Готові списки джерел за темами / Spine Measurement

Добірка наукової літератури з теми "Spine Measurement"

Автор: Grafiati
Опубліковано: 4 червня 2021
Оновлено: 18 лютого 2022

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Статті в журналах з теми "Spine Measurement":

1

Mellin, Guy. "Lumbar Spine Measurement." Physiotherapy 78, no. 3 (March 1992): 201. http://dx.doi.org/10.1016/s0031-9406(10)61398-3.

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2

Okabe, Shigeo. "Recent advances in computational methods for measurement of dendritic spines imaged by light microscopy." Microscopy 69, no. 4 (April 3, 2020): 196–213. http://dx.doi.org/10.1093/jmicro/dfaa016.

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Abstract Dendritic spines are small protrusions that receive most of the excitatory inputs to the pyramidal neurons in the neocortex and the hippocampus. Excitatory neural circuits in the neocortex and hippocampus are important for experience-dependent changes in brain functions, including postnatal sensory refinement and memory formation. Several lines of evidence indicate that synaptic efficacy is correlated with spine size and structure. Hence, precise and accurate measurement of spine morphology is important for evaluation of neural circuit function and plasticity. Recent advances in light microscopy and image analysis techniques have opened the way toward a full description of spine nanostructure. In addition, large datasets of spine nanostructure can be effectively analyzed using machine learning techniques and other mathematical approaches, and recent advances in super-resolution imaging allow researchers to analyze spine structure at an unprecedented level of precision. This review summarizes computational methods that can effectively identify, segment and quantitate dendritic spines in either 2D or 3D imaging. Nanoscale analysis of spine structure and dynamics, combined with new mathematical approaches, will facilitate our understanding of spine functions in physiological and pathological conditions.
3

Merrill, Robert K., Jun S. Kim, Dante M. Leven, Joshua J. Meaike, Joung Heon Kim, and Samuel K. Cho. "A Preliminary Algorithm Using Spine Measurement Software to Predict Sagittal Alignment Following Pedicle Subtraction Osteotomy." Global Spine Journal 7, no. 6 (April 11, 2017): 543–51. http://dx.doi.org/10.1177/2192568217700098.

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Study Design: Retrospective case series. Objective: To evaluate if spine measurement software can simulate sagittal alignment following pedicle subtraction osteotomy (PSO). Methods: We retrospectively reviewed consecutive adult spinal deformity patients who underwent lumbar PSO. Sagittal measurements were performed on preoperative lateral, standing radiographs. Sagittal measurements after simulated PSO were compared to actual postoperative measurements. A regression equation was developed using cases 1-7 to determine the amount of manual rotation required of each film to match the simulated sagittal vertical axis (SVA) to the actual postoperative SVA. The equation was then applied to cases 8-13. Results: For all 13 cases, the spine software accurately simulated lumbar lordosis, pelvic incidence lumbar lordosis mismatch, and T1 pelvic angle, with no significant differences between actual and simulated measurements. The pelvic tilt (PT), sacral slope (SS), thoracolumbar alignment (TL), thoracic kyphosis (TK), T9 spino-pelvic inclination (T9SPi), T1 spino-pelvic inclination (T1SPi), and SVA were inaccurately simulated. The PT, SS, T9SPi, T1SPi, and SVA all change with manual rotation of the film, and by using the regression equation developed with cases 1-7, we were able to improve the accuracy and decrease the variability of the simulated PT, SS, T9SPi, T1SPi, and SVA for cases 8-13. Conclusions: Dedicated spine measurement software can accurately simulate certain sagittal measurements, such as LL, PI-LL, and TPA, following PSO. A number of measurements, including PT, SS, TL, TK, T9SPi, T1SPi, and SVA were inaccurately simulated. Our preliminary algorithm improved the accuracy and decreased the variability of certain measurements, but requires future prospective studies for further validation.
4

Tatavarty, Vedakumar, Sulagna Das, and Ji Yu. "Polarization of actin cytoskeleton is reduced in dendritic protrusions during early spine development in hippocampal neuron." Molecular Biology of the Cell 23, no. 16 (August 15, 2012): 3167–77. http://dx.doi.org/10.1091/mbc.e12-02-0165.

