Search

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.

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.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been correlated with prognosis in head and neck squamous cell carcinoma as well as with changes in normal tissues. These studies implement different software, either commercial or in-house, and different scan protocols. Thus, the generalizability of the results is not confirmed. To assist in the standardization of quantitative metrics to confirm the generalizability of these previous studies, this data descriptor delineates in detail the DCE-MRI digital imaging and communications in medicine (DICOM) files with DICOM radiation therapy (RT) structure sets and digital reference objects (DROs), as well as, relevant clinical data that encompass a data set that can be used by all software for comparing quantitative metrics. Variable flip angle (VFA) with six flip angles and DCE-MRI scans with a temporal resolution of 5.5 s were acquired in the axial direction on a 3T MR scanner with a field of view of 25.6 cm, slice thickness of 4 mm, and 256×256 matrix size.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides quantitative metrics (e.g. Ktrans, ve) via pharmacokinetic models. We tested inter-algorithm variability in these quantitative metrics with 11 published DCE-MRI algorithms, all implementing Tofts-Kermode or extended Tofts pharmacokinetic models. Digital reference objects (DROs) with known Ktrans and ve values were used to assess performance at varying noise levels. Additionally, DCE-MRI data from 15 head and neck squamous cell carcinoma patients over 3 time-points during chemoradiotherapy were used to ascertain Ktrans and ve kinetic trends across algorithms. Algorithms performed well (less than 3% average error) when no noise was present in the DRO. With noise, 87% of Ktrans and 84% of ve algorithm-DRO combinations were generally in the correct order. Low Krippendorff’s alpha values showed that algorithms could not consistently classify patients as above or below the median for a given algorithm at each time point or for differences in values between time points. A majority of the algorithms produced a significant Spearman correlation in ve of the primary gross tumor volume with time. Algorithmic differences in Ktrans and ve values over time indicate limitations in combining/comparing data from distinct DCE-MRI model implementations. Careful cross-algorithm quality-assurance must be utilized as DCE-MRI results may not be interpretable using differing software.

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. PACS numbers: 87.57.Q‐, 87.57.nf

Superparamagnetic relaxometry (SPMR) exploits the unique magnetic properties of targeted superparamagnetic iron oxide nanoparticles (SPIOs) to detect small numbers of cancer cells. Reconstruction of the spatial distribution of cancer-bound nanoparticles requires solving an ill-posed inverse problem. The current method, multiple source analysis (MSA), uses a least-squares fit to determine the strength and location of a pre-determined number of magnetic dipoles. In this proof-of-concept study, we propose the application of a sparsity averaged reweighting algorithm (SARA) for volumetric reconstruction of immobilized nanoparticle distributions. We first calibrate the parameters that define the location of the sensors in the forward model of measurement physics. Using this optimized model, we evaluated the performance of the algorithms on various configurations of single and multiple point-source phantoms. We investigated the effect of the data fidelity parameter, voxel size, and iterative reweighting on the reconstruction produced by SARA. We found that the calibrated physics model can predict the detected field values within 5% of the measured data. When only a single source was present, both algorithms were able to detect as little as 0.5 µg of immobilized particles. However, when two sources were measured simultaneously, MSA failed to detect sources containing as much as 10 µg of particles, while SARA detected all of the sources containing at least 5 µg of particles. We show that a suitable data fidelity parameter can be selected objectively, and the total magnitude and location of a point source reconstructed by SARA is not sensitive to voxel size. Detection and localization of multiple small clusters of nanoparticles is a crucial step in SPMR-based diagnostic applications. Our algorithm overcomes the need to know the number of dipoles before reconstruction and improves the sensitivity of the reconstruction when multiple sources are present.

