One-Pot, One-Step Synthesis of Drug-Loaded Magnetic Multimicelle Aggregates.

Lipid-based formulations provide a nanotechnology platform that is widely used in a variety of biomedical applications because it has several advantageous properties including biocompatibility, reduced toxicity, relative ease of surface modifications, and the possibility for efficient loading of drugs, biologics, and nanoparticles. A combination of lipid-based formulations with magnetic nanoparticles such as iron oxide was shown to be highly advantageous in a growing number of applications including magnet-mediated drug delivery and image-guided therapy. Currently, lipid-based formulations are prepared by multistep protocols. Simplification of the current multistep procedures can lead to a number of important technological advantages including significantly decreased processing time, higher reaction yield, better product reproducibility, and improved quality. Here, we introduce a one-pot, single-step synthesis of drug-loaded magnetic multimicelle aggregates (MaMAs), which is based on controlled flow infusion of an iron oxide nanoparticle/lipid mixture into an aqueous drug solution under ultrasonication. Furthermore, we prepared molecular-targeted MaMAs by directional antibody conjugation through an Fc moiety using Cu-free click chemistry. Fluorescence imaging and quantification confirmed that antibody-conjugated MaMAs showed high cell-specific targeting that was enhanced by magnetic delivery.

Gaussian process classification of superparamagnetic relaxometry data: Phantom study.

MOTIVATION: Superparamagnetic relaxometry (SPMR) is an emerging technology that holds potential for use in early cancer detection. Measurement of the magnetic field after the excitation of cancer-bound superparamagnetic iron oxide nanoparticles (SPIONs) enables the reconstruction of SPIONs spatial distribution and hence tumor detection. However, image reconstruction often requires solving an ill-posed inverse problem that is computationally challenging and sensitive to measurement uncertainty. Moreover, an additional image processing module is required to automatically detect and localize the tumor in the reconstructed image. OBJECTIVE: Our goal is to examine the use of data-driven machine learning technique to detect a weak signal induced by a small cluster of SPIONs (surrogate tumor) in presence of background signal and measurement uncertainty. We aim to investigate the performance of both data-driven and image reconstruction models to characterize situations that one can replace the computationally-challenging reconstruction technique by the data-driven model. METHODS: We utilize Gaussian process (GP) classification model and a physics-based image reconstruction method, tailored to SPMR datasets that are obtained from (i) in silico simulations designed based on mouse cancer models and (ii) phantom experiments using MagSense system (Imagion Biosystems, Inc.). We investigate the performance of the GP classifier against the reconstruction technique, for different levels of measurement noise, different scenarios of SPIONs distribution, and different concentrations of SPIONs at the surrogate tumor. RESULTS: In our in silico source detection analysis, we were able to achieve high sensitivity results using GP model that outperformed the image reconstruction model for various choices of SPIONs concentration at the surrogate tumor and measurement noise levels. Moreover, in our phantom studies we were able to detect the surrogate tumor phantoms with 5% and 7.3% of the total used SPIONs, surrounded by 9 low-concentration phantoms with accuracies of 87.5% and 96.4%, respectively. CONCLUSIONS: The GP framework provides acceptable classification accuracies when dealing with in silico and phantom SPMR datasets and can outperform an image reconstruction method for binary classification of SPMR data.

The impact of x-ray tube stabilization on localized radiation dose in axial CT scans: initial results in CTDI phantoms.

Rise, fall, and stabilization of the x-ray tube output occur immediately before and after data acquisition on some computed tomography (CT) scanners and are believed to contribute additional dose to anatomy facing the x-ray tube when it powers on or off. In this study, we characterized the dose penalty caused by additional radiation exposure during the rise, stabilization, and/or fall time (referred to as overscanning). A 32 cm CT dose-index (CTDI) phantom was scanned on three CT scanners: GE Healthcare LightSpeed VCT, GE Healthcare Discovery CT750 HD, and Siemens Somatom Definition Flash. Radiation exposure was detected for various x-ray tube start acquisition angles using a 10 cm pencil ionization chamber placed in the peripheral chamber hole at the phantom's 12 o'clock position. Scan rotation time, ionization chamber location, phantom diameter, and phantom centering were varied to quantify their effects on the dose penalty caused by overscanning. For 1 s single, axial rotations, CTDI at the 12 o'clock chamber position (CTDI100, 12:00) was 6.1%, 4.0%, and 4.4% higher when the start angle of the x-ray tube was aligned at the top of the gantry (12 o'clock) versus when the start angle was aligned at 9 o'clock for the Siemens Flash, GE CT750 HD, and GE VCT scanner, respectively. For the scanners' fastest rotation times (0.285 s for the Siemens and 0.4 s for both GE scanners), the dose penalties increased to 22.3%, 10.7%, and 10.5%, respectively, suggesting a trade-off between rotation speed and the dose penalty from overscanning. In general, overscanning was shown to have a greater radiation dose impact for larger diameter phantoms, shorter rotation times, and to peripheral phantom locations. Future research is necessary to determine an appropriate method for incorporating the localized dose penalty from overscanning into standard dose metrics, as well as to assess the impact on organ dose.

