Nine hole peg test and transcranial magnetic stimulation: useful to evaluate dexterity of the hand and disease progression in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with involvement of the upper and lower motor neurons. Since the loss of fine motor skills is one of the earliest signs of ALS, the hypothesis was tested if the nine hole PEG test (NHPT) and transcranial magnet stimulation (TMS) with resting-motor threshold (RMT) could be useful in monitoring disease progression. Methods. We examined 28 ALS patients and 27 age-matched healthy controls. ALS patients and healthy controls underwent the nine hole peg test (NHPT) and TMS with RMT. Measurements in patients were repeated after three and six months. Results. At baseline, the median NHPT durations were 1,4-fold longer (p < 0.001), and TMS scores showed a significant 0.8-fold smaller score in ALS patients compared with healthy controls (p < 0.001). The comparison of three and six months versus baseline revealed significant differences for NHPT durations and ALSFRS-R in patients, whereas TMS scores did not significantly differ in the patients. Conclusion. NHPT seems to be a good tool to evaluate dexterity of the hand and the progression of the disease in ALS patients. TMS RMT to the hand muscles seems to be poorly qualified to evaluate the dexterity of the hand function and the course of the disease

Suction against resistance: a new breathing technique to significantly improve the blood flow ratio of the superior and inferior vena cava

Objectives: Optimal contrast within the pulmonary artery is achieved by the maximum amount of contrast-enhanced blood flowing through the superior vena cava (SVC), while minimum amounts of non-contrasted blood should originate from the inferior vena cava (IVC). This study aims to clarify whether "suction against resistance” might optimise this ratio. Methods: Phase-contrast pulse sequences on a 1.5T MRI magnet were used for flow quantification (mean flow (mL/s), stroke volume (Vol) in the SVC and IVC in volunteers. Different breathing manoeuvers were analysed repeatedly: free breathing; inspiration; expiration; suction against resistance, and Valsalva. To standardise breathing commands, volunteers performed suction and Valsalva manoeuvers with an MR-compatible manometer. Results: Suction against resistance was associated with a significant drop of the IVC/SVC flow quotient (1.63 [range 1.3-2.0] p  0.05). Conclusions: Suction against resistance caused a significant drop in the IVC/SVC quotient. Theoretically, this breathing manoeuver might significantly improve the enhancement characteristics of CT angiography. Key Points: • Suction provokes reduction in blood flow in the inferior vena cava. • Ratio between the inferior and superior vena cava blood flow diminished during suction. • Manometer used during breathing standardises MR phase-contrast blood flow measurements

Sex and age dependencies of aqueductal cerebrospinal fluid dynamics parameters in healthy subjects

Objectives: To assess the influence of age and sex on 10 cerebrospinal fluid (CSF) flow dynamics parameters measured with an MR phase contrast (PC) sequence within the cerebral aqueduct at the level of the intercollicular sulcus.Materials and Methods: 128 healthy subjects (66 female subjects with a mean age of 52.9 years and 62 male subjects with a mean age of 51.8 years) with a normal Evans index, normal medial temporal atrophy (MTA) score, and without known disorders of the CSF circulation were included in the study. A PC MR sequence on a 3T MR scanner was used. Ten different flow parameters were analyzed using postprocessing software. Ordinal and linear regression models were calculated.Results: The parameters stroke volume (sex: p < 0.001, age: p = 0.003), forward flow volume (sex: p < 0.001, age: p = 0.002), backward flow volume (sex: p < 0.001, age: p = 0.018), absolute stroke volume (sex: p < 0.001, age: p = 0.005), mean flux (sex: p < 0.001, age: p = 0.001), peak velocity (sex: p = 0.009, age: p = 0.0016), and peak pressure gradient (sex: p = 0.029, age: p = 0.028) are significantly influenced by sex and age. The parameters regurgitant fraction, stroke distance, and mean velocity are not significantly influenced by sex and age.Conclusion: CSF flow dynamics parameters measured in the cerebral aqueduct are partly age and sex dependent. For establishment of reliable reference values for clinical use in future studies, the impact of sex and age should be considered and incorporated

Amide Proton Transfer Contrast Distribution in Different Brain Regions in Young Healthy Subjects