Background: Iron oxide nanoparticles (NPs) have been used for a variety of in-vivo and ex-vivo applications within the biomedical sciences. Moreover, when intended for clinical in-vivo applications, NPs need to meet rigorous requirements to ensure safety as well as bio-functionality including blood circulation time and specificity for cellular targets. PrecisonMRX® NPs are extensively characterized superparamagnetic NPs composed of a 25nm magnetite cores that are currently employed in a variety of in-vivo applications including non-invasive/in vivo diagnosis of cancer, Magnetic Particle Imaging, MRI, and magnetic hyperthermia. Objective: Here we report on the extensive pre-clinical development and functionality of antibody (Herceptin)-conjugated NPs for in-vivo and ex-vivo detection of HER2+ tumor cells by Magnetic Relaxometry (MRX). Results: We observed: 1) specific binding and detection of HER2 positive tumor cells in-vitro; 2) specific detection of HER2+ tumors in mice; 3) binding and amplitude of magnetic signal to be proportional to the level of HER2 expression in-vitro and in-vivo; 4) the nanoconstruct remains stable in circulation; 5) the particles do not induce a pro-inflammatory response nor activate complement; 6) the particles are biodegradable; and do not induce acute or delayed signs of morbidity in mice. Conclusion: Precision MRX® nanoparticles offer great clinical promise including the in- vivo detection of tumor cells by magnetic relaxometry. Given the stability and safety of these NPs, our pre-clinical results support progressing to clinical testing. A first-in human ex-vivo clinical research study design and strategy will be discussed. Citation Format: Bischoff FZ, Mathieu K, Vargas J, Pang L, Kulp AM, Dewing A, Liu X, Bast RC, Hazle J, Zhang M. Detection of HER2 positive tumor cells using functionalized iron oxide nanoparticles [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P6-01-05.

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 re...

The United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) recently reported a reduction in the average overall mortality among ovarian cancer patients screened with an annual sequential, multimodal strategy that tracked biomarker CA125 over time, where increasing serum CA125 levels prompted ultrasound. However, multiple cases were documented wherein serum CA125 levels were rising, but ultrasound screens were normal, thus delaying surgical intervention. A significant factor which could contribute to false negatives is that many aggressive ovarian cancers are believed to arise from epithelial cells on the fimbriae of the fallopian tubes, which are not readily imaged. Moreover, because only a fraction of metastatic tumors may reach a sonographically-detectable size before they metastasize, annual screening with ultrasound may fail to detect a large fraction of early-stage ovarian cancers. The ability to detect ovarian carcinomas before they metastasize is critical and future efforts towards improving screening should focus on identifying unique features specific to aggressive, early-stage tumors, as well as improving imaging sensitivity to allow for detection of tubal lesions. Implementation of a three-stage multimodal screening strategy in which a third modality is employed in cases where the first-line blood-based assay is positive and the second-line ultrasound exam is negative may also prove fruitful in detecting early-stage cases missed by ultrasound.

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.

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., semilogarithmic (exponential) and linear interpolation]. Results: The Lambert W model was validated for modeling attenuation versus attenuator thickness with respect to the datamore » collected in this study (R{sup 2} > 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).« less

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%).

e13019Background: Lymph node assessment following a primary HER2 positive breast cancer diagnosis often requires biopsy for pathologic evaluation to determine patients’ risk for metastatic disease ...

Introduction: Superparamagnetic relaxometry (SPMR) is an emerging technology that holds potential for use as a second-line screening modality to improve early cancer detection. During SPMR scanning, targeted superparamagnetic iron oxide nanoparticles (SPIONs) specifically bind to cancer cells and their spatial distribution can be characterized by measurement of the magnetic field relaxation following a brief excitation pulse. Highly sensitive superconducting quantum interference devices (SQUIDs) detect relaxation of clusters of SPIONs bound to small tumors. Challenges inherent to the SPMR technology include measurement noise, as well as the competing influence of SPION uptake by healthy organs (namely the liver), which also contributes to the overall SPMR signal. Hence, manual and stand-alone classification of the SPMR data into positive (i.e., the subject has cancer) or negative (i.e., the subject does not have cancer) screen results can be erroneous. Methods: We employed a data-driven approach based on Gaussian process (GP) formulation tailored to SPMR datasets to systematically quantify the probability of cancer. In silico, we simulated the SPION uptake process and generated SPMR signals that closely resembled experimental data collected in mouse models of cancer. We investigated the classification accuracy for different amounts of SPION accumulation within the tumor, as well as different levels of measurement noise (coefficient of variation (CV)). In a phantom study, a mouse liver was simulated by clustering together nine cotton swabs containing a total of 150 μg of immobilized SPIONs, while a mouse tumor was simulated by a single cotton swab containing either 9.4 μg or 14.4 μg of immobilized SPIONs. An additional nine cotton swabs containing 32.3 μg of immobilized SPIONs ( Results: Our in silico analysis for tumor accumulations of 3% and 5% of the injected SPION dose achieved 87% and 97% classification accuracies, respectively, when CV=0 and 75% and 93% when CV=0.015. Similarly, in our phantom study, classification accuracies of 87.5% and 96.4%, respectively, were reported for the 9.4 μg and 14.4 μg tumor phantoms. Conclusion: Using a data-driven GP model, tumor-status classification accuracies of up to 96.4% were achieved in SPMR phantom datasets. In the future, we plan to evaluate the accuracy of our classifier in preclinical settings using animal datasets. Citation Format: Javad Sovizi, Sara L. Thrower, David Fuentes, Wolfgang Stefan, John D. Hazle, Kelsey Mathieu. Binary classification of superparamagnetic relaxometry data for cancer screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 564. doi:10.1158/1538-7445.AM2017-564

