The proposed research focuses on optimization of the electrodes used for bioimpedance based bladder volume estimation by employing the use of a digital twin derived from radiological data. The first aim involves selecting a subset of electrodes from the anterior matrix, evaluating individual electrodes based on the cumulative sensitivity of measurement frames they are involved in. The innovative geometric configuration method allowed for millions of possible configurations to be tested. The second aim characterized the trends of the bioimpedance signal as a function of relative change and absolute change over a wide range of bladder volumes. The trends were used to select measurement frames maximally sensitive to the bladder with highly linear changes to demonstrate a proof-of-concept lightweight estimation algorithm. The third and final aim is to establish bounds for the effects of low-grade ascites on the signal. Together, these aims provide the framework needed to create a wearable non-invasive bladder monitoring device that is capable of continuous monitoring.