Current Issue : April - June Volume : 2016 Issue Number : 2 Articles : 6 Articles
Background: The observation of ultrasound generated cavitation bubbles deep in\ntissue is very difficult. The development of an imaging method capable of investigating\ncavitation bubbles in tissue would improve the efficiency and application of ultrasound\nin the clinic. Among the previous imaging modalities capable of detecting cavitation\nbubbles in vivo, the acoustic detection technique has the positive aspect of in vivo\napplication. However the size of the initial cavitation bubble and the amplitude of the\nultrasound that produced the cavitation bubbles, affect the timing and amplitude of\nthe cavitation bubbles� emissions.\nMethods: The spatial distribution of cavitation bubbles, driven by 0.8835 MHz therapeutic\nultrasound system at output power of 14 Watt, was studied in water using a synchrotron\nX-ray imaging technique, Analyzer Based Imaging (ABI). The cavitation bubble\ndistribution was investigated by repeated application of the ultrasound and imaging\nthe water tank. The spatial frequency of the cavitation bubble pattern was evaluated\nby Fourier analysis. Acoustic cavitation was imaged at four different locations through\nthe acoustic beam in water at a fixed power level. The pattern of cavitation bubbles in\nwater was detected by synchrotron X-ray ABI.\nResults: The spatial distribution of cavitation bubbles driven by the therapeutic\nultrasound system was observed using ABI X-ray imaging technique. It was observed\nthat the cavitation bubbles appeared in a periodic pattern. The calculated distance\nbetween intervals revealed that the distance of frequent cavitation lines (intervals) is\none-half of the acoustic wave length consistent with standing waves.\nConclusion: This set of experiments demonstrates the utility of synchrotron ABI for\nvisualizing cavitation bubbles formed in water by clinical ultrasound systems working\nat high frequency and output powers as low as a therapeutic system....
Background: Uterine fibroids occur singly or as multiple benign tumors originating\nin the myometrium. Because they vary in size and location, the approach and technique\nfor their identification and surgical management vary. Reference images, such as\nultrasound images, magnetic resonance images, and sonohystograms, do not provide\nreal-time intraoperative findings.\nMethods: Electromagnetic image guidance, as incorporated in the Acessa Guidance\nSystem, has been cleared by the FDA to facilitate targeting and ablation of\nuterine fibroids during laparoscopic surgery. This is the first feasibility study to verify\nthe features and usefulness of the guidance system in targeting symptomatic uterine\nfibroidsââ?¬â?particularly hard-to-reach intramural fibroids and those abutting the endometrium.\nOne gynecologic surgeon, who had extensive prior experience in laparoscopic\nultrasound-guided identification of fibroids, treated five women with symptomatic\nuterine fibroids using the Acessa Guidance System. The surgeon evaluated the\nsystem and its features in terms of responses to prescribed statements; the responses\nwere analyzed prospectively.\nResults: The surgeon strongly agreed (96 %) or agreed (4 %) with statements describing\nthe helpfulness of the transducer and hand pieceââ?¬â?¢s dynamic animation in targeting\neach fibroid, reaching the fibroid quickly, visualizing the positions of the transducer and\nhandpiece within the pelvic cavity, and providing the surgeon with confidence when\ntargeting the fibroid even during ââ?¬Å?out-of-planeââ?¬Â positioning of the handpiece.\nConclusions: The surgeonââ?¬â?¢s positive user experience was evident in the guidance\nsystemââ?¬â?¢s facilitation of accurate handpiece tip placement during targeting and ablation\nof uterine fibroids. Continued study of electromagnetic image guidance in the laparoscopic\nidentification and treatment of fibroids is warranted....
To determine whether an automated extraction of the brain-tissue region from CT\nimages is useful for the histogram analysis of the brain-tissue region was studied. We\nused the CT images of 11 patients. We developed an automatic brain-tissue extraction\nalgorithm. We evaluated the similarity index of this automated extraction method\nrelative to manual extraction, and we compared the mean CT number of all extracted\npixels and the kurtosis and skewness of the distribution of CT numbers of all extracted\npixels from the automated and manual extractions. The similarity index was 0.93. The\nmean CT number and the kurtosis and skewness from the automated extraction were\n35.0 Hounsfield units, 0.63, and 0.51, respectively, and were equivalent to those from\nthe manual extraction (35.4 Hounsfield units, 0.59, and 0.46, respectively). The automated\nextraction of the brain-tissue region from whole-brain CT images was useful for\nhistogram analysis of the brain-tissue region....
