Cardiomyopathy, a disorder affecting the heart muscle, is associated with severe symptoms, such as heart failure, arrhythmia, or cardiac arrest. Echocardiography is the primary clinical tool for myocardial assessment, which provides diagnostic and prognostic insights. However, current clinical parameters, such as myocardial strain, often detect the condition only after symptoms become pronounced, showing the need for early-stage assessment. Based on preliminary studies, intracardiac flow patterns have the potential to detect abnormal myocardial changes earlier than the current clinical parameters. Mouse models are commonly imaged with ultrasound in the cardiovascular disease research but their use for flow pattern evaluation has been hindered by spatial and temporal resolution limitations. Here, we address this limitation by conducting data acquisition using a Verasonics scanner with a range of high-frequency probes (16 to 42 MHz), transmit cycles, and transmit angles. A singular value decomposition (SVD) filter and frequency de-aliasing were also investigated to optimize flow quantification. Our findings show that a higher transmission frequency, while providing superior spatial resolution, may not yield the best flow quantification due to increased Doppler aliasing. Furthermore, SVD filtering and Doppler de-aliasing should be used with caution because improper implementation can adversely impact the quantification results.