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Dendritic spines are small protrusions that receive synaptic signals in neuronal networks. The actin cytoskeleton plays a key role in regulating spine morphogenesis, as well as in the function of synapses. Here we report the first quantitative measurement of F-actin retrograde flow rate in dendritic filopodia, the precursor of dendritic spines, and in newly formed spines, using a technique based on photoactivation localization microscopy. We found a fast F-actin retrograde flow in the dendritic filopodia but not in the spine necks. The quantification of F-actin flow rates, combined with fluorescence recovery after photobleaching measurements, allowed for a full quantification of spatially resolved kinetic rates of actin turnover, which was not previously feasible. Furthermore we provide evidences that myosin II regulates the actin flow in dendritic filopodia and translocates from the base to the tip of the protrusion upon spine formation. Rac1 inhibition led to mislocalization of myosin II, as well as to disruption of the F-actin flow. These results provide advances in the quantitative understanding of F-actin remodeling during spine formation.
5

Koh, Ingrid Y. Y., W. Brent Lindquist, Karen Zito, Esther A. Nimchinsky, and Karel Svoboda. "An Image Analysis Algorithm for Dendritic Spines." Neural Computation 14, no. 6 (June 1, 2002): 1283–310. http://dx.doi.org/10.1162/089976602753712945.

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The structure of neuronal dendrites and their spines underlie the connectivity of neural networks. Dendrites, spines, and their dynamics are shaped by genetic programs as well as sensory experience. Dendritic structures and dynamics may therefore be important predictors of the function of neural networks. Based on new imaging approaches and increases in the speed of computation, it has become possible to acquire large sets of high-resolution optical micrographs of neuron structure at length scales small enough to resolve spines. This advance in data acquisition has not been accompanied by comparable advances in data analysis techniques; the analysis of dendritic and spine morphology is still accomplished largely manually. In addition to being extremely time intensive, manual analysis also introduces systematic and hard-to-characterize biases. We present a geometric approach for automatically detecting and quantifying the three-dimensional structure of dendritic spines from stacks of image data acquired using laser scanning microscopy. We present results on the measurement of dendritic spine length, volume, density, and shape classification for both static and timelapse images of dendrites of hippocampal pyramidal neurons. For spine length and density, the automated measurements in static images are compared with manual measurements. Comparisons are also made between automated and manual spine length measurements for a time-series data set. The algorithm performs well compared to a human analyzer, especially on time-series data. Automated analysis of dendritic spine morphology will enable objective analysis of large morphological data sets. The approaches presented here are generalizable to other aspects of neuronal morphology.
6

SARASTE, HELENA, BROSTRÖM, TOMAS APARISI, and GABRIELLA AXDORPH. "Radiographic Measurement of the Lumbar Spine." Spine 10, no. 3 (April 1985): 236–41. http://dx.doi.org/10.1097/00007632-198504000-00008.

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7

Horng, Ming-Huwi, Chan-Pang Kuok, Min-Jun Fu, Chii-Jen Lin, and Yung-Nien Sun. "Cobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network." Computational and Mathematical Methods in Medicine 2019 (February 19, 2019): 1–18. http://dx.doi.org/10.1155/2019/6357171.

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Scoliosis is a common spinal condition where the spine curves to the side and thus deforms the spine. Curvature estimation provides a powerful index to evaluate the deformation severity of scoliosis. In current clinical diagnosis, the standard curvature estimation method for assessing the curvature quantitatively is done by measuring the Cobb angle, which is the angle between two lines, drawn perpendicular to the upper endplate of the uppermost vertebra involved and the lower endplate of the lowest vertebra involved. However, manual measurement of spine curvature requires considerable time and effort, along with associated problems such as interobserver and intraobserver variations. In this article, we propose an automatic system for measuring spine curvature using the anterior-posterior (AP) view spinal X-ray images. Due to the characteristic of AP view images, we first reduced the image size and then used horizontal and vertical intensity projection histograms to define the region of interest of the spine which is then cropped for sequential processing. Next, the boundaries of the spine, the central spinal curve line, and the spine foreground are detected by using intensity and gradient information of the region of interest, and a progressive thresholding approach is then employed to detect the locations of the vertebrae. In order to reduce the influences of inconsistent intensity distribution of vertebrae in the spine AP image, we applied the deep learning convolutional neural network (CNN) approaches which include the U-Net, the Dense U-Net, and Residual U-Net, to segment the vertebrae. Finally, the segmentation results of the vertebrae are reconstructed into a complete segmented spine image, and the spine curvature is calculated based on the Cobb angle criterion. In the experiments, we showed the results for spine segmentation and spine curvature; the results were then compared to manual measurements by specialists. The segmentation results of the Residual U-Net were superior to the other two convolutional neural networks. The one-way ANOVA test also demonstrated that the three measurements including the manual records of two different physicians and our proposed measured record were not significantly different in terms of spine curvature measurement. Looking forward, the proposed system can be applied in clinical diagnosis to assist doctors for a better understanding of scoliosis severity and for clinical treatments.
8

