Kinetics of intravenous radiographic contrast medium injections as used on CT: simulation with time delay differential equations in a basic human cardiovascular multicompartment model

Influence of Nanobubble Concentration on Blood-Brain Barrier Opening Using Focused Ultrasound Under Real-Time Acoustic Feedback Control

Real-time acoustic feedback control based on harmonic emissions of stimulated microbubbles may serve as a way to achieve reliable blood-brain barrier (BBB) opening with focused ultrasound in the brain. Previously, we demonstrated BBB opening was possible using sub-micron bubbles (aka nanobubbles) and produced comparable results to commercially available microbubbles (Optison, Definity, etc.). The harmonic emissions and acoustic control were observed to be more consistent using nanobubbles, which warrants further study of BBB opening using these agents. This study examined the stimulated acoustic emissions of nanobubbles at different concentrations both in vitro and in vivo and evaluated BBB opening under real-time acoustic feedback control across concentrations. Original nanobubbles (10¹¹ bubbles/mL) have long in vitro persistence (7.3 ± 3.3 min) and circulation time in rats (approximately 10 min) under exposures in this study, and both degraded with dilutions. With all three tested dilutions (1:1, 1:10 and 1:100), successful BBB opening was reliably achieved under real-time feedback control.

DCE-MR imaging of orbital lesions: diagnostic performance of the tumor flow residence time τ calculated by a multi-compartmental pharmacokinetic tumor model based on individual factors

BACKGROUND

Differentiating benign from malignant orbital lesions by imaging and clinical presentation can be challenging.

PURPOSE

To differentiate benign from malignant orbital masses using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on tumor flow residence time τ calculated with the aid of a pharmacokinetic tumor model.

MATERIAL AND METHODS

Sixty patients with orbital masses were investigated by 3-T MRI including dynamic sequences. The signal intensity-time curve after i.v. contrast medium administration within lesions was approximated by Gd-concentration profiles on the basis of model calculations where the tumor is embedded in a whole-body kinetic model. One output of the model was tumor flow residence time τ, defined as the ratio of the tumor volume and the tumor blood flow rate. Receiver operating characteristic (ROC) curves were used to analyze the diagnostic performance of τ. The results were compared with those of K^trans, k_ep, v_e, iAUC, and ADC.

RESULTS

Thirty-one benign and 29 malignant orbital masses were identified (reference standard: histopathology, clinical characteristics). Mean τ was significantly longer for benign masses (94 ± 48 s) than for malignant masses (21 ± 19 s, P < 0.001). ROC analysis revealed the highest area under the curve (AUC = 0.94) for τ in orbital masses compared to standard methods.

CONCLUSION

Tumor flow residence times τ of benign and malignant orbital masses are valuable in the diagnostic work-up of orbital tumors. Measures of diagnostic accuracy were superior for τ compared to ADC, K^trans, v_e, and iAUC.

Computer-aided diagnosis of liver tumors on computed tomography images

BACKGROUND AND OBJECTIVE

Liver cancer is the tenth most common cancer in the USA, and its incidence has been increasing for several decades. Early detection, diagnosis, and treatment of the disease are very important. Computed tomography (CT) is one of the most common and robust imaging techniques for the detection of liver cancer. CT scanners can provide multiple-phase sequential scans of the whole liver. In this study, we proposed a computer-aided diagnosis (CAD) system to diagnose liver cancer using the features of tumors obtained from multiphase CT images.

METHODS

A total of 71 histologically-proven liver tumors including 49 benign and 22 malignant lesions were evaluated with the proposed CAD system to evaluate its performance. Tumors were identified by the user and then segmented using a region growing algorithm. After tumor segmentation, three kinds of features were obtained for each tumor, including texture, shape, and kinetic curve. The texture was quantified using 3 dimensional (3-D) texture data of the tumor based on the grey level co-occurrence matrix (GLCM). Compactness, margin, and an elliptic model were used to describe the 3-D shape of the tumor. The kinetic curve was established from each phase of tumor and represented as variations in density between each phase. Backward elimination was used to select the best combination of features, and binary logistic regression analysis was used to classify the tumors with leave-one-out cross validation.

RESULTS

The accuracy and sensitivity for the texture were 71.82% and 68.18%, respectively, which were better than for the shape and kinetic curve under closed specificity. Combining all of the features achieved the highest accuracy (58/71, 81.69%), sensitivity (18/22, 81.82%), and specificity (40/49, 81.63%). The Az value of combining all features was 0.8713.

CONCLUSIONS

Combining texture, shape, and kinetic curve features may be able to differentiate benign from malignant tumors in the liver using our proposed CAD system.