavy iron loading in thalassemia major patients. The proposed method benefited from abundant edge information of the free induction decay sequential MRI. Experiment results demonstrated that the proposed method is feasible in myocardium segmentation and was clinically applicable to measure myocardium iron loading.