Quint, Douglas J., Gerald F. Tuite, Joseph D. Stern, Steven E. Doran, Stephen M. Papadopoulos, John E. McGillicuddy, and Craig A. Lundquist. "Computer-assisted measurement of lumbar spine radiographs." Academic Radiology 4, no. 11 (November 1997): 742–52. http://dx.doi.org/10.1016/s1076-6332(97)80078-5.

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9

Lam, Wendy W. M., Victor Ai, Virginia Wong, Wai-man Lui, Fu-luk Chan, and Lilian Leong. "Ultrasound measurement of lumbosacral spine in children." Pediatric Neurology 30, no. 2 (February 2004): 115–21. http://dx.doi.org/10.1016/j.pediatrneurol.2003.07.002.

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10

Lee, Raymond. "Measurement of movements of the lumbar spine." Physiotherapy Theory and Practice 18, no. 4 (January 2002): 159–64. http://dx.doi.org/10.1080/09593980290058562.

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Також вас можуть зацікавити розширені добірки на тему "Spine Measurement" для конкретних типів джерел:
Статті в журналах Дисертації Книги

Дисертації з теми "Spine Measurement":

1

Hauerstock, David. "Telemetric measurement of compressive loads in the sheep lumbar spine." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=30785.

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Анотація:
The goal of this study was to develop and validate a system for the telemetric measurement of in vivo compressive intervertebral loads in the sheep, and to measure these loads in a variety of activities.
A miniature load cell and radio transmitter were implanted in the L3--L4 space of the spine. A total of four sheep were operated on; one was sacrificed five days after surgery, due to failure of the transmitter, and another was sacrificed after failing to ambulate for two weeks after surgery. The other two animals (average mass 67 kg) were kept for five weeks, during which a range of activities were performed, including standing, lying prone, walking/trotting, and jumping.
Results for a range of activities were as follows: in walking at 1.5 m/s, average maximum and minimum loads were 461 N and 256 N, respectively; in walking at 2m/s, average maximum and minimum loads were 684 N and 303 N, respectively; in standing, loads averaged 161 N; and in lying prone, loads averaged 212 N. The highest loads were recorded in jumping, where the peak load was 1290 N.
The results of this study demonstrate for the first time, to our knowledge, the magnitude of in vivo axial loads in the sheep lumbar spine. These findings have implications for the evaluation of studies which employ the sheep model to test spinal implants. As treatment methods for disc degeneration progress from the spacer and fusion approach to more sophisticated prostheses and tissue engineered disc replacements which preserve segmental mobility, such data will become even more important to the design, animal testing, and evaluation of implants.
2

Zheng, Yalin. "Automated segmentation of lumbar vertebrae for the measurement of spine kinematics." Thesis, University of Southampton, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288154.

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3

Hindle, Richard John. "Three-dimensional kinematics of the human back in the normal and pathologic spine." Thesis, Durham University, 1989. http://etheses.dur.ac.uk/6513/.

