The effect of mountain bike suspensions on vibrations and off-road uphill performance.

AIM: This study evaluates the effect of front suspension (FS) and dual suspension (DS) mountain-bike on performance and vibrations during off-road uphill riding. METHODS: Thirteen male cyclists (27+/-5 years, 70+/-6 kg, VO(2max)59+/-6 mL.kg(-1).min(-1), mean+/-SD) performed, in a random sequence, at their lactate threshold, an off-road uphill course (1.69 km, 212 m elevation gain) with both type of bicycles. Variable measured: a) VO(2) consumption (K4b2 analyzer, Cosmed), b) power output (SRM) c) gain in altitude and d) 3-D accelerations under the saddle and at the wheel (Physilog, EPFL, Switzerland). Power spectral analy- sis (Fourier) was performed from the vertical acceleration data. RESULTS: Respectively for the FS and DS mountain bike: speed amounted to 7.5+/-0.7 km.h(-1) and 7.4+/-0.8 km.h(-1), (NS), energy expenditure 1.39+/-0.16 kW and 1.38+/-0.18, (NS), gross efficiency 0.161+/-0.013 and 0.159+/-0.013, (NS), peak frequency of vibration under the saddle 4.78+/-2.85 Hz and 2.27+/-0.2 Hz (P<0.01) and median-frequency of vertical displacements of the saddle 9.41+/-1.47 Hz and 5.78+/-2.27 Hz (P<0.01). CONCLUSION: Vibrations at the saddle level of the DS bike are of low frequencies whereas those of the FS bike are mostly of high frequencies. In the DS bike, the torque produced by the cyclist at the pedal level may generate low frequency vibrations. We conclude that the DS bike absorbs more high frequency vibrations, is more comfortable and performs as well as the FS bicycle.

A review of atlas-based segmentation for magnetic resonance brain images.

Normal and abnormal brains can be segmented by registering the target image with an atlas. Here, an atlas is defined as the combination of an intensity image (template) and its segmented image (the atlas labels). After registering the atlas template and the target image, the atlas labels are propagated to the target image. We define this process as atlas-based segmentation. In recent years, researchers have investigated registration algorithms to match atlases to query subjects and also strategies for atlas construction. In this paper we present a review of the automated approaches for atlas-based segmentation of magnetic resonance brain images. We aim to point out the strengths and weaknesses of atlas-based methods and suggest new research directions. We use two different criteria to present the methods. First, we refer to the algorithms according to their atlas-based strategy: label propagation, multi-atlas methods, and probabilistic techniques. Subsequently, we classify the methods according to their medical target: the brain and its internal structures, tissue segmentation in healthy subjects, tissue segmentation in fetus, neonates and elderly subjects, and segmentation of damaged brains. A quantitative comparison of the results reported in the literature is also presented.

Validation of tissue modelization and classification techniques in T1-weighted MR brain images

We propose a deep study on tissue modelization andclassification Techniques on T1-weighted MR images. Threeapproaches have been taken into account to perform thisvalidation study. Two of them are based on FiniteGaussian Mixture (FGM) model. The first one consists onlyin pure gaussian distributions (FGM-EM). The second oneuses a different model for partial volume (PV) (FGM-GA).The third one is based on a Hidden Markov Random Field(HMRF) model. All methods have been tested on a DigitalBrain Phantom image considered as the ground truth. Noiseand intensity non-uniformities have been added tosimulate real image conditions. Also the effect of ananisotropic filter is considered. Results demonstratethat methods relying in both intensity and spatialinformation are in general more robust to noise andinhomogeneities. However, in some cases there is nosignificant differences between all presented methods.

Molecular investigation of lymph nodes in colon cancer patients using one-step nucleic acid amplification (OSNA): a new road to better staging?

BACKGROUND: A new diagnostic system, called one-step nucleic acid amplification (OSNA), has recently been designed to detect cytokeratin 19 mRNA as a surrogate for lymph node metastases. The objective of this prospective investigation was to compare the performance of OSNA with both standard hematoxylin and eosin (H&E) analysis and intensive histopathology in the detection of colon cancer lymph node metastases. METHODS: In total, 313 lymph nodes from 22 consecutive patients with stage I, II, and III colon cancer were assessed. Half of each lymph node was analyzed initially by H&E followed by an intensive histologic workup (5 levels of H&E and immunohistochemistry analyses, the gold standard for the assessment of sensitivity/specificity of OSNA), and the other half was analyzed using OSNA. RESULTS: OSNA was more sensitive in detecting small lymph node tumor infiltrates compared with H&E (11 results were OSNA positive/H&E negative). Compared with intensive histopathology, OSNA had 94.5% sensitivity, 97.6% specificity, and a concordance rate of 97.1%. OSNA resulted in an upstaging of 2 of 13 patients (15.3%) with lymph node-negative colon cancer after standard H&E examination. CONCLUSIONS: OSNA appeared to be a powerful and promising molecular tool for the detection of lymph node metastases in patients with colon cancer. OSNA had similar performance in the detection of lymph node metastases compared with intensive histopathologic investigations and appeared to be superior to standard histology with H&E. Most important, the authors concluded that OSNA may lead to a potential upstaging of >15% of patients with colon cancer.

