A novel image formation method for ultrasonic strain imaging.
Ontology highlight
ABSTRACT:
SUBMITTER: Jiang J
PROVIDER: S-EPMC2041835 | biostudies-literature | 2007 Apr
REPOSITORIES: biostudies-literature
Publications
A novel image formation method for ultrasonic strain imaging.
Jiang Jingfeng J Hall Timothy J TJ Sommer Amy M AM
Ultrasound in medicine & biology 20070321 4
This paper presents a new method for forming high-quality ultrasonic strain images. To achieve this goal, three radiofrequency echo frames are selected by an automated performance assessment method and used to generate two parent strain images located in the same physical grid from which a high quality composite strain image may be calculated by averaging. The automatic performance evaluation method combines the consistency among the two parent strain images and the accuracy of motion tracking i ...[more]
Similar Datasets
Project description:Ultrasonic strain imaging that uses signals from conventional diagnostic ultrasound systems is capable of showing the contrast of tissue elasticity, which provides new diagnostically valuable information. To assess and improve the diagnostic performance of ultrasonic strain imaging, it is essential to have a quantitative measure of image quality. Moreover, it is useful if the image quality measure is simple to interpret and can be used for visual feedback while scanning and as a training tool for operator performance evaluation. This report describes the development of a novel quantitative method for systematic performance assessment that is based on the combination of measures of the accuracy of motion tracking and consistency among consecutive strain fields. The accuracy of motion tracking assesses the reliability of strain images. The consistency among consecutive strain images assesses the signal quality in strain images. The clinical implications of the proposed method to differentiate good or poor strain images are discussed. Results of experiments with tissue-mimicking phantoms and in vivo breast-tissue data demonstrate that the performance measure is a useful method for automatically rating elasticity image quality.
| S-EPMC1866296 | biostudies-literature
Project description:BackgroundImaging repositories are commonly attached to ongoing clinical trials, but capturing, transmitting, and storing images can be complicated and labor-intensive. Typical methods include outdated technologies such as compact discs. Electronic file transfer is becoming more common, but even this requires hours of staff time on dedicated computers in the radiology department.MethodsWe describe and test an image capture method using smartphone camera video-derived images of brain computed tomography (CT) scans of traumatic intracranial hemorrhage. The deidentified videos are emailed or uploaded from the emergency department for central adjudication. We selected eight scans, mild moderate, and severe subdural and multicompartmental hematomas and mild and moderate intraparenchymal hematomas. Ten users acquired data using seven different smartphones. We measured the time in seconds it took to capture and send the files. The primary outcomes were hematoma volume measured by ABC/2, Marshall scale, midline shift measurement, image quality by a contrast-to-noise ratio (CNR), and time to capture. A radiologist and an imaging scientist applied the ABC/2 method and calculated the Marshall scale and midline shift on the data acquired on different smartphones and the PACS in a randomized order. We calculate the intraclass correlation coefficient (ICC). We measured image quality by calculating the contrast-to-noise ratio (CNR). We report summary statistics on time to capture in the smartphone group without a comparator.ResultsICC for lesion volume, midline shift, and Marshall score were 0.973 (95% CI 0.931, 0.994), 0.998 (95% CI: 0.996, 0.999), and 0.973 (0.931, 0.994), respectively. Lesion conspicuity was not different among the image types via assessment of CNR using the Friedman test, [Formula: see text] of 24.8, P = < .001, with a small Kendall's W effect size (0.591). Mean (standard deviation) time to capture and email the video was 60.1 (24.3) s.ConclusionsTypical smartphones may produce video image quality high enough for use in a clinical trial imaging repository. Video capture and transfer takes only seconds, and hematoma volumes, Marshall scales, and image quality measured on the videos did not differ significantly from those calculated on the PACS.
