Steady state free precession magnetization transfer imaging

The formerly proposed concept for magnetization transfer imaging (MTI) using balanced steady-state free precession (SSFP) image acquisitions is in this work extended to nonbalanced protocols. This allows SSFP-based MTI of targets with high susceptibility variation (such as the musculoskeletal system), or at ultra-high magnetic fields (where balanced SSFP suffers from considerable off-resonance related image degradations). In the first part, SSFP-based MTI in human brain is analyzed based on magnetization transfer ratio (MTR) histograms. High correlations are observed among all different SSFP MTI protocols and thereby ensure proper conceptual extension to nonbalanced SSFP. The second part demonstrates SSFP-based MTI allowing fast acquisition of high resolution volumetric MTR data from human brain and cartilage at low (1.5T) to ultra-high (7.0T) magnetic fields.

Comparison of delayed gadolinium enhanced MRI of cartilage (dGEMRIC) using inversion recovery and fast T1 mapping sequences

The delayed Gadolinium Enhanced MRI of Cartilage (dGEMRIC) technique has shown promising results in pilot clinical studies of early osteoarthritis. Currently, its broader acceptance is limited by the long scan time and the need for postprocessing to calculate the T1 maps. A fast T1 mapping imaging technique based on two spoiled gradient echo images was implemented. In phantom studies, an appropriate flip angle combination optimized for center T1 of 756 to 955 ms yielded excellent agreement with T1 measured using the inversion recovery technique in the range of 200 to 900 ms, of interest in normal and diseased cartilage. In vivo validation was performed by serially imaging 26 hips using the inversion recovery and the Fast 2 angle T1 mapping techniques (center T1 756 ms). Excellent correlation with Pearson correlation coefficient R2 of 0.74 was seen and Bland-Altman plots demonstrated no systematic bias.

Hermitian curve interpolation for CT-data re-sampling

Purpose: Development of an interpolation algorithm for re‐sampling spatially distributed CT‐data with the following features: global and local integral conservation, avoidance of negative interpolation values for positively defined datasets and the ability to control re‐sampling artifacts. Method and Materials: The interpolation can be separated into two steps: first, the discrete CT‐data has to be continuously distributed by an analytic function considering the boundary conditions. Generally, this function is determined by piecewise interpolation. Instead of using linear or high order polynomialinterpolations, which do not fulfill all the above mentioned features, a special form of Hermitian curve interpolation is used to solve the interpolation problem with respect to the required boundary conditions. A single parameter is determined, by which the behavior of the interpolation function is controlled. Second, the interpolated data have to be re‐distributed with respect to the requested grid. Results: The new algorithm was compared with commonly used interpolation functions based on linear and second order polynomial. It is demonstrated that these interpolation functions may over‐ or underestimate the source data by about 10%–20% while the parameter of the new algorithm can be adjusted in order to significantly reduce these interpolation errors. Finally, the performance and accuracy of the algorithm was tested by re‐gridding a series of X‐ray CT‐images. Conclusion: Inaccurate sampling values may occur due to the lack of integral conservation. Re‐sampling algorithms using high order polynomialinterpolation functions may result in significant artifacts of the re‐sampled data. Such artifacts can be avoided by using the new algorithm based on Hermitian curve interpolation

Ultrasonography for depiction of brachial plexus injury

Recent development of ultrasonographic equipment has allowed improved spatial resolution for visualizing normal and pathologic conditions of peripheral nerves. Regarding the brachial plexus, only ultrasonographic studies that have described the normal appearance have been reported. To the best of our knowledge, no case report regarding the ultrasonographic description of a brachial plexus lesion has been published. We report the ultrasonographic findings of a brachial plexus injury after extirpation of a suspected enlarged supraclavicular lymph node.

On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

OBJECTIVES: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. METHODS: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. RESULTS: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. CONCLUSION: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.

