Effects of Local Blood Flow on Muscle Stiffness

Muscle injuries, in the form of strains or even tears, affect millions of people each year through undue tension on muscles during everyday activities, work tasks, or physical activity including sports or working out. These injuries can take from a few weeks to even months to heal, with patients having to deal with inflammation, swelling, and pain throughout the healing process. Scar tissue also forms when the muscle is injured, which regenerates throughout the healing process, but never fully recovers to its state prior to injury. This scar tissue is thought to make the muscle more prone to subsequent injury, making it important to avoid muscle injuries to begin with so as to not lose overall strength and range of motion. Although there are currently certain activities identified to increase the probability of muscle injury, there is limited evidence as to what physiological components may make an individual more susceptible to injury. Therefore, the purpose of this study is to look at the association between two measures; blood flow velocity through muscle using Doppler ultrasound and the muscle\u27s stiffness, or Young\u27s modulus, using ultrasound elastography. A notable correlation between the two factors could allow clinicians to know if patients have a predisposition to muscle injury due to their rate of blood flow

Cerebrovascular Reactivity Change with Increased Intracranial Pressure

Cerebrovascular reactivity (CVR) is an important regulatory factor of the brain. The parameter controls the dilation and constriction of blood vessels in the brain in response to certain stimuli, such as carbon dioxide. Abnormal CVR values could potentially be indicators of poorly functioning regulatory systems. Testing of CVR is one method of assessing the brain\u27s regulatory capabilities. The purpose of this study was to test for a relationship between CVR and intracranial pressure (ICP). In this study, increased intracranial pressure was created in 4 female subjects through head down tilt, using an inversion table. Subjects were lowered in the table and secured in 15 degree increments, from positive 45 degrees, to negative 30 degrees. At each level, a breath holding test was performed to measure CVR. In the breath holding test, the subject breathed normally for 30 seconds, held their breath for 30 seconds, and breathed normally for 30 seconds while blood flow velocity was recorded in the middle cerebral artery. The subject’s blood pressure was taken at each level of the table. The blood flow velocities from the middle cerebral artery were used to calculate the subject’s CVR value for each angle. The CVR values were observed to not change significantly at different angles of the inversion table. As mean arterial pressure was also observed to have remained unchanged through the various angles of the inversion table, it was confirmed that ICP was being increased

Influence of ultrasound machine settings on quantitative measures derived from spatial frequency analysis of muscle tissue

Background Ultrasound is a powerful tool for diagnostic purposes and provides insight into both normal and pathologic tissue structure. Spatial frequency analysis (SFA) methods characterize musculoskeletal tissue organization from ultrasound images. Both sonographers in clinical imaging and researchers may alter a minimized range of ultrasound settings to optimize image quality, and it is important to know how these small adjustments of these settings affect SFA parameters. The purpose of this study was to investigate the effects of making small adjustments in a typical default ultrasound machine setting on extracted spatial frequency parameters (peak spatial frequency radius (PSFR), Mmax, Mmax%, and Sum) in the biceps femoris muscle. Methods Longitudinal B-mode images were collected from the biceps femoris muscle in 36 participants. The window depth, foci locations, and gain were systematically adjusted consistent with clinical imaging procedures for a total of 27 images per participant. Images were analyzed by identifying a region of interest (ROI) in the middle portion of the muscle belly in a template image and using a normalized two-dimensional cross-correlation technique between the template image and subsequent images. The ROI was analyzed in the frequency domain using conventional SFA methods. Separate linear mixed effects models were run for each extracted parameter. Results PSFR was affected by modifications in focus location only (p \u3c 0.001) with differences noted between all locations. Mmax% was influenced by the interaction of gain and focus location (p \u3c 0.001) but was also independently affected by increasing window depth (p \u3c 0.001). Both Mmax and Sum parameters were sensitive to small changes in machine settings with the interaction of focus location and window depth (p \u3c 0.001 for both parameters) as well as window depth and gain (p \u3c 0.001 for both) influencing the extracted values. Conclusions Frequently adjusted imaging settings influence some SFA statistics. PSFR and Mmax% appear to be most robust to small changes in image settings, making them best suited for comparison across individuals and between studies, which is appealing for the clinical utility of the SFA method