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This thesis investigated the relationship between the three-dimensional kinematics of the human back and spinal pathology. This required the development of a system capable of the in vivo measurement of spinal movement non-invasively and in three-dimensions. The opto-electronic CODA-3 Scanner proved unsatisfactory in this respect. The electro-magnetic 3SPACE Isotrak, however, was found to be an accurate and reliable system during a study of twisting in flexed postures. Available axial rotation was significantly increased in some degree of sagittal flexion suggesting that this may be a mechanism for intervertebral disc injury. At high degrees of sagittal flexion a reduction in available axial rotation was noted. In vitro tests on isolated lumbar motion segments confirmed the increase in axial rotation available in flexed postures shown in vivo, this was presumed to be due to an opening of the lumbar zygapophysial joints. Mechanical testing of lumbar interspinous and supraspinous ligaments showed them to be active only in the extremes of sagittal flexion and hence that they could be responsible for the reduction in axial rotation seen in vivo. The 3SPACE Isotrak was used in a clinical study of 80 normal and 43 pathologic subjects. In the normals ranges of motion were, in general, reduced with increasing age in both males and females although a significant increase in sagittal flexion occurred with increasing age in females. Male mobility significantly exceeded female in sagittal flexion but female tended to exceed male in extension, lateral bend and axial rotation. Opposite axial rotation occurred consistently upon lateral bend and vice versa, flexion also occurred on lateral bend but not axial rotation. There was widespread disruption to the primary and coupled movements of the back pain patients when compared to normal movement patterns but there was no clear distinction between the kinematic movement patterns of discrete patient groups. The small numbers in these patient groups warrant a further, more detailed, clinical study.
4

Harvey, Steven Brian. "Interactive computer methods for morphometric and kinematic measurement of images of the spine." Thesis, University of Aberdeen, 1999. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU116153.

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The aim of this project was to develop robust interactive computer methods for measuring the shape and movement of the lumbar spine vertebrae from lateral radiographs of the spine. In order to achieve this aim, two software packages were written - the Aberdeen Vertebral Morphometry System (AVMS) and the Aberdeen Spinal Videofluoroscopy System (ASVS). AVMS was designed to analyse static images from dual energy x-ray absorptiometry (DXA) imaging densitometers. Comparative precision tests of the ability of AVMS software and Lunar EXPERT-XL software to measure vertebral height were undertaken using four vertebrae from the same lateral spine image (male, 67 years). Two of the vertebrae in this image were abnormal and two were normal. It was concluded that AVMS had higher precision when measuring abnormal and normal vertebrae. The effects of axial rotation and lateral bending, which lead to movement out of the sagittal plane, were investigated by generating a three-dimensional computer model of two adjacent vertebrae and projecting it on to the sagittal plane. The projected model was measured as if it were a radiograph, allowing the effects of out-of-plane movement and errors in reference point placement to be calculated. ASVS was used to acquire and analyse a sequence of images of the spine in motion obtained using videofluoroscopy and incorporated the findings of the computer modelling work. A clinical study for the measurement of intervertebral motion using ASVS during flexion-extension was organised and seven subjects suffering from severe lower back pain were recruited. Analysis of the image sequences using the computerised measurement system in ASVS revealed the apparent effect of analgesia/sedative on the shape and size of centroidal trajectories of vertebrae, and the differences in trajectory shape between subjects. It was concluded that ASVS was able to quantify spinal motion at a minimal radiation dose to the subject.
5

Breen, Alan Clark. "The measurement of the kinematics of the human spine using videofluoroscopy and image processing." Thesis, University of Southampton, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303090.

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6

Darlington, Sarah Elizabeth. "Effect of intra-abdominal fat on the accuracy of DXA lumbar spine bone mineral density measurement using DXA body composition measurements." Thesis, Cardiff University, 2012. http://orca.cf.ac.uk/44881/.