The brain tracks the energetic value in food images.

Do our brains implicitly track the energetic content of the foods we see? Using electrical neuroimaging of visual evoked potentials (VEPs) we show that the human brain can rapidly discern food's energetic value, vis à vis its fat content, solely from its visual presentation. Responses to images of high-energy and low-energy food differed over two distinct time periods. The first period, starting at approximately 165 ms post-stimulus onset, followed from modulations in VEP topography and by extension in the configuration of the underlying brain network. Statistical comparison of source estimations identified differences distributed across a wide network including both posterior occipital regions and temporo-parietal cortices typically associated with object processing, and also inferior frontal cortices typically associated with decision-making. During a successive processing stage (starting at approximately 300 ms), responses differed both topographically and in terms of strength, with source estimations differing predominantly within prefrontal cortical regions implicated in reward assessment and decision-making. These effects occur orthogonally to the task that is actually being performed and suggest that reward properties such as a food's energetic content are treated rapidly and in parallel by a distributed network of brain regions involved in object categorization, reward assessment, and decision-making.

Impact of the Advisa MRI pacing system on the diagnostic quality of cardiac MR images and contraction patterns of cardiac muscle during scans: Advisa MRI randomized clinical multicenter study results.

BACKGROUND: The Advisa MRI system is designed to safely undergo magnetic resonance imaging (MRI). Its influence on image quality is not well known. OBJECTIVE: To evaluate cardiac magnetic resonance (CMR) image quality and to characterize myocardial contraction patterns by using the Advisa MRI system. METHODS: In this international trial with 35 participating centers, an Advisa MRI system was implanted in 263 patients. Of those, 177 were randomized to the MRI group and 150 underwent MRI scans at the 9-12-week visit. Left ventricular (LV) and right ventricular (RV) cine long-axis steady-state free precession MR images were graded for quality. Signal loss along the implantable pulse generator and leads was measured. The tagging CMR data quality was assessed as the percentage of trackable tagging points on complementary spatial modulation of magnetization acquisitions (n=16) and segmental circumferential fiber shortening was quantified. RESULTS: Of all cine long-axis steady-state free precession acquisitions, 95% of LV and 98% of RV acquisitions were of diagnostic quality, with 84% and 93%, respectively, being of good or excellent quality. Tagging points were trackable from systole into early diastole (360-648 ms after the R-wave) in all segments. During RV pacing, tagging demonstrated a dyssynchronous contraction pattern, which was not observed in nonpaced (n = 4) and right atrial-paced (n = 8) patients. CONCLUSIONS: In the Advisa MRI study, high-quality CMR images for the assessment of cardiac anatomy and function were obtained in most patients with an implantable pacing system. In addition, this study demonstrated the feasibility of acquiring tagging data to study the LV function during pacing.

One-step nucleic acid amplification (OSNA): a new road to better staging in colon cancer patients?

Introduction: Approximately one fifth of stage I and II colon cancer patients will suffer from recurrent disease. This is partly due to the presence of small nodal tumour infiltrates, which are undetected by standard histopathology using Haematoxylin & Eosin (H&E) staining on one slice and thus may not receive beneficial adjuvant therapy. A new diagnostic, semi-automatic system, called one-step nucleic acid amplification (OSNA), was recently designed for the detection of cytokeratin 19 (CK19) mRNA as a surrogate for lymph node metastases. The objective of the present investigation was to compare the performance of OSNA with both standard H&E as well as intensive histopathologic analyses in the detection of colon cancer lymph node micro- and macro-metastases.Methods: In this prospective study 313 lymph nodes from 22 consecutive stage I - III colon cancer patients were assessed. Half of each lymph node was analysed initially based on one slice of H&E followed by an intensive histologic work-up (5 levels of H&E and immuno-histochemistry staining for each slice), the other half was analysed using OSNA.Results: All OSNA results were available after less than 40 minutes. Fifty-one lymph nodes were positive and 246 lymph nodes negative with both OSNA and standard H&E. OSNA was more sensitive to detect small nodal tumor infiltrates compared to H&E (11 OSNA pos. /H&E neg.). Compared to intensive histopathologic analyses, OSNA had a sensitivity of 94.5% and a specificity of 97.6% to detect lymph node micro- and macro-metastases with a concordance rate of 97.1%. An upstaging due to OSNA was found in 2/13 (15.3%) initially node negative colon cancer patients.Conclusion: OSNA appears to be a powerful and promising molecular tool for the detection of lymph node macro- and micro-metastases in colon cancer patients. OSNA has a similar performance in the detection of micro- and macro-metastases compared to intensive histopathologic investigations and appears to be superior to standard histology with H&E. Since the use of OSNA allows the analysis of the whole lymph node, the problem of sampling bias and undetected tumor deposits due to uninvestigated material will be overcome in the future and OSNA may thus improve staging in colon cancer patients. It is hoped that this improved staging will lead to better patient selection for adjuvant therapy and consecutively improved local and distant control as well as better overall survival.

