Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging.

OBJECTIVES: Quantum noise is a random process in X-ray-based imaging systems. We addressed and measured the uncertainty of radiomics features against this quantum noise in computed tomography (CT) images. METHODS: A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used. A solid tumor tissue removed from a male BALB/c mouse was included. We the placed phantom sets on the CT scanning table and repeated 20 acquisitions with identical imaging settings. Regions of interest were delineated for feature extraction. Statistical quantities-average, standard deviation, and percentage uncertainty-were calculated from these 20 repeated scans. Percentage uncertainty was used to measure and quantify feature stability against quantum noise. Twelve radiomics features were measured. Random noise was added to study the robustness of machine learning classifiers against feature uncertainty. RESULTS: We found the ranges of percentage uncertainties from homogeneous soft tissue phantoms, homogeneous bone phantoms, and solid tumor tissue to be 0.01-2138%, 0.02-15%, and 0.18-16%, respectively. Overall, it was found that the CT features ShortRunHighGrayLevelEmpha (SRHGE) (0.01-0.18%), ShortRunLowGrayLevelEmpha (SRLGE) (0.01-0.41%), LowGrayLevelRunEmpha (LGRE) (0.01-0.39%), and LongRunLowGrayLevelEmpha (LRLGE) (0.02-0.66%) were the most stable features against the inherent quantum noise. The most unstable features were cluster shade (1-2138%) and max probability (1-16%). The impact of random noise to the prediction accuracy by different machine learning classifiers was found to be between 0 and 12%. CONCLUSIONS: Twelve features were used for uncertainty measurements. The upper and lower bounds of percentage uncertainties were determined. The quantum noise effect on machine learning classifiers is model dependent. KEY POINTS: • Quantum noise is a random process and is intrinsic to X-ray-based imaging systems. This inherent quantum noise creates unpredictable fluctuations in the gray-level intensities of image pixels. Extra cautions and further validations are strongly recommended when unstable radiomics features are selected by a predictive model for disease classification or treatment outcome prognosis. • We addressed and used the statistical quantity of percentage uncertainty to measure the uncertainty of radiomics features against the inherent quantum noise in computed tomography (CT) images. • A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used in the stability measurement. A solid tumor tissue removed from a male BALB/c mouse was included in the heterogeneous sample.

Utility of CT Radiomics and Delta Radiomics for Survival Evaluation in Locally Advanced Nasopharyngeal Carcinoma with Concurrent Chemoradiotherapy.

BACKGROUND: A high incidence rate of nasopharyngeal carcinoma (NPC) has been observed in Southeast Asia compared to other parts of the world. Radiomics is a computational tool to predict outcomes and may be used as a prognostic biomarker for advanced NPC treated with concurrent chemoradiotherapy. Recently, radiomic analysis of the peripheral tumor microenvironment (TME), which is the region surrounding the gross tumor volume (GTV), has shown prognostic usefulness. In this study, not only was gross tumor volume (GTVt) analyzed but also tumor peripheral regions (GTVp) were explored in terms of the TME concept. Both radiomic features and delta radiomic features were analyzed using CT images acquired in a routine radiotherapy process. METHODS: A total of 50 patients with NPC stages III, IVA, and IVB were enrolled between September 2004 and February 2014. Survival models were built using Cox regression with clinical factors (i.e., gender, age, overall stage, T stage, N stage, and treatment dose) and radiomic features. Radiomic features were extracted from GTVt and GTVp. GTVp was created surrounding GTVt for TME consideration. Furthermore, delta radiomics, which is the longitudinal change in quantitative radiomic features, was utilized for analysis. Finally, C-index values were computed using leave-one-out cross-validation (LOOCV) to evaluate the performances of all prognosis models. RESULTS: Models were built for three different clinical outcomes, including overall survival (OS), local recurrence-free survival (LRFS), and progression-free survival (PFS). The range of the C-index in clinical factor models was (0.622, 0.729). All radiomics models, including delta radiomics models, were in the range of (0.718, 0.872). Among delta radiomics models, GTVt and GTVp were in the range of (0.833, 0.872) and (0.799, 0.834), respectively. CONCLUSIONS: Radiomic analysis on the proximal region surrounding the gross tumor volume of advanced NPC patients for survival outcome evaluation was investigated, and preliminary positive results were obtained. Radiomic models and delta radiomic models demonstrated performance that was either superior to or comparable with that of conventional clinical models.

Prognostic value of interim CT-based peritumoral and intratumoral radiomics in laryngeal and hypopharyngeal cancer patients undergoing definitive radiotherapy.