Feasibility of T2* mapping for the evaluation of hip joint cartilage at 1.5T using a three-dimensional (3D), gradient-echo (GRE) sequence: a prospective study

This study defines the feasibility of utilizing three-dimensional (3D) gradient-echo (GRE) MRI at 1.5T for T(2)* mapping to assess hip joint cartilage degenerative changes using standard morphological MR grading while comparing it to delayed gadolinium-enhanced MRI of cartilage (dGEMRIC). MRI was obtained from 10 asymptomatic young adult volunteers and 33 patients with symptomatic femoroacetabular impingement (FAI). The protocol included T(2)* mapping without gadolinium-enhancement utilizing a 3D-GRE sequence with six echoes, and after gadolinium injection, routine hip sequences, and a dual-flip-angle 3D-GRE sequence for dGEMRIC T(1) mapping. Cartilage was classified as normal, with mild changes, or with severe degenerative changes based on morphological MRI. T(1) and T(2)* findings were subsequently correlated. There were significant differences between volunteers and patients in normally-rated cartilage only for T(1) values. Both T(1) and T(2)* values decreased significantly with the various grades of cartilage damage. There was a statistically significant correlation between standard MRI and T(2)* (T(1)) (P < 0.05). High intraclass correlation was noted for both T(1) and T(2)*. Correlation factor was 0.860 to 0.954 (T(2)*-T(1) intraobserver) and 0.826 to 0.867 (T(2)*-T(1) interobserver). It is feasible to gather further information about cartilage status within the hip joint using GRE T(2)* mapping at 1.5T.

Multivariate modelling of responses to conditional items: New possibilities for latent class analysis

Questionnaire data may contain missing values because certain questions do not apply to all respondents. For instance, questions addressing particular attributes of a symptom, such as frequency, triggers or seasonality, are only applicable to those who have experienced the symptom, while for those who have not, responses to these items will be missing. This missing information does not fall into the category 'missing by design', rather the features of interest do not exist and cannot be measured regardless of survey design. Analysis of responses to such conditional items is therefore typically restricted to the subpopulation in which they apply. This article is concerned with joint multivariate modelling of responses to both unconditional and conditional items without restricting the analysis to this subpopulation. Such an approach is of interest when the distributions of both types of responses are thought to be determined by common parameters affecting the whole population. By integrating the conditional item structure into the model, inference can be based both on unconditional data from the entire population and on conditional data from subjects for whom they exist. This approach opens new possibilities for multivariate analysis of such data. We apply this approach to latent class modelling and provide an example using data on respiratory symptoms (wheeze and cough) in children. Conditional data structures such as that considered here are common in medical research settings and, although our focus is on latent class models, the approach can be applied to other multivariate models.

Rapid estimation of cartilage T2 based on double echo at steady state (DESS) with 3 Tesla

The double-echo-steady-state (DESS) sequence generates two signal echoes that are characterized by a different contrast behavior. Based on these two contrasts, the underlying T2 can be calculated. For a flip-angle of 90 degrees , the calculated T2 becomes independent of T1, but with very low signal-to-noise ratio. In the present study, the estimation of cartilage T2, based on DESS with a reduced flip-angle, was investigated, with the goal of optimizing SNR, and simultaneously minimizing the error in T2. This approach was validated in phantoms and on volunteers. T2 estimations based on DESS at different flip-angles were compared with standard multiecho, spin-echo T2. Furthermore, DESS-T2 estimations were used in a volunteer and in an initial study on patients after cartilage repair of the knee. A flip-angle of 33 degrees was the best compromise for the combination of DESS-T2 mapping and morphological imaging. For this flip angle, the Pearson correlation was 0.993 in the phantom study (approximately 20% relative difference between SE-T2 and DESS-T2); and varied between 0.429 and 0.514 in the volunteer study. Measurements in patients showed comparable results for both techniques with regard to zonal assessment. This DESS-T2 approach represents an opportunity to combine morphological and quantitative cartilage MRI in a rapid one-step examination.