Radiation dose reduction for CT lung cancer screening using ASIR and MBIR: a phantom study.

The purpose of this study was to reduce the radiation dosage associated with computed tomography (CT) lung cancer screening while maintaining overall diagnostic image quality and definition of ground-glass opacities (GGOs). A lung screening phantom and a multipurpose chest phantom were used to quantitatively assess the performance of two iterative image reconstruction algorithms (adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR)) used in conjunction with reduced tube currents relative to a standard clinical lung cancer screening protocol (51 effective mAs (3.9 mGy) and filtered back-projection (FBP) reconstruction). To further assess the algorithms' performances, qualitative image analysis was conducted (in the form of a reader study) using the multipurpose chest phantom, which was implanted with GGOs of two densities. Our quantitative image analysis indicated that tube current, and thus radiation dose, could be reduced by 40% or 80% from ASIR or MBIR, respectively, compared with conventional FBP, while maintaining similar image noise magnitude and contrast-to-noise ratio. The qualitative portion of our study, which assessed reader preference, yielded similar results, indicating that dose could be reduced by 60% (to 20 effective mAs (1.6 mGy)) with either ASIR or MBIR, while maintaining GGO definition. Additionally, the readers' preferences (as indicated by their ratings) regarding overall image quality were equal or better (for a given dose) when using ASIR or MBIR, compared with FBP. In conclusion, combining ASIR or MBIR with reduced tube current may allow for lower doses while maintaining overall diagnostic image quality, as well as GGO definition, during CT lung cancer screening.

Partial arc beam filtration: a novel approach to reducing CT breast radiation dose.

OBJECTIVE: We sought to assess the effectiveness of a novel CT radiation dose reduction strategy in which filtration was added at the x-ray tube output port between the x-ray beam and the breast area of three sizes of anthropomorphic phantoms. MATERIALS AND METHODS: To evaluate the dose-reduction potential of partial arc x-ray beam filtration, copper foil filtration or lead foil filtration was placed over CT scanners' covers when scanning anthropomorphic phantoms representative of a 5-year-old child, a 10-year-old child, and an adult female. Dose reduction was calculated as the percentage difference between the mean entrance radiation dose (on the phantoms' surfaces at locations representing the sternum and left breast) in unshielded scans compared with the mean dose in scans shielded by copper or lead foil. We also compared the CT numbers and noise sampled in regions representing the lung and the soft tissues near the sternum, left breast, and spine in CT images of the phantoms during unshielded scans relative to acquisitions shielded by copper or lead foil. RESULTS: Entrance dose at the sternum and left breast in the three anthropomorphic phantoms was reduced by 28-66% and 54-79% when using copper or lead foil filtration, respectively. However, copper foil filtration affected the CT numbers and noise in the CT images less than the lead foil filtration did (8.2% vs 32% mean increase in noise). CONCLUSION: By incorporating partial arc beam filtration into CT scanners, substantial dose reductions may be achieved with a minimal increase in image noise.

A comparison of methods to estimate organ doses in CT when utilizing approximations to the tube current modulation function.

PURPOSE: Most methods to estimate patient dose from computed tomography (CT) exams have been developed based on fixed tube current scans. However, in current clinical practice, many CT exams are performed using tube current modulation (TCM). Detailed information about the TCM function is difficult to obtain and therefore not easily integrated into patient dose estimate methods. The purpose of this study was to investigate the accuracy of organ dose estimates obtained using methods that approximate the TCM function using more readily available data compared to estimates obtained using the detailed description of the TCM function. METHODS: Twenty adult female models generated from actual patient thoracic CT exams and 20 pediatric female models generated from whole body PET∕CT exams were obtained with IRB (Institutional Review Board) approval. Detailed TCM function for each patient was obtained from projection data. Monte Carlo based models of each scanner and patient model were developed that incorporated the detailed TCM function for each patient model. Lungs and glandular breast tissue were identified in each patient model so that organ doses could be estimated from simulations. Three sets of simulations were performed: one using the original detailed TCM function (x, y, and z modulations), one using an approximation to the TCM function (only the z-axis or longitudinal modulation extracted from the image data), and the third was a fixed tube current simulation using a single tube current value which was equal to the average tube current over the entire exam. Differences from the reference (detailed TCM) method were calculated based on organ dose estimates. Pearson's correlation coefficients were calculated between methods after testing for normality. Equivalence test was performed to compare the equivalence limit between each method (longitudinal approximated TCM and fixed tube current method) and the detailed TCM method. Minimum equivalence limit was reported for each organ. RESULTS: Doses estimated using the longitudinal approximated TCM resulted in small differences from doses obtained using the detailed TCM function. The calculated root-mean-square errors (RMSE) for adult female chest simulations were 9% and 3% for breasts and lungs, respectively; for pediatric female chest and whole body simulations RMSE were 9% and 7% for breasts and 3% and 1% for lungs, respectively. Pearson's correlation coefficients were consistently high for the longitudinal approximated TCM method, ranging from 0.947 to 0.999, compared to the fixed tube current value ranging from 0.8099 to 0.9916. In addition, an equivalence test illustrated that across all models the longitudinal approximated TCM is equivalent to the detailed TCM function within up to 3% for lungs and breasts. CONCLUSIONS: While the best estimate of organ dose requires the detailed description of the TCM function for each patient, extracting these values can be difficult. The presented results show that an approximation using available data extracted from the DICOM header provides organ dose estimates with RMSE of less than 10%. On the other hand, the use of the overall average tube current as a single tube current value was shown to result in poor and inconsistent estimates of organ doses.

