~60% of cancer patients undergo surgery to remove a primary tumor, and precise removal of all cancer cells is vital for optimal outcomes [1]. Microscopic clusters of cancer cells are intraoperatively hard to visualize and are often left behind, significantly increasing the risk of cancer recurrence. Radioguided surgery (RGS) is one technique that has been shown to help prevent this [2]. This method involves conjugating a gamma photon (V)-emitting radioisotope to a cancer-targeting molecule and administering this radioactive tracer intravenously to the patient before surgery where it preferentially binds to cancer cells. During surgery, a v-sensing probe is used to locate areas with y signal to remove all cancerous lesions, and since y's have superior tissue penetration properties compared to visible photons used in microscopy [3], deeper lesions can be sensed as well. State-of-the-art y probes show the benefit of RGS but lack the form-factor, configurability, and energy resolution that would allow for high-specificity detection, short acquisition times, and differentiation of lesion size and depth in tissue (e.g. similar y flux of large, deep lesions and shallow, small lesions), respectively [4]. This paper presents a y spectrometer ASIC in 180nm CMOS that: (1) is mm-scale, pixelated, and single y sensitive to allow for high-specificity detection, (2) has pixels that can be configured individually based on the incoming y flux and PVT variations to maximize detection area and minimize acquisition times, and (3) has an energy resolving scheme with configurable resolution and dynamic range to encode lesion depth in tissue (i.e. energy profiles of y's change with cm-scale tissue depth due to scattering [5]). Previous works [6]–[10] have shown promise in achieving energy-binning of radiation for dosimetry under high-flux X-ray beams, but suffer from few energy bins, low detection area per energy bin, and no configurability, incompatible with low-flux, wide-energy y-sensing applications such as RGS.
Abstract:
Publication date:
January 1, 2025
Publication type:
Conference Paper