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BACKGROUND: Diagnosis of infective endocarditis (IE) can be challenging due to negative blood cultures and diagnostic limitations of various imaging modalities. Transesophageal echocardiography (TEE) is the gold standard imaging modality for visualization of valvular vegetations. However, due to the anterior location of the pulmonary valve, post-surgical changes, and sedation requirement, TEE can be challenging in the pediatric population. The aim of this study was to assess the value of Cardiac CT (CCT) for diagnosis of IE in children and young adults with congenital heart disease (CHD). METHODS: This is a single-center retrospective study of pediatric patients with CHD and diagnosis of IE who underwent CCT from 2018 to 2022. Data collected included age, gender, cardiac diagnosis, clinical presentation, echocardiographic/CCT findings, and blood culture results. In addition, modified Duke criteria (MDC) for the diagnosis of IE were applied with and without CCT findings as the diagnostic imaging criterion. RESULTS: Fourteen patients were included in this study with a median age of 11 years old. Nine patients were female. Ten patients had IE of the RV-PA conduit and four patients had IE of the aortic valve. Using MDC, 4 patients had definite IE. After including CCT findings, 11 patients (79%) met MDC for definite IE. Blood cultures were positive in 12 patients. CCT revealed the following complications: thromboembolic findings/pseudoaneurysms in 5 patients each and prosthetic valve perforation/prosthetic valve leak in one patient each. CONCLUSIONS: This study reinforces the complimentary role of CCT to echocardiography in the work-up and diagnosis of IE in patients with CHD. With further improvement in lowering radiation exposure, CCT may have a key role in the diagnostic work-up of endocarditis and could be implemented in the diagnostic criteria of IE.
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Duchenne muscular dystrophy (DMD) is a childhood-onset neuromuscular disease that results in the degeneration of muscle, starting in the extremities, before progressing to more vital areas, such as the lungs. Respiratory failure and pneumonia due to respiratory muscle weakness lead to hospitalization and early mortality. However, tracking the disease in this region can be difficult, as current methods are based on breathing tests and are incapable of distinguishing between muscle involvements. Cine MRI scans give insight into respiratory muscle movements, but the images suffer due to low spatial resolution and poor signal-to-noise ratio. Thus, a robust lung segmentation method is required for accurate analysis of the lung and respiratory muscle movement. We deployed a deep learning approach that utilizes sequence-specific prior information to assist the segmentation of lung in cine MRI. More specifically, we adopt a holistically nested network to conduct image-to-image holistic training and prediction. One frame of the cine MRI is used in the training and applied to the remainder of the sequence ([Formula: see text] frames). We applied this method to cine MRIs of the lung in the axial, sagittal, and coronal planes. Characteristic lung motion patterns during the breathing cycle were then derived from the segmentations and used for diagnosis. Our data set consisted of 31 young boys, age [Formula: see text] years, 15 of whom suffered from DMD. The remaining 16 subjects were age-matched healthy volunteers. For validation, slices from inspiratory and expiratory cycles were manually segmented and compared with results obtained from our method. The Dice similarity coefficient for the deep learning-based method was [Formula: see text] for the sagittal view, [Formula: see text] for the axial view, and [Formula: see text] for the coronal view. The holistic neural network approach was compared with an approach using Demon's registration and showed superior performance. These results suggest that the deep learning-based method reliably and accurately segments the lung across the breathing cycle.
RESUMO
OBJECTIVE: To examine the diaphragm and chest wall dynamics with cine breathing magnetic resonance imaging (MRI) in ambulatory boys with Duchenne muscular dystrophy (DMD) without respiratory symptoms and controls. METHODS: In 11 DMD boys and 15 controls, cine MRI of maximal breathing was recorded for 10 sec. The lung segmentations were done by an automated pipeline based on a Holistically-Nested Network model (HNN method). Lung areas, diaphragm, and chest wall motion were measured throughout the breathing cycle. RESULTS: The HNN method reliably identified the contours of the lung and the diaphragm in every frame of each dataset (~180 frames) within seconds. The lung areas at maximal inspiration and expiration were reduced in DMD patients relative to controls (P = 0.02 and <0.01, respectively). The change in the lung area between inspiration and expiration correlated with percent predicted forced vital capacity (FVC) in patients (rs = 0.75, P = 0.03) and was not significantly different between groups. The diaphragm position, length, contractility, and motion were not significantly different between groups. Chest wall motion was reduced in patients compared to controls (P < 0.01). INTERPRETATION: Cine breathing MRI allows independent and reliable assessment of the diaphragm and chest wall dynamics during the breathing cycle in DMD patients and controls. The MRI data indicate that ambulatory DMD patients breathe at lower lung volumes than controls when their FVC is in the normal range. The diaphragm moves normally, whereas chest wall motion is reduced in these boys with DMD.