Effects of computing parameters and measurement locations on the estimation of 3D NPS in non-stationary MDCT images.

The goal of this study was to investigate the impact of computing parameters and the location of volumes of interest (VOI) on the calculation of 3D noise power spectrum (NPS) in order to determine an optimal set of computing parameters and propose a robust method for evaluating the noise properties of imaging systems. Noise stationarity in noise volumes acquired with a water phantom on a 128-MDCT and a 320-MDCT scanner were analyzed in the spatial domain in order to define locally stationary VOIs. The influence of the computing parameters in the 3D NPS measurement: the sampling distances bx,y,z and the VOI lengths Lx,y,z, the number of VOIs NVOI and the structured noise were investigated to minimize measurement errors. The effect of the VOI locations on the NPS was also investigated. Results showed that the noise (standard deviation) varies more in the r-direction (phantom radius) than z-direction plane. A 25 × 25 × 40 mm(3) VOI associated with DFOV = 200 mm (Lx,y,z = 64, bx,y = 0.391 mm with 512 × 512 matrix) and a first-order detrending method to reduce structured noise led to an accurate NPS estimation. NPS estimated from off centered small VOIs had a directional dependency contrary to NPS obtained from large VOIs located in the center of the volume or from small VOIs located on a concentric circle. This showed that the VOI size and location play a major role in the determination of NPS when images are not stationary. This study emphasizes the need for consistent measurement methods to assess and compare image quality in CT.

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

Mixed spectral-structural classification of very high resolution images with summation kernels

In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.

Qualitative grading of severity of lumbar spinal stenosis based on the morphology of the dural sac on magnetic resonance images.

STUDY DESIGN.: Retrospective radiologic study on a prospective patient cohort. OBJECTIVE.: To devise a qualitative grading of lumbar spinal stenosis (LSS), study its reliability and clinical relevance. SUMMARY OF BACKGROUND DATA.: Radiologic stenosis is assessed commonly by measuring dural sac cross-sectional area (DSCA). Great variation is observed though in surfaces recorded between symptomatic and asymptomatic individuals. METHODS.: We describe a 7-grade classification based on the morphology of the dural sac as observed on T2 axial magnetic resonance images based on the rootlet/cerebrospinal fluid ratio. Grades A and B show cerebrospinal fluid presence while grades C and D show none at all. The grading was applied to magnetic resonance images of 95 subjects divided in 3 groups as follows: 37 symptomatic LSS surgically treated patients; 31 symptomatic LSS conservatively treated patients (average follow-up, 2.5 and 3.1 years); and 27 low back pain (LBP) sufferers. DSCA was also digitally measured. We studied intra- and interobserver reliability, distribution of grades, relation between morphologic grading and DSCA, as well relation between grades, DSCA, and Oswestry Disability Index. RESULTS.: Average intra- and interobserver agreement was substantial and moderate, respectively (k = 0.65 and 0.44), whereas they were substantial for physicians working in the study originating unit. Surgical patients had the smallest DSCA. A larger proportion of C and D grades was observed in the surgical group. Surface measurementsresulted in overdiagnosis of stenosis in 35 patients and under diagnosis in 12. No relation could be found between stenosis grade or DSCA and baseline Oswestry Disability Index or surgical result. C and D grade patients were more likely to fail conservative treatment, whereas grades A and B were less likely to warrant surgery. CONCLUSION.: The grading defines stenosis in different subjects than surface measurements alone. Since it mainly considers impingement of neural tissue it might be a more appropriate clinical and research tool as well as carrying a prognostic value.