| S-EPMC9854024 | biostudies-literature
Project description:Ultrasonic imaging, using ultrasonic phased arrays, has an enormous impact in science, medicine and society and is a widely used modality in many application fields. The maximum amount of information which can be captured by an array is provided by the data acquisition method capturing the complete data set of signals from all possible combinations of ultrasonic generation and detection elements of a dense array. However, capturing this complete data set requires long data acquisition time, large number of array elements and transmit channels and produces a large volume of data. All these reasons make such data acquisition unfeasible due to the existing phased array technology or non-applicable to cases requiring fast measurement time. This paper introduces the concept of an adaptive data acquisition process, the Selective Matrix Capture (SMC), which can adapt, dynamically, to specific imaging requirements for efficient ultrasonic imaging. SMC is realised experimentally using Laser Induced Phased Arrays (LIPAs), that use lasers to generate and detect ultrasound. The flexibility and reconfigurability of LIPAs enable the evolution of the array configuration, on-the-fly. The SMC methodology consists of two stages: a stage for detecting and localising regions of interest, by means of iteratively synthesising a sparse array, and a second stage for array optimisation to the region of interest. The delay-and-sum is used as the imaging algorithm and the experimental results are compared to images produced using the complete generation-detection data set. It is shown that SMC, without a priori knowledge of the test sample, is able to achieve comparable results, while preforming ∼ 10 times faster data acquisition and achieving ∼ 10 times reduction in data size.
| S-EPMC11015044 | biostudies-literature
Project description:This study developed an improved motion estimation algorithm for ultrasonic strain imaging that employs a dynamic programming technique. In this article, we model the motion estimation task as an optimization problem. Since tissue motion under external mechanical stimuli often should be reasonably continuous, a set of cost functions combining correlation and various levels of motion continuity constraint were used to regularize the motion estimation. To solve the optimization problem with a reasonable computational load, a dynamic programming technique that does not require iterations was used to obtain displacement vectors in integer precision. Then, a subsample estimation algorithm was used to calculate local displacements in fractional precision. Two implementation schemes were investigated with in vivo ultrasound echo data sets. We found that the proposed algorithm provides more accurate displacement estimates than our previous algorithm for in vivo clinical data. In particular, the new algorithm is capable of tracking motion in more complex anatomy and increases strain image consistency in a sequence of images. Preliminary results also suggest that a significantly longer sequence of high contrast strain images could be obtained with the new algorithm compared with the previous algorithm. The new algorithm can also tolerate larger motion discontinuities (e.g., cavity in an anthropomorphic uterine phantom).
| S-EPMC2843521 | biostudies-literature
Project description:Mechanical signals play a vital role in cell biology and is a vast area of research. Thus, there is motivation to understand cell deformation and mechanobiological responses. However, the ability to controllably deform cells in the ultrasonic regime and test their response is a noted challenge throughout the literature. Quantifying and eliciting an appropriate stimulus has proven to be difficult, resulting in methods that are either too aggressive or oversimplified. Furthermore, the ability to gain a real-time insight into cell deformation and link this with the biological response is yet to be achieved. One application of this understanding is in ultrasonic surgical cutting, which is a promising alternative to traditional methods, but with little understanding of its effect on cells. Here we present the image based ultrasonic cell shaking test, a novel method that enables controllable loading of cells and quantification of their response to ultrasonic vibrations. Practically, this involves seeding cells on a substrate that resonates at ultrasonic frequencies and transfers the deformation to the cells. This is then incorporated into microscopic imaging techniques to obtain high-speed images of ultrasonic cell deformation that can be analysed using digital image correlation techniques. Cells can then be extracted after excitation to undergo analysis to understand the biological response to the deformation. This method could aid in understanding the effects of ultrasonic stimulation on cells and how activated mechanobiological pathways result in physical and biochemical responses.