BACKGROUND AND PURPOSE: We aimed to investigate the prognostic value of peritumoral and intratumoral computed tomography (CT)-based radiomics during the course of radiotherapy (RT) in patients with laryngeal and hypopharyngeal cancer (LHC). MATERIALS AND METHODS: A total of 92 eligible patients were 1:1 randomly assigned into training and validation cohorts. Pre-RT and mid-RT radiomic features were extracted from pre-treatment and interim CT. LASSO-Cox regression was used for feature selection and model construction. Time-dependent area under the receiver operating curve (AUC) analysis was applied to evaluate the models' prognostic performances. Risk stratification ability on overall survival (OS) and progression-free survival (PFS) were assessed using the Kaplan-Meier method and Cox regression. The associations between radiomics and clinical parameters as well as circulating lymphocyte counts were also evaluated. RESULTS: The mid-RT peritumoral (AUC: 0.77) and intratumoral (AUC: 0.79) radiomic models yielded better performance for predicting OS than the pre-RT intratumoral model (AUC: 0.62) in validation cohort. This was confirmed by Kaplan-Meier analysis, in which risk stratification depended on the mid-RT peritumoral (p = 0.009) and intratumoral (p = 0.003) radiomics could be improved for OS, in comparison to the pre-RT intratumoral radiomics (p = 0.199). Multivariate analysis identified mid-RT peritumoral and intratumoral radiomic models as independent prognostic factors for both OS and PFS. Mid-RT peritumoral and intratumoral radiomics were correlated with treatment-related lymphopenia. CONCLUSION: Mid-RT peritumoral and intratumoral radiomic models are promising image biomarkers that could have clinical utility for predicting OS and PFS in patients with LHC treated with RT.

68Ga-PSMA-11 PET/CT Features Extracted from Different Radiomic Zones Predict Response to Androgen Deprivation Therapy in Patients with Advanced Prostate Cancer.

Purpose: Prediction of treatment response to androgen deprivation therapy (ADT) prior to treatment initiation remains difficult. This study was undertaken to investigate whether 68Ga-PSMA-11 PET/CT features extracted from different radiomic zones within the prostate gland might predict response to ADT in patients with advanced prostate cancer (PCa). Methods: A total of 35 patients with prostate adenocarcinoma underwent two 68Ga-PSMA-11 PET/CT scans­termed PET-1 and PET-2­before and after 3 months of ADT, respectively. The prostate was divided into three radiomic zones, with zone-1 being the metabolic tumor zone, zone-2 the proximal peripheral tumor zone, and zone-3 the extended peripheral tumor zone. Patients in the response group were those who showed a reduction ratio > 30% for PET-derived parameters measured at PET-1 and PET-2. The remaining patients were classified as non-responders. Results: Seven features (glcm_idmn, glcm_idn, glcm_imc1, ngtdm_Contrast, glrlm_rln, gldm_dn, and shape_MeshVolume) from zone-1, two features (gldm_sdlgle and shape_MinorAxisLength) from zone-2, and two features (diagnostics_Mask-interpolated_Minimum and shape_Sphericity) from zone-3 successfully distinguished responders from non-responders to ADT. One predictive feature (shape_SurfaceVolumeRatio) was consistently identified in all of the three zones. Conclusions: this study demonstrates the potential usefulness of radiomic features extracted from different prostatic zones in distinguishing responders from non-responders prior to ADT initiation.

Utility of radiomic zones for risk classification and clinical outcome predictions using supervised machine learning during simultaneous 11 C-choline PET/MRI acquisition in prostate cancer patients.

PURPOSE: In most radiomic studies related to cancer research, the traditional tumor-centric view has predominated. In this retrospective study, we go beyond the single-tumor region and investigate the utility of proposed radiomic zones for risk classification and clinical outcome predictions using radiomic features extracted from 11 C-choline positron emission tomography (PET) imaging and supervised machine learning in prostate tumors. MATERIALS AND METHODS: Seventy-seven prostate tumors were selected and delineated. The prostate organ was divided into three radiomic zones, with zone-1 being the metabolic tumor zone, zone-2 the proximal peripheral tumor zone, and zone-3 the extended peripheral tumor zone. LIFEx was used for PET-radiomic feature extraction. Risk groups were created using Gleason scores (GS), prostate-specific antigen (PSA) levels, clinical TNM staging, and progression-free survival (PFS). Random forest (RF) and AdaBoost advanced machine learning algorithms were used for supervised machine learning. Accuracy, positive predictive value, area under the receiver operating characteristic curve (AreaROC), and other metrics were calculated for comparisons of predictive performance between zones. RESULTS: For the GS risk classification group, the accuracies of risk classification predictions were 71%, 71%, and 67% using RF and 65%, 64%, and 63% using AdaBoost for zones -1, -2, and -3, respectively. For the PSA group, the accuracies of risk classification predictions were 74%, 65%, and 64% using RF and 76%, 66%, and 67% using AdaBoost for zones -1, -2, and -3, respectively. For the TNM group, the accuracies of risk classification predictions were 68%, 76%, and 78% using RF and 66%, 75%, and 80% using AdaBoost for zones -1, -2, and -3, respectively. For the PFS group, the accuracies of clinical outcome predictions were 77%, 75%, and 83% using RF and 77%, 74%, and 83% using AdaBoost in zones -1, -2, and -3, respectively. CONCLUSIONS: We proposed three radiomic zones with different standard uptake value characteristics and created four risk groups of prostate cancer patients for testing this idea. We showed that these radiomic zones have different predicting strengths in classifying risk groups and might allow us to identify a radiomic zone with higher accuracy for patient outcome prediction.