Superparamagnetic relaxometry (SPMR) is an emerging technology that uses the unique magnetic properties of superparamagnetic iron oxide nanoparticles (SPIONs) to detect cancer cells. In order to estimate tumor locations from raw MRX data, we developed an L1 reconstruction algorithm under the assumption that early stage disease is sparsely distributed throughout the anatomy. The approach was previously validated in phantom datasets of known signal locations. Advantages of our method are that the solver does not require the user to input prior information regarding the expected number of tumors or their approximate locations. Additionally, the solver reconstructs a volumetric distribution of detected sources within the field of view. To validate the algorithm for use in preclinical settings, SPMR was performed on SKOV3 ovarian tumor bearing mice (n = 3) with the MRX device over time following an intratumoral injection of anti-Her2 antibody-conjugated 25nm SPIONs (Senior Scientific LLC). The SPMR data was reconstructed with our sparse solver and was found to be highly correlated (r = 0.9978) with the results generated by the commercial software that accompanies the MRX instrument (MSA). Additionally, segmentation of the reconstruction revealed a strong signal (2.0·106 pJ/T) in the area of the tumor and almost no signal in areas outside of the tumor (0.077 pJ/T) at four hours after injection. This result was consistent with our prior observations which have revealed that a large fraction of intratumorally-injected nanoparticles remain localized within the tumor for several hours after injection. Furthermore, these results were consistent with SPMR data collected by measuring tissue samples excised 24 hours after injection, of which the tumor had the highest signal. Thus, our sparse reconstruction algorithm was able to return the expected results without prior information regarding the location of nanoparticles. Future work will focus on quantifying the uncertainty in our reconstruction method, as well as characterizing its stability with increasingly complex nanoparticle distributions and detectability limits. In conclusion, this work represents an important advancement of the SPMR technology by allowing for volumetric reconstructions of bound nanoparticles from in vivo data. Citation Format: Sara L. Thrower, Kelsey Mathieu, Wolfgang Stefan, R. Romero Aburto, Zhen Lu, Robert C. Bast, Javad Sovizi, David Fuentes, John D. Hazle. Volumetric reconstruction of targeted nanoparticles for superparamagnetic relaxometry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 888. doi:10.1158/1538-7445.AM2017-888

