Evaluation of spectral photon counting computed tomography K-edge imaging for determination of gold nanoparticle biodistribution in vivo.

Spectral photon counting computed tomography (SPCCT) is an emerging medical imaging technology. SPCCT scanners record the energy of incident photons, which allows specific detection of contrast agents due to measurement of their characteristic X-ray attenuation profiles. This approach is known as K-edge imaging. Nanoparticles formed from elements such as gold, bismuth or ytterbium have been reported as potential contrast agents for SPCCT imaging. Furthermore, gold nanoparticles have many applications in medicine, such as adjuvants for radiotherapy and photothermal ablation. In particular, longitudinal imaging of the biodistribution of nanoparticles would be highly attractive for their clinical translation. We therefore studied the capabilities of a novel SPCCT scanner to quantify the biodistribution of gold nanoparticles in vivo. PEGylated gold nanoparticles were used. Phantom imaging showed that concentrations measured on gold images correlated well with known concentrations (slope = 0.94, intercept = 0.18, RMSE = 0.18, R2 = 0.99). The SPCCT system allowed repetitive and quick acquisitions in vivo, and follow-up of changes in the AuNP biodistribution over time. Measurements performed on gold images correlated with the inductively coupled plasma-optical emission spectrometry (ICP-OES) measurements in the organs of interest (slope = 0.77, intercept = 0.47, RMSE = 0.72, R2 = 0.93). TEM results were in agreement with the imaging and ICP-OES in that much higher concentrations of AuNPs were observed in the liver, spleen, bone marrow and lymph nodes (mainly in macrophages). In conclusion, we found that SPCCT can be used for repetitive and non-invasive determination of the biodistribution of gold nanoparticles in vivo.

Breast-density measurement using photon-counting spectral mammography.

PURPOSE: To evaluate a method for measuring breast density using photon-counting spectral mammography. Breast density is an indicator of breast cancer risk and diagnostic accuracy in mammography, and can be used as input to personalized screening, treatment monitoring and dose estimation. METHODS: The measurement method employs the spectral difference in x-ray attenuation between adipose and fibro-glandular tissue, and does not rely on any a priori information. The method was evaluated using phantom measurements on tissue-equivalent material (slabs and breast-shaped phantoms) and using clinical data from a screening population (n=1329). A state-of-the-art nonspectral method for breast-density assessment was used for benchmarking. RESULTS: The precision of the spectral method was estimated to be 1.5-1.8 percentage points (pp) breast density. Expected correlations were observed in the screening population for thickness versus breast density, dense volume, breast volume, and compression height. Densities ranged between 4.5% and 99.6%, and exhibited a skewed distribution with a mode of 12.5%, a median of 18.3%, and a mean of 23.7%. The precision of the nonspectral method was estimated to be 2.7-2.8 pp. The major uncertainty of the nonspectral method originated from the thickness estimate, and in particular thin/dense breasts posed problems compared to the spectral method. CONCLUSIONS: The spectral method yielded reasonable results in a screening population with a precision approximately two times that of the nonspectral method, which may improve or enable applications of breast-density measurement on an individual basis such as treatment monitoring and personalized screening.

Characterization of Cystic Lesions by Spectral Mammography: Results of a Clinical Pilot Study.

OBJECTIVES: Round lesions are a common mammographic finding, which can contribute more than 20% of overall recalls at screening. Discrimination of cystic fluid from solid tissue by spectral x-ray imaging has been demonstrated in specimen experiments. This work translates these results into a clinical pilot study to investigate the feasibility of discriminating cystic from solid lesions using spectral mammography. MATERIALS AND METHODS: Women undergoing mammography as part of their routine diagnostic workup were consented for analysis of spectral information obtained from a photon-counting mammography system. Images were analyzed retrospectively after diagnosis was confirmed with ultrasound and pathology. Well-defined solitary lesions were delineated independently by 3 expert radiologists. A breast lesion model is generated from the spectral mammography data using the energy-dependent x-ray attenuation of cyst fluid, carcinoma, and adipose and glandular tissue. From the breast lesion model, 2 spectral features are computed and combined in a 2-feature discrimination algorithm, which is evaluated in an analysis of the receiver operating characteristic curve for the task of identifying solid lesions ("positive result"). Expected outcomes on a screening population are extrapolated from this pilot study by cross-validation with bootstrapping using a 95% confidence interval (CI). RESULTS: The 2-feature discrimination algorithm was evaluated on the set of 119 eligible lesions (62 solids, 57 cysts) of diameter greater than 10 mm. The area under the receiver operating characteristic curve (AUC) was 0.88 with a specificity of 61% at the 99% sensitivity level on average over all expert radiologists. Cross-validation with bootstrapping of the clinical data revealed an AUC of 0.89 (95% CI, 0.79-0.96) and a specificity of 56% (95% CI, 33%-78%) when operating the algorithm at the 99% sensitivity level. CONCLUSIONS: Discriminating cystic from solid lesions with spectral mammography demonstrates promising results with the potential to reduce mammographic recalls. It is estimated that for each missed cancer at least 625 cystic lesions would have been correctly identified and hence would not have been needed to be recalled. Our results justify undertaking a larger reader study to refine the algorithm and determine clinically relevant thresholds to allow safe classification of cystic lesions by spectral mammography.