Fifty-eighth annual meeting of the american association of physicists in medicine
SU-G-IeP3-07: High-Resolution, High-Sensitivity Imaging and Quantification of Intratumoral Distributions of Gold Nanoparticles Using a Benchtop L-Shell XRF Imaging System
To demonstrate the ability to perform high-resolution imaging and quantification of sparse distributions of gold nanoparticles (GNPs) within ex vivo tumor samples using a highly-sensitive benchtop L-shell x-ray fluorescence (XRF) imaging system.
An optimized L-shell XRF imaging system was assembled using a tungsten-target x-ray source (operated at 62 kVp and 45 mA). The x-rays were filtered (copper: 0.08 mm & aluminum: 0.04 mm) and collimated (lead: 5 cm thickness, 3 cm aperture diameter) into a cone-beam in order to irradiate small samples or objects. A collimated (stainless steel: 4 cm thickness, 2 mm aperture diameter) silicon drift detector, capable of 2D translation, was placed at 90° with respect to the beam to acquire XRF/scatter spectra from regions of interest. Spectral processing involved extracting XRF signal from background, followed by attenuation correction using a Compton scatter-based normalization algorithm. Calibration phantoms with water/GNPs (0 and 0.00001–10 mg/cm3) were used to determine the detection limit of the system at a 10-second acquisition time. The system was then used to map the distribution of GNPs within a 12×11×2 mm3 slice excised from the center of a GNP-loaded ex vivo murine tumor sample; a total of 110 voxels (2.65×10−3 cm3) were imaged with 1.3-mm spatial resolution.
The detection limit of the current cone-beam benchtop L-shell XRF system was 0.003 mg/cm3 (3 ppm). Intratumoral GNP concentrations ranging from 0.003 mg/cm3 (3 ppm) to a maximum of 0.055 mg/cm3 (55 ppm) and average of 0.0093 mg/cm3 (9.3 ppm) were imaged successfully within the ex vivo tumor slice.
The developed cone-beam benchtop L-shell XRF imaging system can immediately be used for imaging of ex vivo tumor samples containing low concentrations of GNPs. With minor finetuning/optimization, the system can be directly adapted for performing routine preclinical in vivo imaging tasks. Supported by NIH/NCI grant R01CA155446
This investigation was supported by NIH/NCI grant R01CA155446.