Introduction: Most ovarian cancers are diagnosed in a late, incurable stage, which has prompted efforts towards earlier detection and more effective screening strategies. To be considered effective, screening must provide sufficient sensitivity and specificity to impact patient mortality while minimizing false positives. Magnetic relaxometry (MRX), which detects binding of targeted iron oxide nanoparticles (NPs) to cancer cells, offers the promise of improved sensitivity and specificity over conventional early detection modalities. Methods: We investigated the sensitivity and specificity of MRX by scanning ovarian cancer cell samples containing 105, 106, and 107 cells incubated with a fixed amount (57 µg Fe3O4) of anti-HER2 antibody-conjugated, PEG-coated NPs or unconjugated, PEG-coated NPs (Senior Scientific LLC). To further evaluate specificity, we used cell lines with both high and low expression of HER2 (SKOV3 and HEY, respectively). To assess MRX under in vivo conditions, we subcutaneously injected 105, 106, and 107 anti-HER2 NP-labeled SKOV3 cells into nude mice (n = 9) and immediately performed MRX scanning. Prior to performing this study, we verified successful antibody-NP conjugation through an ELISA assay, which confirmed the presence of anti-HER2 antibody in NP pellets. Additionally, we performed flow cytometry to confirm a high level of specific binding between SKOV3 cells and anti-HER2-conjugated NPs. Results: Our in vitro data revealed strong linearity between cell number and MRX signal for both SKOV3 and HEY cells incubated with anti-HER2 NPs (R2 = 0.99 and 1, respectively). Furthermore, there was little to no MRX signal for all cell samples incubated with unconjugated, PEG NPs regardless of cell number. The highest MRX signal was observed when 107 SKOV3 cells were incubated with anti-HER2 NPs, which was 2.1 ± 0.3 and 15.7 ± 1.4 times higher than when 107 Hey cells were incubated with anti-HER2 NPs or when 107 SKOV3 cells were incubated with unconjugated PEG NPs, respectively; significantly higher MRX signals (relative to the controls; p < .01) were also noted for samples containing 106 SKOV3 cells incubated with anti-HER2 NPs. When scanning live mice injected with 107 anti-HER2 NP-labeled SKOV3 cells, the MRX signal was significantly higher than the signal from the mice prior to the injection (p < .001). Additionally, MRX signal versus cell number in the injected mice was highly correlated (r = 0.99) with the MRX data from the corresponding cell sample scans. Conclusion: MRX is sufficiently sensitive to detect 1 million cells in culture or 10 million cells in mice with a high level of specificity. Sensitivity may be improved by using nanoparticles coated with antibodies against antigens that are overexpressed by a larger fraction of ovarian cancers and will be the focus of future work. Citation Format: Kelsey Mathieu, Zhen Lu, Hailing Yang, Lan Pang, Adam Kulp, John Hazle, Robert C. Bast. Feasibility of magnetic relaxometry for early ovarian cancer detection: preliminary evaluation of sensitivity and specificity in cell culture and in mice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1864. doi:10.1158/1538-7445.AM2017-1864

Magnetic relaxometry (MRX) has the potential to provide unprecedented sensitivity in early detection of cancer by sensing changes in magnetic relaxation of iron oxide (Fe3O4) nanoparticles targeted to cancer biomarkers and is expected to exceed the detection limits of established clinical modalities. MRX uses superconducting quantum interference device (SQUID) sensors to measure Neél relaxation of bound particles. Our strategy was to develop molecular specific Fe3O4 nanoparticles using amphiphilic functionalized phospholipids that allow for clinical translation of the MRX technology. To accomplish this, we used automated, controlled rate, direct infusion of an organic phase mixture of phospholipids and nanoparticles into water to produce monodisperse micellar nanoparticles with a mean diameter of 75±12 nm and surface charge of -10mV. The particles were determined to be stable in various biological media, including human plasma, for more than 24 hours with no detectable formation of a protein corona. Furthermore, in-vivo studies in healthy mice showed blood circulation times of more than 2 hours, as well as minimal MRX signals during this time. Additionally, we developed maleimide conjugation chemistry for epidermal growth factor receptor (EGFR) antibody attachment to micellar nanoparticles. We have achieved molecular specific labeling of cancer cells over-expressing EGFR. In the future, we will evaluate the MRX signal impact from injecting EGFR-conjugated nanoparticles into tumor-bearing mice. Citation Format: Rebeca Romero Aburto, Konstantin Sokolov, Adam M. Kulp, Erika C. Vreeland, Zhen Lu, Robert C. Bast, John D. Hazle, Kelsey B. Mathieu. Magnetic relaxometry detection of stealth, antibody-targeted micellar iron oxide nanoparticles in-vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 887. doi:10.1158/1538-7445.AM2017-887

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.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].The Lambert W model was validated for modeling attenuation versus attenuator thickness with respect to the data collected in this study (R20.99). 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.The proposed Lambert W model accurately describes attenuation of both monoenergetic radiation and (kilovoltage) polyenergetic beams (under narrow-beam geometry).

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.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).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.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%).