Analysis of Breath-Holding Index as an Assessment of Cerebrovascular Reactivity

Cerebrovascular reactivity (CVR) is a key factor in regulating blood flow into the brain, and a marker for vascular disease. If the brain\u27s regulatory system is not working, a patient may be in serious trouble. Testing of CVR is one method of assessing the brain\u27s regulatory capabilities. Transcranial Doppler ultrasound (TCD) is one tool to measure CVR. In this method, carbon dioxide in the blood is transiently increased (such as with the holding of breath), and the resulting blood flow in the brain is measured. In this study, we are going to measure the variability of the breathholding index. Within the four subjects, the standard deviations of the CVR measurements are 0.27, 0.20, 0.15, and 0.61 respectively, with an overall standard deviation of 0.31 across the population. The standard deviation between the average CVR measurements of each subject is 0.09. The CVR measurement is usually higher after the first breathholding for each subject, with the following two CVR measurements being lower and less variable. There is no significant increase or decrease in the mean arterial blood pressure (MAP) before or after breath-holding. In conclusion, the breath-holding maneuver is a convenient and well-tolerated screening method for CVR. However, this experiment showed a high variability in this measurement. To obtain an accurate result, three breath-holding indices need to be taken and averaged for each subject

Effect of flow gradient, ROI size and random scatterer movement during speckle size estimation based blood flow measurement

Conventional blood flow velocity measurement using ultrasound is capable of resolving the axial component (i.e., that aligned with the ultrasound propagation direction) of the blood flow velocity vector. However, these Doppler-based methods are incapable of detecting blood flow in the direction normal to the ultrasound beam. An algorithm which measures the lateral blood flow velocity using speckle size change with scan velocity was developed in our previous studies. This method uses the apparent speckle size change that occurs when scatterers are moving relative to the spatial rate of A-line acquisition. Our previous results showed that the estimation error of this algorithm increases with increasing flow gradient and random scatterer movement. In this paper, the relationship between the estimation performance and flow gradient, random scatterer movement and ROI size is investigated and quantitatively assessed. Simulated blood flow data with and without flow gradient and random scatterer movement were generated by the Field II simulation program. The flow gradient is introduced by a parabolic flow profile in the simulated vessel and the random scatterer movement is generated by adding Gaussian noise to the scatterers’ position with a standard deviation as much as one tenth of the speckle cell size in each direction. Our results showed that: 1) in plug flow, estimation error decreases with increasing ROI size, with an average minimum error below 5%. An optimal ROI size exists in both directions, which is 2.5 axial speckle cell lengths axially and 30 lateral speckle cell widths laterally; 2) the estimation error increases up to 10% with flow gradient; 3) an optimal lateral ROI size still exists given the presence of a flow gradient; 4) the estimation error increases with increasing axial ROI size since the correlation length shortens by the introduction of a flow gradient; 5) in addition to the previous results, when random scatterer movement is introduced into the blood flow, the average estimation error is worse by about a factor of three than data without random scatterer movement

Resolving the Lateral Component of Blood Flow Velocity based on Ultrasound Speckle Size Change with Scan Direction and Speed

Conventional blood flow velocity measurement using ultrasound is capable of resolving the axial component (i.e., that aligned with the ultrasound propagation direction) of the blood flow velocity vector. However, these Doppler-based methods are incapable of detecting blood flow in the direction normal to the ultrasound beam. In addition, these methods require repeated pulse-echo interrogation at the same spatial location. In this paper, we introduce a method which estimates the lateral component of blood flow within a single image frame using the observation that the speckle pattern corresponding to the blood reflectors (typically red blood cells) stretches (i.e., is “smeared”) if the blood is moving in the same direction as the electronically-controlled transducer line selection in a 2D image. The situation is analogous to the observed elongation of a subject photographed with a moving camera. Here, we develop a relationship between speckle size, scan speed, and blood flow velocity. Experiments were performed with a blood flow phantom and high-frequency transducer of a commercially available ultrasound machine. Data was captured through an interface allowing access to the raw beam formed data. Blood flow with velocities ranging from 15 to 40 cm/s were investigated in this paper. Results show that there is a linear relationship between the reciprocal of the stretch factor and blood flow velocity. Two scan speeds were used in our experiments. When the scan velocity is 64.8 cm/s, compared with the theoretical model, fitting results based on experimental data gave us a linear relationship with average flow estimation error of 1.74±1.48 cm/s. When the scan velocity is 37.4 cm/s, the average estimation error is 0.65±0.45 cm/s

