The liver has a unique vascular supply, and triple-phase contrast-enhanced computed tomography examinations are being performed in order to characterize liver lesions. This study aimed to look for any associations between the attenuation values of liver lesions and their histological classification. The inclusion criteria for this retrospective study were focal or multifocal liver lesions and histological diagnosis. All of the dogs underwent pre-contrast and triple-phase postcontrast computed tomography (CT) examinations with identical timings of the postcontrast series. Thirty-one dogs were included in the study, and various benign and malignant pathologies were identified. The results did not identify any significant differences between the benign and malignant liver lesions, nor between the individual histological diagnoses. Inflammatory lesions were significantly different compared to the normal liver parenchyma, and significant hypoattenuation was found in the portal and delayed venous phases. Hemangiosarcomas were significantly hypoattenuating to the normal liver parenchyma in the pre-contrast and arterial phases, and also to all of the benign lesions in the arterial phase. The other pathologies showed variable attenuation patterns in the different postcontrast phases, and differentiation was not possible. On the basis of this study, triple-phase contrast-enhanced computed tomography cannot differentiate between benign and malignant liver lesions, and biopsy and further histological analysis are necessary.

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Structural intervention cardiology (SIC) interventions are crucial procedures for correcting heart valves, walls, and muscle form defects. However, the possibility of embolization or perforation, as well as the lack of transparent vision and autonomous surgical equipment, make it difficult for the clinician. This paper proposes a robot-assisted tendon-driven catheter and machine learning-based path planner to overcome these challenges. Firstly, an analytical inverse kinematic model is constructed to convert the tip location in the Cartesian space to the tendons' displacement. Then inverse reinforcement learning algorithm is employed to calculate the optimal path to avoid possible collisions between the catheter tip and the atrial wall. Moreover, a closed-loop feedback controller is adopted to improve positioning accuracy in a direct distal position measurement manner. Simulation and experiments are designed and conducted to demonstrate the feasibility and performance of the proposed system.

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