Ultrasonic Imaging最新文献

Polymer microbubbles have garnered broad interest as potential theranostic agents. However, the capabilities of polymer MBs can be greatly enhanced, particularly regarding the imaging performance and functional versatility of the platform. This study investigates integrating fluorescent carbon nanodots within polylactic acid (PLA) microbubbles. First, the formulations are characterized by their size, microbubble counts, zeta potential, and resonance frequency. Then, the fluorescence capabilities, nanoparticle loading, and acoustic capabilities are examined. Unmodified (U-), carboxylated (C-), and aminated graphene quantum dots (A-GQDs) were separately suspended and synthesized at a 2% w/w ratio with PLA in the organic phase of the water/oil/water double emulsion process. The new microbubbles were characterized using an AccuSizer, Zetasizer, scanning electron microscopy, fluorescence microscopy and fluorimetry, a custom-built acoustic setup, and clinical ultrasound. The GQD microbubbles were sized between 1.4 and 1.9 µm (U = 1.90, C = 1.44, A = 1.72, Unloaded = 2.02 µm). The U-GQD microbubble exhibited a higher bubble concentration/mg PLA (p < .05) and the A-GQD microbubbles exhibited the greatest shift in zeta potential. Electron microscopy revealed smooth surfaces and a spherical shape, showing that the nanoparticle addition was not deleterious. The A-GQD microbubbles were specifically detectable using DAPI-filtering with fluorescence microscopy and had the highest TRITC-filtered fluorescence. The C-GQD microbubbles had the highest loading efficiency at 59.4% (p < .05), and the lowest max acoustic enhancement at 5 MHz (U = 19.8, C = 17.6, A = 18.9, Unloaded = 18.5 dB; p < .05). Additionally, all microbubbles were visible and susceptible to inertial cavitation utilizing clinical ultrasound. The A-GQDs showed promise toward improving the theranostic capabilities of the microbubble platform. They have imbued the most advantageous fluorescence capability and slightly improved backscatter enhancement while retaining all the necessary capabilities of an ultrasound contrast agent. Future studies will investigate the coloading potential of A-GQDs and drug within microbubbles.

To formulate a predictive model for assessing Ki-67 expression in breast cancer by integrating pre-treatment ultrasound features with non-morphological magnetic resonance imaging (MRI) parameters, encompassing functional and hemodynamic indicators. A retrospective study was conducted on 167 patients. All patients underwent a breast mass biopsy for histopathological and Ki-67 analysis prior to neoadjuvant chemotherapy (NAC) treatment. Additionally, all patients underwent ultrasonography and MRI examinations prior to the biopsy. The recorded variables were Ki-67, apparent diffusion coefficient (ADC) values, Max Slope, time to peak (TTP), signal enhancement ratio (SER), early enhancement rate (EER), time-signal intensity curve (TIC), tumor maximum diameter, tumor margins and boundaries, aspect ratio, microcalcification, color Doppler flow imaging grading, resistance index (RI), and axillary lymph node metastasis. Statistical analysis was performed using the R software package. Normally distributed continuous data are presented as mean ± standard deviation (SD), skewed continuous data as median, and categorical variables as frequency or percentage. The dataset was randomly divided into a modeling group and a validation group following a 7:3 ratio, employing a predetermined random seed. The selection of variables was conducted using the random forest algorithm. Specifically, in the initial analysis, we trained a random forest model using all available variables. By evaluating the Gini importance scores of each variable, we identified those that contributed the most to predicting Ki-67 expression. The predictive model for Ki-67 expression was constructed using selected variables: Maximum Diameter, ADC value, SER value, Max Slope value, TTP value, and EER value. Within the validation group, the evaluation metrics demonstrated an Area under the curve of 0.961 with a 95% confidence interval ranging from 0.865 to 0.995. The model achieved a kappa score of 1.00, precision of 0.949, recall of 1, an F1 score of 0.974, sensitivity of 100%, specificity of 85.71%, a positive predictive value of 94.87%, and a negative predictive value of 100%. The combination of non-morphological MRI parameters and pre-treatment ultrasound features in a breast cancer prediction model powered by RF machine learning demonstrated favorable clinical outcomes and improved diagnostic performance.

