By mapping the distribution of targeted plasmonic nanoparticles (NPs), photoacoustic (PA) imaging offers the potential to detect the pathologies in the early stages. However, optical absorption of the endogenous chromophores in the background tissue significantly reduces the contrast resolution of photoacoustic imaging. Previously, we introduced MPA imaging – a synergistic combination of magneto-motive ultrasound (MMUS) and PA imaging, and demonstrated MPA contrast enhancement using cell culture studies. In the current study, contrast enhancement was investigated in vivo using the magneto-photo-acoustic (MPA) imaging augmented with dual-contrast nanoparticles. Liposomal nanoparticles (LNPs) possessing both optical absorption and magnetic properties were injected into a murine tumor model. First, photoacoustic signals were generated from both the endogenous absorbers in the tissue and the liposomal nanoparticles in the tumor. Then, given significant differences in magnetic properties of tissue and LNPs, the magnetic response of LNPs (i.e. MMUS signal) was utilized to suppress the unwanted PA signals from the background tissue and thus improves the PA imaging contrast. In this study, we demonstrated the 3D MPA image of LNP-labeled xenografted tumor in a live animal. Compared to conventional PA imaging, the MPA images show significantly enhanced contrast between the nanoparticle-labeled tumor and the background tissue. Our results suggest the feasibility of MPA for high contrast in vivo mapping of dual-contrast nanoparticles.

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Electrode positions and timing delays influence the efficacy of biventricular pacing (BVP). Accordingly, this study focuses on BVP optimization, using a detailed 3-D electrophysiological model of the human heart, which is adapted to patient-specific anatomy and pathophysiology. The research is effectuated on ten heart models with left bundle branch block and myocardial infarction derived from magnetic resonance and computed tomography data. Cardiac electrical activity is simulated with the ten Tusscher cell model and adaptive cellular automaton at physiological and pathological conduction levels. The optimization methods are based on a comparison between the electrical response of the healthy and diseased heart models, measured in terms of root mean square error (E(RMS)) of the excitation front and the QRS duration error (E(QRS)). Intra- and intermethod associations of the pacing electrodes and timing delays variables were analyzed with statistical methods, i.e., t -test for dependent data, one-way analysis of variance for electrode pairs, and Pearson model for equivalent parameters from the two optimization methods. The results indicate that lateral the left ventricle and the upper or middle septal area are frequently (60% of cases) the optimal positions of the left and right electrodes, respectively. Statistical analysis proves that the two optimization methods are in good agreement. In conclusion, a noninvasive preoperative BVP optimization strategy based on computer simulations can be used to identify the most beneficial patient-specific electrode configuration and timing delays.

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