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In the diagnosis of osteoporosis, dual-energy X-ray absorptiometry (DXA) is the accepted method for measuring bone mineral density (BMD) due to its good precision. However, accuracy is compromised by two assumptions: (1) the body is composed of only soft tissue and bone mineral and (2) the composition of tissue overlying bone is equal to that adjacent to bone. To diagnosis osteoporosis, BMD is compared to that of a young healthy population to calculate a T-score. BMD is normal if T-score>-1 and osteoporotic if < -2.5. The aim of this study was to use DXA whole body (WB) scans to quantify variation in abdominal fat thickness and to explore whether this information could be used to improve the accuracy of lumbar spine (LS) BMD measurement. Relevant data were extracted from archived DXA images for groups of patients who had received both LS and WB scans. LS BMD increased with the width of the associated soft tissue baseline and BMD was correlated with fat thickness within the baseline. For individuals, the bone mineral equivalence of the difference in fat thickness between a standard width baseline and a region over the spine corresponded to a maximum T-score difference of 0.6. However, the average for the groups gave a T-score difference of 0.2. The predicted inaccuracy in LS BMD measurement resulting from a non-uniform fat distribution was within 0.013 g/cm2 for groups and 0.017 g/cm2 for individuals. From these measurements, errors in BMD of up to 6% and 3% for a standard width baseline were observed for individuals and groups respectively. In the majority of patients, errors introduced by a non-uniform distribution of fat are unlikely to cause a mis-diagnosis. However, significant errors may occur in certain individuals. The clinical application of the proposed method to quantify errors in BMD requires further investigation
7

Beange, Kristen. "Validation of Wearable Sensor Performance and Placement for the Evaluation of Spine Movement Quality." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/38698.

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Inertial measurement units (IMUs) are being recognized as a portable and cost-effective alternative to motion analysis systems and have the potential to be introduced into clinical settings for the assessment of functional movement quality of the spine in patients with low back pain. However, uncertainties regarding sensor accuracy and reliability are limiting the widespread use and acceptance of IMU-based assessments into routine clinical practice. The objective of this work was to assess the performance of inexpensive wearable IMUs (Mbientlab MetaMotionR IMUs; Mbientlab Inc., San Francisco, USA; product specifications available in Appendix C) relative to conventional optical motion capture equipment (Vicon Motion Systems Ltd., Oxford, UK) in: 1) a controlled environment, and 2) an uncontrolled environment. The first study evaluated the performance of 2 IMUs in a controlled environment during simulated repetitive spine motion carried out by means of a motorized gimbal. Root mean square error (RMSE) and mean absolute measurement differences between cycle-to-cycle minimum, maximum, and range of motion values, as well as correlational analyses within IMUs and between IMUs and Vicon, in all movement directions (i.e., simulated flexion-extension (FE), lateral bending (LB), and axial twisting (AT)), were compared. Measurement error was low in all axes during all tests (i.e., ≤ 1.54°); however, low-to-moderate correlational results were found in one non-primary axis, and this axis changed depending on the direction of the movement (i.e., LB during FE-motion (0.244 ≤ R ≤ 0.515), AT during LB-motion (0.594 ≤ R ≤ 0.795), and FE during AT-motion (0.002 ≤ R ≤ 0.255)). The second study was designed to assess the performance of the IMUs in an uncontrolled environment during repetitive spine FE in human participants. Absolute angles and local dynamic stability were compared for individual IMUs (which were placed over T10-T12 spinous processes, and the pelvis) as well as for relative motion between IMUs. Maximum finite-time Lyapunov exponents (λmax) were used to quantify local dynamic stability and were calculated using both FE and the sum of squares (SS) from measured spine kinematics. It was found that the IMUs have acceptable performance in all axes when tracking motion (RMSE ≤ 2.43°); however, low-to-moderate correlational results were found in one non-primary axis (0.987 ≤ RFE ≤ 0.998; 0.746 ≤ RLB ≤ 0.978; 0.343 ≤ RAT ≤ 0.679). In addition, correlations between λmax estimates were high; therefore, local dynamic stability can be accurately estimated using both FE and SS data (0.807 ≤ 〖ICC〗_2,1^FE ≤ 0.919; 0.738 ≤ 〖ICC〗_2,1^SS ≤ 0.868). Correlation between λmax estimates was higher when using FE data for individual sensors/rigid-body marker clusters; however, correlation was higher when using SS data for relative motion. In general, the results of these studies show that the MetaMotionR IMUs have acceptable performance in all axes when considering absolute angle orientation and motion tracking, and measurement of local dynamic stability; however, there is low-to-moderate correlation in one non-primary axis, and that axis changes depending on the direction of motion. Future research will investigate how to optimize performance of the third axis for motion tracking; it will also focus on understanding the significance of the third axis performance when calculating specific outcome measures related to spine movement quality.
8

MacMillan, Erin Leigh. "Myelin water measurement by magnetic resonance imaging in the healthy human spinal cord : reproducibility and changes with age." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1887.