| S-EPMC10503762 | biostudies-literature
Project description:ObjectivesWe aimed to assess the clinical value of multimodal photoacoustic/ultrasound (PA/US) articular imaging scores, a novel imaging method which can reflect the micro-vessels and oxygenation level of inflamed joints of rheumatoid arthritis (RA).MethodsSeven small joints were examined by the PA/US imaging system. A 0-3 scoring system was used to semi-quantify the PA and power-Doppler (PD) signals, and the sums of PA and PD scores (PA-sum and PD-sum scores) of the seven joints were calculated. The relative oxygen saturation (SO2) values of the inflamed joints were measured and classified into 3 PA+SO2 patterns. The correlations between the PA/US imaging scores and the disease activity scores were assessed.ResultsThirty-one patients of RA and a total of 217 joints were examined using the PA/US system. The PA-sum had high positive correlations with the standard clinical scores of RA (DAS28 [ESR] ρ = 0.754, DAS28 [CRP] ρ = 0.796, SDAI ρ = 0.836, CDAI ρ = 0.837, p < 0.001), which were superior to the PD-sum (DAS28 [ESR] ρ = 0.651, DAS28 [CRP] ρ = 0.676, SDAI ρ = 0.716, CDAI ρ = 0.709, p < 0.001). For the patients with high PA-sum scores, significant differences between hypoxia and hyperoxia were identified in pain visual analog score (p = 0.020) and patient's global assessment (p = 0.026). The PA+SO2 patterns presented moderate and high correlation with PGA (ρ = 0.477, p = 0.0077) and VAS pain score (ρ = 0.717, p < 0.001).ConclusionThe PA scores have significant correlations with standard clinical scores for RA, and the PA+SO2 patterns are also related with clinical scores that reflect pain severity. PA may have clinical potential in evaluating RA.Key points• Multimodal photoacoustic/ultrasound imaging is a novel method to assess micro-vessels and oxygenation of local lesions. • Significant correlations between multimodal imaging parameters and clinical scores of RA patients were verified. • The multimodal PA/US system can provide objective imaging parameters, including PA scores of micro-vessels and relative SO2 value, as a supplementary to disease activity evaluation.
| S-EPMC8043900 | biostudies-literature
Project description:There is a growing interest in using 18F-DPA-714 PET to study neuroinflammation and microglial activation through imaging the 18-kDa translocator protein (TSPO). Although quantification of 18F-DPA-714 binding can be achieved through kinetic modeling analysis with an arterial input function (AIF) measured with blood sampling procedures, the invasiveness of such procedures has been an obstacle for wide application. To address these challenges, we developed an image-derived input function (IDIF) that noninvasively estimates the arterial input function from the images acquired for 18F-DPA-714 quantification. Methods: The method entails three fully automatic steps to extract the IDIF, including a segmentation of voxels with highest likelihood of being the arterial blood over the carotid artery, a model-based matrix factorization to extract the arterial blood signal, and a scaling optimization procedure to scale the extracted arterial blood signal into the activity concentration unit. Two cohorts of human subjects were used to evaluate the extracted IDIF. In the first cohort of five subjects, arterial blood sampling was performed, and the calculated IDIF was validated against the measured AIF through the comparison of distribution volumes from AIF (VT,AIF) and IDIF (VT,IDIF). In the second cohort, PET studies from twenty-eight healthy controls without arterial blood sampling were used to compare VT,IDIF with VT,REF measured using a reference region-based analysis to evaluate whether it can distinguish high-affinity (HAB) and mixed-affinity (MAB) binders. Results: In the arterial blood-sampling cohort, VT derived from IDIF was found to be an accurate surrogate of the VT from AIF. The bias of VT, IDIF was −5.8 ± 7.8% when compared to VT,AIF, and the linear mixed effect model showed a high correlation between VT,AIF and VT, IDIF (p < 0.001). In the nonblood-sampling cohort, VT, IDIF showed a significance difference between the HAB and MAB healthy controls. VT, IDIF and standard uptake values (SUV) showed superior results in distinguishing HAB from MAB subjects than VT,REF. Conclusions: A novel IDIF method for 18F-DPA-714 PET quantification was developed and evaluated in this study. This IDIF provides a noninvasive alternative measurement of VT to quantify the TSPO binding of 18F-DPA-714 in the human brain through dynamic PET scans.