ObjectivesTo define normal signal intensity values of amide proton transfer-weighted (APTw) magnetic resonance (MR) imaging in different brain regions.Materials and MethodsTwenty healthy subjects (9 females, mean age 29 years, range 19 – 37 years) underwent MR imaging at 3 Tesla. 3D APTw (RF saturation B1,rms = 2 μT, duration 2 s, 100% duty cycle) and 2D T2-weighted turbo spin echo (TSE) images were acquired. Postprocessing (image fusion, ROI measurements of APTw intensity values in 22 different brain regions) was performed and controlled by two independent neuroradiologists. Values were measured separately for each brain hemisphere. A subject was scanned both in prone and supine position to investigate differences between hemispheres. A mixed model on a 5% significance level was used to assess the effect of gender, brain region and side on APTw intensity values.ResultsMean APTw intensity values in the hippocampus and amygdala varied between 1.13 and 1.57%, in the deep subcortical nuclei (putamen, globus pallidus, head of caudate nucleus, thalamus, red nucleus, substantia nigra) between 0.73 and 1.84%, in the frontal, occipital and parietal cortex between 0.56 and 1.03%; in the insular cortex between 1.11 and 1.15%, in the temporal cortex between 1.22 and 1.37%, in the frontal, occipital and parietal white matter between 0.32 and 0.54% and in the temporal white matter between 0.83 and 0.89%. APTw intensity values were significantly impacted both by brain region (p &lt; 0.001) and by side (p &lt; 0.001), whereby overall values on the left side were higher than on the right side (1.13 vs. 0.9%). Gender did not significantly impact APTw intensity values (p = 0.24). APTw intensity values between the left and the right side were partially reversed after changing the position of one subject from supine to prone.ConclusionWe determined normal baseline APTw intensity values in different anatomical localizations in healthy subjects. APTw intensity values differed both between anatomical regions and between left and right brain hemisphere

Image denoising substantially improves accuracy and precision of intravoxel incoherent motion parameter estimates

<div><p>Applicability of intravoxel incoherent motion (IVIM) imaging in the clinical setting is hampered by the limited reliability in particular of the perfusion-related parameter estimates. To alleviate this problem, various advanced postprocessing methods have been introduced. However, the underlying algorithms are not readily available and generally suffer from an increased computational burden. Contrary, several computationally fast image denoising methods have recently been proposed which are accessible online and may improve reliability of IVIM parameter estimates. The objective of the present work is to investigate the impact of image denoising on accuracy and precision of IVIM parameter estimates using comprehensive in-silico and in-vivo experiments. Image denoising is performed with four different algorithms that work on magnitude data: two algorithms which are based on nonlocal means (NLM) filtering, one algorithm that relies on local principal component analysis (LPCA) of the diffusion-weighted images, and another algorithms that exploits joint rank and edge constraints (JREC). Accuracy and precision of IVIM parameter estimates is investigated in an in-silico brain phantom and an in-vivo ground truth as a function of the signal-to-noise ratio for spatially homogenous and inhomogenous levels of Rician noise. Moreover, precision is evaluated using bootstrap analysis of in-vivo measurements. In the experiments, IVIM parameters are computed a) by using a segmented fit method and b) by performing a biexponential fit of the entire attenuation curve based on nonlinear least squares estimates. Irrespective of the fit method, the results demonstrate that reliability of IVIM parameter estimates is substantially improved by image denoising. The experiments show that the LPCA and the JREC algorithms perform in a similar manner and outperform the NLM-related methods. Relative to noisy data, accuracy of the IVIM parameters in the in-silico phantom improves after image denoising by 76–79%, 79–81%, 84–99% and precision by 74–80%, 80–83%, 84–95% for the perfusion fraction, the diffusion coefficient, and the pseudodiffusion coefficient, respectively, when the segmented fit method is used. Beyond that, the simulations reveal that denoising performance is not impeded by spatially inhomogeneous levels of Rician noise in the image. Since all investigated algorithms are freely available and work on magnitude data they can be readily applied in the clinical setting which may foster transition of IVIM imaging into clinical practice.</p></div

CVs of the perfusion fraction f (first column), the diffusion coefficient D (second column), and the pseudodiffusion coefficient D* (third column) in the in-vivo ground truth as a function of the SNR when spatially homogeneous Rician noise is added and a segmented fit is performed.

<p>Please note that the CVs of the pseudodiffusion coefficient (third column) are displayed on a logarithmic scale for clarity.</p

RMSEs of the perfusion fraction f (first column), the diffusion coefficient D (second column), and the pseudodiffusion coefficient D* (third column) in the in-silico brain phantom as a function of the SNR.

<p>Results of the simulations when (a) stationary and (b) spatially varying Rician noise is added and the segmented fit method is utilized, and (c) when stationary Rician noise is added and a full biexponential fit is performed. Please note that the RMSEs of the pseudodiffusion coefficient (third column) are displayed on a logarithmic scale for clarity. At each SNR, the RMSEs were calculated as the average across all simulations (n = 50).</p

Parameter maps of the perfusion fraction f (first row), the diffusion coefficient D (second row), and the pseudodiffusion coefficient D∗ (third row) computed from noisy (first column) and denoised in-vivo DW images using the NLM (second column), the ANLM (third column), the JREC (fourth column), and the LPCA algorithms (fifth column).

<p>The results were computed from a single measurement (NSA = 1, scan duration = 72 s).</p

Parameter maps of the perfusion fraction f (first row), the diffusion coefficient D (second row), and the pseudodiffusion coefficient D* (third row) in the in-silico brain phantom computed from noisy (first column) and denoised DW images using the NLM (second column), the ANLM (third column), the JREC (fourth column), and the LPCA algorithms (fifth column).

<p>Stationary homogeneous Rician noise was added to obtain a SNR of 60 and the IVIM parameters were computed using a full biexponential fit. The gold standard is depicted for reference.</p