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Multi-echo T2 relaxation measurements of the human spinal cord (SC) reveal a short T2 pool of water believed to arise from water trapped between myelin bilayers, where the proportion of this water to the total water signal is called the myelin water fraction (MWF). In the present study, MWF were measured in the healthy human cervical spine at the C4-C6 vertebral levels in vivo using a 3D modified 32 echo CPMG sequence to acquire axial slices perpendicular to the cord. Volunteers were recruited in two age ranges, under 30 years old and over 50 years old, and a subset of both groups were scanned twice to test reproducibility. Mean MWF in the dorsal and lateral column WM of the group under 30 years of age was 0.29 (0.01) (mean(SE)), which agrees with previously reported MWF values in the cervical spine. The mean absolute difference between two scans was 0.06 or 26%. A negative correlation between WM MWF and age was hinted at in these findings, however more subjects are required to improve statistical power. This study paves the way for the use of 3D myelin water imaging in the cervical spine at 3.0T for the assessment of SC WM pathology.
9

Toosizadeh, Nima. "Time-dependent assessment of the human lumbar spine in response to flexion exposures: in vivo measurement and modeling." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/19274.

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Among several work-related injuries, low back disorders (LBDs) are the leading cause of lost workdays, and with annual treatment costs in excess of $10 billion in the US. Epidemiological evidence has indicated that prolonged and/or repetitive non-neutral postures, such as trunk flexion, are commonly associated with an increased risk of LBDs. Trunk flexion can result in viscoelastic deformations of soft tissues and subsequent mechanical and neuromuscular alterations of the trunk, and may thereby increase LBD risk. While viscoelastic behaviors of isolated spinal motion segments and muscles have been extensively investigated, in vivo viscoelastic responses of the trunk have not, particularly in response to flexion exposures. Further, most biomechanical efforts at understanding occupational LBDS have not considered the influence of flexion exposures on spine loads. Four studies were completed to characterize viscoelastic deformation of the trunk in response several flexion exposures and to develop and evaluate a computational model of the human trunk that accounts for time-dependent characteristics of soft tissues. Participants were exposed to prolonged flexion at different trunk angles and external moments, and repetitive trunk flexion with different external moments and flexion rates. Viscoelastic properties were quantified using laboratory experiments and viscoelastic models. A multi-segment model of the upper body was developed and evaluated, and then used to estimate muscle forces and spine loads during simulated lifting tasks before and after prolonged trunk flexion at a constant angle and constant external moment. Material properties from the earlier experiments were used to evaluate/calibrate the model. Experimental results indicated important effects of flexion angle, external moment, and flexion rate on trunk viscoelastic behaviors. Material properties from fitted Kelvin-solid models differed with flexion angle and external moment. Nonlinear viscoelastic behavior of the trunk tissues was evident, and predictive performance was enhanced using Kelvin-solid models with ≥2 iii retardation/relaxation time constants. Predictions using the multi-segment model suggested increases in spine loads following prolonged flexion exposures, primarily as a consequence of additional muscle activity. As a whole, these results help to characterize the effects of trunk flexion exposures on trunk biomechanics, contribute to more effective estimates of load distribution among passive and active components, enhance our understanding of LBD etiology, and may facilitate future controls/interventions.
Ph. D.
10

Russell, Patricia Anne Hartley. "Measurement of the three-dimensional kinematics of the human lumbar and cervical spine using the 3Space Isotrak system." Thesis, Durham University, 1993. http://etheses.dur.ac.uk/5650/.

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Книги з теми "Spine Measurement":

1

McKenzie, R. Tait. The accurate measurement of spinal curvatures with the description of a new instrument for the purpose. [S.l: s.n., 1985.

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2

Hirano, Teruyuki. Measurements of Spin-Orbit Angles for Transiting Systems. Tokyo: Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-54586-6.

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3

Araddad, Salah Y. Lifetime measurements of high spin states in 168Yb. Manchester: University of Manchester, 1996.