| S-EPMC9140104 | biostudies-literature
Project description:BackgroundVarious studies, mostly with animals, have provided evidence of adverse impacts of fluoride (F-) on bone density, collagen and microstructure, yet its effects on overall bone quality (strength) has not been clearly or extensively characterized in human populations.ObjectiveIn this observational study, we assessed variation in an integrated measures of bone quality in a population exposed to wide-ranging F- levels (0.3 to 15.5 mg/L) in drinking water, using a novel application of non-ionizing ultrasonic method.MethodWe collected 871 speed of sound (SOS) measurements from 341 subjects residing in 25 communities, aged 10-70 years (188 males and 153 females). All subjects received scans of the cortical radius and tibia, and adults over the age of 19 received an additional scan of the phalanx. Associations between F- in drinking water and 24-h urine samples, and SOS as a measure of bone quality, were evaluated in bivariate and multivariable regressions adjusting for age, sex, BMI, smoking, and toothpaste use.ResultsWe found negative associations between F- exposure and bone quality at all three bones. Adult tibial SOS showed the strongest inverse association with F- exposure, which accounted for 20% of the variance in SOS measures (r = 0.45; n = 199; p < 0.0001). In adjusted analysis, a 1 mg/L increase in F- in drinking water was related to a reduction of 15.8 m/s (95% CI: -21.3 to -10.3), whereas a 1 mg/L increase in 24-h urinary F- (range: 0.04-39.5 mg/L) was linked to a reduction of 8.4 m/s (95% CI: -12.7, -4.12) of adult tibial SOS. Among adolescents, in contrast, weaker and non-significant inverse associations between F- exposure and SOS were found, while age, gender, and BMI were more significant predictors than in adults.ConclusionsThese results are indicative of a fluoride-induced deterioration of bone quality in humans, likely reflecting a combination of factors related to SOS: net bone loss, abnormal mineralization and collagen formation, or altered microarchitecture. The portable and low-cost ultrasound technique appears potentially useful for assessment of bone quality, and should be tested in other locations and for other bone-related disorders, to assess the feasibility of its more extensive diagnostic use in hard-to-reach rural regions.
| S-EPMC6933268 | biostudies-literature
Project description:Abstract With the rapid increase in the number of people infected with COVID-19 disease in the entire world, and with the limited medical equipment used to detect it (testing kit), it becomes necessary to provide another detection method that mainly relies on Artificial Intelligence and radiographic Image Analysis to determine the disease infection. In this study, we proposed a diagnosis system that detects the COVID-19 using chest X-ray or computed tomography (CT) scan images knowing that this system does not eliminate the reverse transcription-polymerase chain reaction test but rather complements it. The proposed system consists of the following steps, starting with extracting the image’s features using Visual Words Fusion of ResNet-50 (deep neural network) and Histogram of Oriented Gradient descriptors based on Bag of Visual Word methodology. Then training the Adaptive Boosting classifier to classify the image to COVID-19 or NOTCOVID-19 and finally retrieving the most similar images. We implemented our work on X-ray and CT scan databases, and the experimental results demonstrate the effectiveness of the proposed system. The performance of the classification task in terms of accuracy was as follows: 100% for classifying the input image to X-ray or CT scan, 99.18% for classifying X-ray image to COVID-19 or NOTCOVID-19 and 97.84% for classifying CT scan to COVID-19 or NOTCOVID-19.
| S-EPMC8194842 | biostudies-literature
Project description:ObjectivesThis study aimed to develop an approach to enhance the model precision by artificial images.Materials and methodsGiven an epidemiological study designed to predict 1 response using f features with M samples, each feature was converted into a pixel with certain value. Permutated these pixels into F orders, resulting in F distinct artificial image sample sets. Based on the experience of image recognition techniques, appropriate training images results in higher precision model. In the preliminary experiment, a binary response was predicted by 76 features, the sample set included 223 patients and 1776 healthy controls.ResultsWe randomly selected 10 000 artificial sample sets to train the model. Models' performance (area under the receiver operating characteristic curve values) depicted a bell-shaped distribution.ConclusionThe model construction strategy developed in the research has potential to capture feature order related information and enhance model predictability.
| S-EPMC10860535 | biostudies-literature