Test-retest reliability of transcarpal sensory NCV method for diagnosis of carpal tunnel syndrome

Background: Carpal Tunnel Syndrome (CTS) is the most frequent entrapment neuropathy affecting the upper extremity. There are a variety of electrodiagnostic methods available for documenting median neuropathy in CTS. In some studies, determining the sensory NCV across the palm-wrist segment has been introduced as the most sensitive diagnostic procedure for CTS. The aim of this study was to investigate the test-retest reliability of transcarpal median sensory NCV method for the diagnosis of CTS. Materials and Methods: Twenty-three patients with clinical symptoms of CTS were tested two times by two different practitioners in one session and again by the first practitioner after one week. Stimulation of the median nerve was performed in the wrist and palm, with a conduction distance maximum of 7 cm, reliabilities of median nerves sensory nerve action potential latencies with stimulation at wrist and palm (W-SNAP, P-SNAP) and its transcarpal NCV were assessed with intraclass correlation coefficient (ICC). Results: Comparison of the obtained values, which were done by two practitioners in one session showed ICC of W-SNAP latency, P-SNAP latency and transcarpal NCV of 0.93, 0.88 and 0.87, respectively and values that were done by one practitioner in two sessions with one-week interval showed ICC of 0.60, 0.50 and 0.47, respectively. Conclusion: Our findings suggest excellent interpractitioner test-retest reliability of transcarpal median sensory NCV method for diagnosing CTS

Improved navigator-gated motion compensation in cardiac MR using additional constraint of magnitude of motion-corrupted data

Background. In conventional prospective respiratory navigator (NAV) acquisitions, 40-60% of the acquired data are discarded resulting in low efficiency and long scan times [1,2].Compressed-sensing Motion Compensation (CosMo) has a shorter fixed scan time by acquiring the full inner k-space and estimating the NAV-rejected outer k-space lines [3]. Respiratory motion will mainly manifest itself as phase variation in the acquired k-space data. We sought to determine if the addition of the magnitude of the rejected k-space lines as a constraint in image reconstruction will improve the performance of CosMo. Methods. To investigate the variability of the magnitude of kspace lines at different respiratory phases, free-breathing, ECG-triggered, targeted right coronary images with multiple averages were acquired from 10 healthy adult subjects. Magnitude variability was investigated quantitatively by calculating the cross-correlation between accepted and rejected k-space lines. CosMo was implemented retrospectively on one acquisition from each subject. The inner k-space (31 ky by 7 kz lines) was filled with lines acquired within the 5mm gating window from all acquisitions. The outer kspace was then filled only with lines from the first average acquired within the 5 mm gating window, resulting in an undersampled k-space with a fully sampled center. For reliable image reconstruction with CosMo, 10-20% of the inner k-space must be fully-sampled. The missing outer k-space lines were then estimated using LOST with an additional magnitude constraint within each estimation iteration or in the final iteration for each coil [4]. The results were compared with prospective NAVgating with a gating window of 5 mm and CosMo reconstruction without the magnitude constraint. Results. Figure 1 shows the cross-correlation between the accepted and worst rejected k-space lines for each position. The correlation is close to 1 at the center of kspace where the majority of image information is contained, indicating low variability in magnitude information at different respiratory phases. Figure 2 shows right coronary images acquired using a) fully-sampled, 5-mm gated data, b) the original CosMo, and CosMo with the additional magnitude constraint c) inside each iteration and d) in the final iteration. The relative signal-to-noise in the left ventricle blood pool is: $30.71 \pm 6.5; 40.32 \pm 14.2; 53.9 \pm 26.8; 56.8 \pm 25.9$ for each reconstruction, respectively. Significant differences (p<0.05) are present for all measurements except between the original CosMo and the CosMo image with the magnitude constraint in each iteration (p=0.09). Conclusions. The addition of the magnitude of rejected lines, readily available in all navigator-gated scans, as a constraint in CosMo results in improved image quality as measured by relative SNR. Funding. NIH R01EB008743-01A2

Free-breathing late gadolinium enhancement CMR with a fixed short scan time using CosMo

To evaluate the performance of compressed sensing for motion correction (CosMo) [1] in compensating the respiratory motion of the heart in 3D late gadolinium enhancement (LGE) CMR

Applying the data fusion method to evaluation of the performance of two control signals in monitoring polarization mode dispersion effects in fiber optic links

With increasing distance and bit rate in fiber optic links the effects of polarization mode dispersion (PMD) have been highlighted. Since PMD has a statistical nature, using a control signal that can provide accurate information to dynamically tune a PMD compensator is of great importance. In this paper, we apply the data fusion method with the aim of introducing a method that can be used to evaluate more accurately the performance of control signals before applying them in a PMD compensation system. Firstly, the minimum and average degree of polarization (DOP_min and DOP_ave respectively) as control signals in monitoring differential group delay (DGD) for a system including all-order PMD are calculated. Then, features including the amounts of sensitivity and ambiguity in DGD monitoring are calculated for NRZ data format as DGD to bit time (DGD/T) varies. It is shown that each of the control signals mentioned has both positive and negative features for efficient DGD monitoring. Therefore, in order to evaluate features concurrently and increase reliability, we employ data fusion to fuse features of each control signal, which makes evaluating and predicting the performance of control signals possible, before applying them in a real PMD compensation system. Finally, the reliability of the results obtained from data fusion is tested in a typical PMD compensator

Interactive Whole-Heart Segmentation in Congenital Heart Disease

We present an interactive algorithm to segment the heart chambers and epicardial surfaces, including the great vessel walls, in pediatric cardiac MRI of congenital heart disease. Accurate whole-heart segmentation is necessary to create patient-specific 3D heart models for surgical planning in the presence of complex heart defects. Anatomical variability due to congenital defects precludes fully automatic atlas-based segmentation. Our interactive segmentation method exploits expert segmentations of a small set of short-axis slice regions to automatically delineate the remaining volume using patch-based segmentation. We also investigate the potential of active learning to automatically solicit user input in areas where segmentation error is likely to be high. Validation is performed on four subjects with double outlet right ventricle, a severe congenital heart defect. We show that strategies asking the user to manually segment regions of interest within short-axis slices yield higher accuracy with less user input than those querying entire short-axis sliceNatural Sciences and Engineering Research Council of Canada (Alexander Graham Bell Canada Graduate Scholarships-Doctoral Program (CGS D))Wistron CorporationNational Institute for Biomedical Imaging and Bioengineering (U.S.) (NAMIC U54-EB005149)Boston Children's Hospital (Translational Research Program Fellowship)Boston Children's Hospital. Office of Faculty DevelopmentHarvard Catalys