Purpose: To reduce radiation dose to patients undergoing computed tomography (CT) for lung cancer screening while maintaining overall diagnostic image quality and definition of ground-glass opacities (GGOs). Methods: A Catphan phantom, a Kyoto Kagaku lung screening phantom, and a Kyoto Kagaku multipurpose chest phantom were scanned on a GE Discovery CT750 HD scanner to quantitatively assess the performance of two 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 mAs and filtered back projection [FBP] reconstruction; CTDIvol = 3.9 mGy). 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 eight GGOs of two densities. Results: Our quantitative image analysis indicated that tube current, and thus radiation dose, could be reduced by at least 60% from MBIR while maintaining similar noise and spatial resolution (as determined from the CT images) compared with conventional FBP. The reader study indicated that dose could be reduced by 40% (to 30 mAs or 2.3 mGy) or 60% (to 20 mAs or 1.5 mGy) from using ASIR or MBIR, respectively, while maintaining GGO definition. Additionally, the readers ratings for overall image quality were equal or better (for a given dose) when using ASIR or MBIR compared with FBP. 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. Dr. Adam Chandler is an employee of GE Healthcare. Dr. Tinsu Pan is the owner of Texas Medical Imaging Consultants.

Purpose: To determine whether a dose penalty exists when scanning patients in axial scan mode on General Electric (GE)Computed Tomography(CT)scanners. A 10‐millisecond‐long rise of the x‐ray tube output occurs immediately before image acquisition and is suspected to contribute additional dose near the start angle of the x‐ray tube. This dose penalty may exist for axial scanning because the rise time occurs for every 360° rotation of the x‐ray tube, whereas rise time only occurs once in helical scan mode. Method and Materials: 10‐, 15‐, and 32‐cm CTDI phantoms were scanned on a GE VCT scanner for a single axial rotation in service mode. Exposure was measured at a constant peripheral location (12:00) and recorded for various x‐ray tube start angles. These methods were repeated on the 32‐cm phantom, changing its vertical position in 2‐cm increments from 6‐cm below to 12‐cm above iso‐center. Results: Exposure, measured at the 12:00 peripheral chamber position, was 5.5%, 1.8%, and 1.0% higher in the 32‐, 15‐, and 10‐cm phantom, respectively, when a start angle of 0° was used versus a start angle of 270° (this start angle was used for comparison to avoid attenuation from the patient table). The exposure penalty ranged from 2.9% to 12.7% when the 32‐cm phantom was 6‐cm below and 12‐cm above iso‐center, respectively. Conclusion: In light of the dose penalty observed in this study, axial acquisitions should feature a start angle of 180° to avoid imparting this penalty on superficial radiosensitive organs (i.e. breast, testes, and thyroid), which are near the anterior side of patients. Also, dose is not consistent around the periphery of a phantom scanned in axial mode and contiguous helical scanning (pitch = 1.0) provides a dose advantage over axial scanning (all other technique factors being equal).

Purpose: To share our experience implementing new strategies (as technology became available) to reduce Computed Tomography(CT) radiation dose to pediatric patients at a high‐volume cancer center housing 13 General Electric (GE) multidetector row CT (MDCT) scanners representing various models.Method and Materials: In October of 2008, tube current modulation (TCM) was implemented and protocols were created based on the display field of view parameter. Approximately 10 months later, the noise index for all protocols was increased by 2. Three months after increasing the noise index, the minimum and maximum tube current (mA) values were both dropped by 20% across all protocols. A chart review was used to identify eight pediatric patients (from 1 – 15 years old) that received chest CT scans before and after implementation of each of the three dose‐reduction strategies; within each patient, all technical parameters other than effective mAs were constant between scans. Results: The average effective mAs (across all eight patients) dropped from 103 mAs (49 – 171 mAs) prior to October 2008 to 75 mAs (42 – 109 mAs) after implementing all of three strategies. TCM alone reduced dose by an average of 19 %, increasing the noise index combined with TCM by 20% on average, and dropping the minimum and maximum mA values combined with the other two strategies by an average of 23%. All of the exams included in this study were of diagnostic quality with respect to the imaging task. Conclusion: To date, the combination of these efforts has led to an average reduction in dose of 23%. Additional approaches (adaptive statistical iterative reconstruction and organ shielding) are currently being evaluated and are expected to provide even greater dose‐savings to our pediatric patients.