Single Molecule Diffusion Coefficient Estimation by Image Analysis of Simulated CCD Images to Aid High-Throughput Screening

Extension of one-dimensional signal analysis to two-dimensional image analysis could accelerate conventional methods of high-throughput screening in the discovery of new pharmaceutical agents. This work describes a first step taken towards this goal – the evaluation of image-analysis based estimation strategies of the diffusion coefficient of a single molecule transported within a microfabricated flowcell. A computer simulation of single-molecule imaging by a charge-coupled device (CCD) camera is used to determine if it is possible to distinguish three different types of molecules with different diffusion coefficients. The Gaussian fitting algorithm finds the variance of the transverse trajectory, which increases linearly with the diffusion coefficient; the path analysis algorithm determines the diffusion coefficient from cumulative summation of the squared displacement along the imaged path; the detector area analysis algorithm determines the number of resolvable positions or pixels in the imaged trajectory. Of the three methods, the path analysis strategy appears to provide the most reliable measure of diffusion coefficient with relative error of 13.6% and 6.4% between single molecules with diffusion coefficients of 2.85e-7 and 1.425e-7 cm2/s. The detector area analysis method can statistically distinguish between single molecules with diffusion coefficients of 5.7e-7 and 1.425e-7 cm2/s at the p0.05 level

Circle of Willis Model for Transcranial Doppler Ultrasound Training

Transcranial Doppler (TCD) ultrasound is a technique involving the use of high frequency transmitters to measure intracranial blood flow. The brain is supplied by blood in an arterial anastomosis called the Circle of Willis. Using TCD ultrasound on the circle is difficult and requires practice and teaching. A functional model of the Circle of Willis could prove to be a valuable teaching tool. Through the use of AutoCAD and 3D printing software, an anatomically accurate model was created and set in gelatin phantom inside of a plastic skull. Milk was pumped through the model with a peristaltic pump to simulate blood flow and was analyzed with TCD ultrasound. Flow rate of anterior cerebral arteries was comparable to physiological blood flow. Further work must be done in improving blood flow waveforms and reproducibility of the model, in order to be used as a teaching tool

Influence of ultrasound machine settings on quantitative measures derived from spatial frequency analysis of muscle tissue

Abstract Background Ultrasound is a powerful tool for diagnostic purposes and provides insight into both normal and pathologic tissue structure. Spatial frequency analysis (SFA) methods characterize musculoskeletal tissue organization from ultrasound images. Both sonographers in clinical imaging and researchers may alter a minimized range of ultrasound settings to optimize image quality, and it is important to know how these small adjustments of these settings affect SFA parameters. The purpose of this study was to investigate the effects of making small adjustments in a typical default ultrasound machine setting on extracted spatial frequency parameters (peak spatial frequency radius (PSFR), Mmax, Mmax%, and Sum) in the biceps femoris muscle. Methods Longitudinal B-mode images were collected from the biceps femoris muscle in 36 participants. The window depth, foci locations, and gain were systematically adjusted consistent with clinical imaging procedures for a total of 27 images per participant. Images were analyzed by identifying a region of interest (ROI) in the middle portion of the muscle belly in a template image and using a normalized two-dimensional cross-correlation technique between the template image and subsequent images. The ROI was analyzed in the frequency domain using conventional SFA methods. Separate linear mixed effects models were run for each extracted parameter. Results PSFR was affected by modifications in focus location only (p < 0.001) with differences noted between all locations. Mmax% was influenced by the interaction of gain and focus location (p < 0.001) but was also independently affected by increasing window depth (p < 0.001). Both Mmax and Sum parameters were sensitive to small changes in machine settings with the interaction of focus location and window depth (p < 0.001 for both parameters) as well as window depth and gain (p < 0.001 for both) influencing the extracted values. Conclusions Frequently adjusted imaging settings influence some SFA statistics. PSFR and Mmax% appear to be most robust to small changes in image settings, making them best suited for comparison across individuals and between studies, which is appealing for the clinical utility of the SFA method