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4

Mine, Shun'ichi. Systematic measurement of the spin-polarization of the cosmic-ray muons. Tokyo, Japan: Institute for Nuclear Study, University of Tokyo, 1996.

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5

Dylla, Thorsten. Electron spin resonance and transient photocurrent measurements on microcrystalline silicon. Jülich: Forschungszentrum, Zentralbibliothek, 2005.

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6

Andersson, Robert Anders. Microstructure in powders: Spin-echo small-angle neutron scattering measurements. Amsterdam: Delft University Press/IOS Press, 2008.

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7

Andersson, Robert Anders. Microstructure in powders: Spin-echo small-angle neutron scattering measurements. Amsterdam: Delft University Press/IOS Press, 2008.

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8

Freer, Martin. Measurements of the spins of symmetrically fissioning states in [superior] [24] Mg. Birmingham: University of Birmingham, 1991.

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9

Lütz, Ralf A. Membership functions for fuzzy poverty measurement: An approach using German panel data. Frankfurt am Main: P. Lang, 1996.

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Greer, Allan J. Low magnetic fields in anisotropic superconductors. Heidelberg, Germany: Springer, 1995.

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Частини книг з теми "Spine Measurement":

1

Pannu, Tejbir Singh, Virginie Lafage, and Frank J. Schwab. "Concepts of Risk Stratification in Measurement and Delivery of Quality." In Quality Spine Care, 111–29. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97990-8_8.

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2

Berger, M. "Cervicomotography: A New Method for Measurement of Cervical Spine Movement." In Updating in Headache, 69–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-88581-5_12.

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3

Carbajal, Guillermo, Álvaro Gómez, Gabor Fichtinger, and Tamas Ungi. "Portable Optically Tracked Ultrasound System for Scoliosis Measurement." In Recent Advances in Computational Methods and Clinical Applications for Spine Imaging, 37–46. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14148-0_4.

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Nøhr, Anne Krogh, Louise Pedersen Pilgaard, Bolette Dybkjær Hansen, Rasmus Nedergaard, Heidi Haavik, Rene Lindstroem, Maciej Plocharski, and Lasse Riis Østergaard. "Semi-automatic Method for Intervertebral Kinematics Measurement in the Cervical Spine." In Image Analysis, 302–13. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59129-2_26.

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5

Pang, Shumao, Stephanie Leung, Ilanit Ben Nachum, Qianjin Feng, and Shuo Li. "Direct Automated Quantitative Measurement of Spine via Cascade Amplifier Regression Network." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 940–48. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00934-2_104.

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6

Mortier, J., and L. Zichner. "Computer-Assisted Pressure Measurement in the Patellofemoral Joint with Electronic Pressure Sensors." In Navigation and Robotics in Total Joint and Spine Surgery, 204–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-59290-4_29.

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7

Sekiguchi, Hidetaka, Hideaki E. Takahashi, Yoshio Koga, Tatsuhiko Tanizawa, and Ikuko Ezawa. "Bone Volume Measurement of Lumbar Spine by DEXA in One-Bound Volleyball Players." In Spinal Disorders in Growth and Aging, 211–14. Tokyo: Springer Japan, 1995. http://dx.doi.org/10.1007/978-4-431-66939-5_19.

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8

Li, Hao, Wee Kheng Leow, Chao-Hui Huang, and Tet Sen Howe. "Modeling and Measurement of 3D Deformation of Scoliotic Spine Using 2D X-ray Images." In Computer Analysis of Images and Patterns, 647–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03767-2_79.

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9

Roche, Clare. "Cervical Spine." In Measurements in Musculoskeletal Radiology, 105–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-540-68897-6_6.

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10

Winn, Naomi, Eva Llopis, and Victor N. Cassar-Pullicino. "Thoracolumbar Spine." In Measurements in Musculoskeletal Radiology, 189–236. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-540-68897-6_7.

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Тези доповідей конференцій з теми "Spine Measurement":

1

"Spine." In 2015 31st Thermal Measurement, Modeling & Management Symposium (SEMI-THERM). IEEE, 2015. http://dx.doi.org/10.1109/semi-therm.2015.7100115.

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2

"[Spine]." In 2013 IEEE/CPMT 29th Semiconductor Thermal Measurement & Management Symposium (SemiTherm). IEEE, 2013. http://dx.doi.org/10.1109/semi-therm.2013.6526789.