Skeletal muscle dysfunction is common in chronic kidney disease (CKD). Of interest is the concept of "muscle quality," of which measures include ultrasound-derived echo intensity (EI). Alternative parameters of muscle texture, for example, gray level of co-occurrence matrix (GCLM), are available and may circumvent limitations in EI. The validity of EI is limited in humans, particularly in chronic diseases. This study aimed to investigate the associations between ultrasound-derived parameters of muscle texture with MRI. Images of the thigh were acquired using a 3 Tesla MRI scanner. Quantification of muscle (contractile), fat (non-contractile), and miscellaneous (connective tissue, fascia) components were estimated. Anatomical rectus femoris cross-sectional area was measured using B-mode 2D ultrasonography. To assess muscle texture, first (i.e., EI)- and second (i.e., GLCM)-order statistical analyses were performed. Fourteen participants with CKD were included (age: 58.0 ± 11.9 years, 50% male, eGFR: 27.0 ± 7.4 ml/min/1.73m², 55% Stage 4). Higher EI was associated with lower muscle % (quadriceps: β = -.568, p = .034; hamstrings: β = -.644, p = .010). Higher EI was associated with a higher fat % in the hamstrings (β = -.626, p = .017). A higher angular second moment from GLCM analysis was associated with greater muscle % (β = .570, p = .033) and lower fat % (β = -.534, p = .049). A higher inverse difference moment was associated with greater muscle % (β = .610, p = .021 and lower fat % (β = -.599, p = .024). This is the first study to investigate the associations between ultrasound-derived parameters of muscle texture with MRI. Our preliminary findings suggest ultrasound-derived texture analysis provides a novel indicator of reduced skeletal muscle % and thus increased intramuscular fat.

Three-dimensional (3D) ultrasound imaging can be accomplished by reconstructing a sequence of two-dimensional (2D) ultrasound images. However, 2D ultrasound images usually suffer from low resolution in the elevation direction, thereby impacting the accuracy of 3D reconstructed results. The lateral resolution of 2D ultrasound is known to significantly exceed the elevation resolution. By combining scanning sequences acquired from orthogonal directions, the effects of poor elevation resolution can be mitigated through a composite reconstructing process. Moreover, capturing ultrasound images from multiple perspectives necessitates a precise probe positioning method with a wide angle of coverage. Optical tracking is popularly used for probe positioning for its high accuracy and environment-robustness. In this paper, a novel large-angle accurate optical positioning method is used for enhancing resolution in 3D ultrasound imaging through orthogonal-view scanning and composite reconstruction. Experiments on two phantoms proved that our method could significantly improve reconstruction accuracy in the elevation direction of the probe compared with single-angle parallel scanning. The results indicate that our method holds the potential to improve current 3D ultrasound imaging techniques.

The Quantitative Ultrasound backscatter coefficient provides the capability to evaluate tissue microstructure parameters. Tissue-based scatterer parameters are extracted using ultrasound scattering models. It is challenging to correlate ultrasound scatterer parameters of tissue structures from optical-measured histology, possibly because of inappropriate scattering models or the presence of multiple scatterers. The objective of this study is to pursue the quantification of pertinent scatterer parameters with scattering models that consider ultrasound scattering from nuclei and cells. The concentric sphere model (CSM) and the structure factor model adapted for two types of scatterers (SFM2) are evaluated for cell-pellet biophantoms and ex vivo tumors of four cell lines: 4T1, JC, LMTK, and MAT. The structure factor model (SFM) was used for comparison. CSM and SFM2 provided scatterer parameters closer to histology (lower relative errors) for nucleus and cell radii and volume fractions than SFM but were not always accompanied by lower dispersion of the scatterer distribution (lower coefficient of variation). CSM and SFM2 quantified cell and nucleus radius and volume fraction parameters with lower relative error compared to SFM. For tumors, CSM provided better results than SFM2.

Skeletal muscle is a vital organ that promotes human movement and maintains posture. Accurate assessment of muscle strength is helpful to provide valuable insights for athletes' rehabilitation and strength training. However, traditional techniques rely heavily on the operator's expertise, which may affect the accuracy of the results. In this study, we propose an automated method to evaluate muscle strength using ultrasound and deep learning techniques. B-mode ultrasound data of biceps brachii of multiple athletes at different strength levels were collected and then used to train our deep learning model. To evaluate the effectiveness of this method, this study tested the contraction of the biceps brachii under different force levels. The classification accuracy of this method for grade 4 and grade 6 muscle strength reached 98% and 96%, respectively, and the overall average accuracy was 93% and 87%, respectively. The experimental results confirm that the innovative methods in this paper can accurately and effectively evaluate and classify muscle strength.