An empirical model of diagnostic x-ray attenuation under narrow-beam geometry.

PURPOSE: The purpose of this study was to develop and validate a mathematical model to describe narrow-beam attenuation of kilovoltage x-ray beams for the intended applications of half-value layer (HVL) and quarter-value layer (QVL) estimations, patient organ shielding, and computer modeling. METHODS: An empirical model, which uses the Lambert W function and represents a generalized Lambert-Beer law, was developed. To validate this model, transmission of diagnostic energy x-ray beams was measured over a wide range of attenuator thicknesses [0.49-33.03 mm Al on a computed tomography (CT) scanner, 0.09-1.93 mm Al on two mammography systems, and 0.1-0.45 mm Cu and 0.49-14.87 mm Al using general radiography]. Exposure measurements were acquired under narrow-beam geometry using standard methods, including the appropriate ionization chamber, for each radiographic system. Nonlinear regression was used to find the best-fit curve of the proposed Lambert W model to each measured transmission versus attenuator thickness data set. In addition to validating the Lambert W model, we also assessed the performance of two-point Lambert W interpolation compared to traditional methods for estimating the HVL and QVL [i.e., semi-logarithmic (exponential) and linear interpolation]. RESULTS: The Lambert W model was validated for modeling attenuation versus attenuator thickness with respect to the data collected in this study (R2 > 0.99). Furthermore, Lambert W interpolation was more accurate and less sensitive to the choice of interpolation points used to estimate the HVL and/or QVL than the traditional methods of semilogarithmic and linear interpolation. CONCLUSIONS: The proposed Lambert W model accurately describes attenuation of both monoenergetic radiation and (kilovoltage) polyenergetic beams (under narrow-beam geometry).

Precision of dosimetry-related measurements obtained on current multidetector computed tomography scanners.

PURPOSE: Computed tomography (CT) intrascanner and interscanner variability has not been well characterized. Thus, the purpose of this study was to examine the within-run, between-run, and between-scanner precision of physical dosimetry-related measurements collected over the course of 1 yr on three different makes and models of multidetector row CT (MDCT) scanners. METHODS: Physical measurements were collected using nine CT scanners (three scanners each of GE VCT, GE LightSpeed 16, and Siemens Sensation 64 CT). Measurements were made using various combinations of technical factors, including kVp, type of bowtie filter, and x-ray beam collimation, for several dosimetry-related quantities, including (a) free-in-air CT dose index (CTDI100,air); (b) calculated half-value layers and quarter-value layers; and (c) weighted CT dose index (CTDIW) calculated from exposure measurements collected in both a 16 and 32 cm diameter CTDI phantom. Data collection was repeated at several different time intervals, ranging from seconds (for CTDI100,air values) to weekly for 3 weeks and then quarterly or triannually for 1 yr. Precision of the data was quantified by the percent coefficient of variation (%CV). RESULTS: The maximum relative precision error (maximum %CV value) across all dosimetry metrics, time periods, and scanners included in this study was 4.33%. The median observed %CV values for CTDI100,air ranged from 0.05% to 0.19% over several seconds, 0.12%-0.52% over 1 week, and 0.58%-2.31% over 3-4 months. For CTDIW for a 16 and 32 cm CTDI phantom, respectively, the range of median %CVs was 0.38%-1.14% and 0.62%-1.23% in data gathered weekly for 3 weeks and 1.32%-2.79% and 0.84%-2.47% in data gathered quarterly or triannually for 1 yr. CONCLUSIONS: From a dosimetry perspective, the MDCT scanners tested in this study demonstrated a high degree of within-run, between-run, and between-scanner precision (with relative precision errors typically well under 5%).