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3

"Spine." In 2009 25th Annual IEEE Semiconductor Thermal Measurement and Management Symposium. IEEE, 2009. http://dx.doi.org/10.1109/stherm.2009.4810788.

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4

"Spine." In 2020 36th Semiconductor Thermal Measurement, Modeling & Management Symposium (SEMI-THERM). IEEE, 2020. http://dx.doi.org/10.23919/semi-therm50369.2020.9142857.

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5

"Spine." In 2012 IEEE/CPMT 28th Semiconductor Thermal Measurement & Management Symposium (SEMI-THERM). IEEE, 2012. http://dx.doi.org/10.1109/stherm.2012.6188807.

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6

"[Spine art]." In 2014 30th Semiconductor Thermal Measurement & Management Symposium (SEMI-THERM). IEEE, 2014. http://dx.doi.org/10.1109/semi-therm.2014.6892197.

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7

"[Spine art]." In 2016 32nd Thermal Measurement, Modeling & Management Symposium (SEMI-THERM). IEEE, 2016. http://dx.doi.org/10.1109/semi-therm.2016.7458426.

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8

"[Spine art]." In 2017 33rd Thermal Measurement, Modeling & Management Symposium (SEMI-THERM). IEEE, 2017. http://dx.doi.org/10.1109/semi-therm.2017.7896888.

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9

"[Spine art]." In 2018 34th Thermal Measurement, Modeling & Management Symposium (SEMI-THERM). IEEE, 2018. http://dx.doi.org/10.1109/semi-therm.2018.8357330.

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10

Mao, Yunxiang, Dong Zheng, Shu Liao, Zhigang Peng, Ruyi Yan, Junhua Liu, Zhongxing Dong, et al. "Automatic lumbar spine measurement in CT images." In SPIE Medical Imaging, edited by Samuel G. Armato and Nicholas A. Petrick. SPIE, 2017. http://dx.doi.org/10.1117/12.2254460.

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Звіти організацій з теми "Spine Measurement":

1

Zyla, Piotr A. Precision Measurement of the Neutron Spin Structure Function. Office of Scientific and Technical Information (OSTI), May 2003. http://dx.doi.org/10.2172/813169.

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2

Barrett, Sean E. Spin Decoherence Measurements for Solid State Qubits. Fort Belvoir, VA: Defense Technical Information Center, July 2005. http://dx.doi.org/10.21236/ada459337.

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3

Stuart, L. M. Spin structure measurements from E143 at SLAC. Office of Scientific and Technical Information (OSTI), January 1996. http://dx.doi.org/10.2172/238584.

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4

Kolomensky, Y. G. Precision measurement of the neutron spin dependent structure functions. Office of Scientific and Technical Information (OSTI), February 1997. http://dx.doi.org/10.2172/485989.

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5

Band, Henry. Spin Structure Function Measurements from E143 at SLAC. Office of Scientific and Technical Information (OSTI), July 2003. http://dx.doi.org/10.2172/813299.

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6

Garnett, R. W. Measurement of np elastic scattering spin-spin correlation parameters at 484, 634, and 788 MeV. Office of Scientific and Technical Information (OSTI), March 1989. http://dx.doi.org/10.2172/6207583.

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7

Rock, Stephen E. Precision Measurement of the Proton and Deuteron Spin Structure Functions g2. Office of Scientific and Technical Information (OSTI), February 2003. http://dx.doi.org/10.2172/812643.

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8

Benmouna, N. A Precision Measurement of the Spin Structure Function G(2)(P). Office of Scientific and Technical Information (OSTI), January 2004. http://dx.doi.org/10.2172/826651.

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9

Fersch, Robert. Measurement of Inclusive Proton Double-Spin Asymmetries and Polarized Structure Functions. Office of Scientific and Technical Information (OSTI), August 2008. http://dx.doi.org/10.2172/956055.

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Anderson, Mark D. Beam Spin Asymmetry Measurements for Two Pion Photoproduction at CLAS. Office of Scientific and Technical Information (OSTI), September 2015. http://dx.doi.org/10.2172/1346695.

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