Background: Prostate cancer is one of the most common forms of cancer found in males making early diagnosis\nimportant. Magnetic resonance imaging (MRI) has been useful in visualizing and localizing tumor candidates and with\nthe use of endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The coils introduce intensity\ninhomogeneities and the surface coil intensity correction built into MRI scanners is used to reduce these\ninhomogeneities. However, the correction typically performed at the MRI scanner level leads to noise amplification\nand noise level variations.\nMethods: In this study, we introduce a new Monte Carlo-based noise compensation approach for coil intensity\ncorrected endorectal MRI which allows for effective noise compensation and preservation of details within the\nprostate. The approach accounts for the ERC SNR profile via a spatially-adaptive noise model for correcting\nnon-stationary noise variations. Such a method is useful particularly for improving the image quality of coil intensity\ncorrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available.\nResults: SNR and contrast-to-noise ratio (CNR) analysis in patient experiments demonstrate an average improvement\nof 11.7 and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong performance when compared\nto existing approaches.\nDiscussion: Experimental results using both phantom and patient data showed that ACER provided strong\nperformance in terms of SNR, CNR, edge preservation, subjective scoring when compared to a number of existing\napproaches.\nConclusions: A new noise compensation method was developed for the purpose of improving the quality of coil\nintensity corrected endorectal MRI data performed at the MRI scanner level. We illustrate that promising noise\ncompensation performance can be achieved for the proposed approach, which is particularly important for\nprocessing coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw\ndata is not available....
Background: For optimizing and evaluating image quality in medical imaging, one can use visual grading\nexperiments, where observers rate some aspect of image quality on an ordinal scale. To analyze the grading\ndata, several regression methods are available, and this study aimed at empirically comparing such techniques,\nin particular when including random effects in the models, which is appropriate for observers and patients.\nMethods: Data were taken from a previous study where 6 observers graded or ranked in 40 patients the image\nquality of four imaging protocols, differing in radiation dose and image reconstruction method. The models tested\nincluded linear regression, the proportional odds model for ordinal logistic regression, the partial proportional odds\nmodel, the stereotype logistic regression model and rank-order logistic regression (for ranking data). In the first two\nmodels, random effects as well as fixed effects could be included; in the remaining three, only fixed effects.\nResults: In general, the goodness of fit (AIC and McFadden�s Pseudo R2) showed small differences between the\nmodels with fixed effects only. For the mixed-effects models, higher AIC and lower Pseudo R2 was obtained, which\nmay be related to the different number of parameters in these models. The estimated potential for dose reduction\nby new image reconstruction methods varied only slightly between models.\nConclusions: The authors suggest that the most suitable approach may be to use ordinal logistic regression, which\ncan handle ordinal data and random effects appropriately....
Background: Fluorescence molecular tomography (FMT) is an optical imaging technique\nthat reveals biological processes within small animals through non-invasively\nreconstructing the distributions of fluorescent agents. The primary problem in FMT\nwith non-stationary fluorescent yield is the increase of the unknown parameters to be\nreconstructed. In this paper, a method is proposed to reconstruct dynamic fluorescent\nyield.\nMethods: A shape-based reconstruction method that recovers dynamic fluorescent\nyield with a level set method is proposed for FMT. To reduce the number of unknown\nparameters, a level set function is introduced to describe the shape of target and a\nsmall number of parameters are used to describe the fluorescent yields at different\ntime points.\nResults: Results of simulations and phantom experiments demonstrate that the proposed\nmethod can recover well the dynamic fluorescent yields, shapes and locations\nof the target.\nConclusions: The proposed method can handle the cases with non-stationary fluorescent\nyields and recover the fluorescent yields at each projection angle....
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