PLG inhibits Hippo signaling pathway through SRC in the hepatitis B virus-induced hepatocellular-carcinoma progression.

PURPOSE: Hepatitis B virus (HBV) infection is one main cause of hepatocellular carcinoma (HCC), but the mechanisms of pathogenesis still remain unclear. METHODS: We screened the 1351 differentially expressed genes related to HBV-induced HCC by bioinformatics analysis from databases and found that Plasminogen (PLG) may be a key gene in HBV-induced HCC progression. Then, we used a series of experiments in vivo and in vitro to explore the roles of PLG in HBV-HCC progression, such as qRT-PCR, western blot, ELISA, flow cytometry and TUNEL assay, subcutaneous xenografts and histopathological analysis to reveal the underlying mechanisms. RESULTS: PLG was over-expressed in HBV positive hepatocellular carcinoma tissues and cells. PLG silencing promoted HBV-HCC cell apoptosis in vitro and suppressed the growth of HBV-induced HCC xenografts in vivo both through inhibiting HBV replication. Then, GO and KEGG analysis of these differentially expressed genes revealed that the Hippo pathway was the key pathway involved in HBV-induced HCC, and SRC, a downstream target gene of PLG, was highly expressed in HBV-induced HCC and related to the Hippo pathway. Thus, we speculated that PLG promoted HBV-induced HCC progression through up-regulating and activating the expression of SRC and promoting Hippo signaling pathway function on HBV-HCC cell survival. CONCLUSION: Our study suggests PLG may be an activator of HBV-infected hepatocellular carcinoma development, as a novel prognostic biomarker and therapeutic target for HBV-HCC.

Extraction of gray-scale intensity distributions from micro computed tomography imaging for femoral cortical bone differentiation between low-magnesium and normal diets in a laboratory mouse model.

Previous studies have shown that the geometric development of femoral trabecular bone is affected by insufficient dietary intake of magnesium. However, it is not clear whether the development of femoral cortical bone can be quantitatively evaluated according to a diet with inadequate magnesium supplementation. Therefore, we used a micro computed tomography (CT) imaging approach with a laboratory mouse model to explore the potential application of texture analysis for the quantitative assessment of femoral cortical bones. C57BL/6J male mice were divided into two groups, where one group was fed a normal diet and the other group was fed a low-magnesium diet. We used a micro CT scanner for image acquisition, and the subsequent development of cortical bone was examined by texture analysis based on the statistical distribution of gray-scale intensities in which seven essential parameters were extracted from the micro CT images. Our calculations showed that the mean intensity increased by 7.20% (p = 0.000134), sigma decreased by 29.18% (p = 1.98E-12), skewness decreased by 19.52% (p = 0.0000205), kurtosis increased by 9.62% (p = 0.0877), energy increased by 24.19% (p = 3.32E-09), entropy decreased by 6.14% (p = 3.00E-10), and the Nakagami parameter increased by 104.32% (p = 4.13E-12) in the low-magnesium group when compared to the normal group. We found that the statistical parameters extracted from the gray-scale intensity distribution were able to differentiate between femoral cortical bone developments in the two different diet groups.

Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening.

Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clinical trials of lung cancer screening have shown that the false-positive rate is high and positive predictive value is low. To address these problems, a technical approach is greatly needed for accurate malignancy differentiation among these early-detected nodules. We studied the clinical feasibility of an additional protocol of localized thin-section CT for further assessment on recalled patients from lung cancer screening tests. Our approach of localized thin-section CT was integrated with radiomics features extraction and machine learning classification which was supervised by pathological diagnosis. Localized thin-section CT images of 122 nodules were retrospectively reviewed and 374 radiomics features were extracted. In this study, 48 nodules were benign and 74 malignant. There were nine patients with multiple nodules and four with synchronous multiple malignant nodules. Different machine learning classifiers with a stratified ten-fold cross-validation were used and repeated 100 times to evaluate classification accuracy. Of the image features extracted from the thin-section CT images, 238 (64%) were useful in differentiating between benign and malignant nodules. These useful features include CT density (p = 0.002 518), sigma (p = 0.002 781), uniformity (p = 0.032 41), and entropy (p = 0.006 685). The highest classification accuracy was 79% by the logistic classifier. The performance metrics of this logistic classification model was 0.80 for the positive predictive value, 0.36 for the false-positive rate, and 0.80 for the area under the receiver operating characteristic curve. Our approach of direct risk classification supervised by the pathological diagnosis with localized thin-section CT and radiomics feature extraction may support clinical physicians in determining truly malignant nodules and therefore reduce problems in lung cancer screening.

Visibility of microcalcification in cone beam breast CT: effects of X-ray tube voltage and radiation dose.

Mammography is the only technique currently used for detecting microcalcification (MC) clusters, an early indicator of breast cancer. However, mammographic images superimpose a three-dimensional compressed breast image onto two-dimensional projection views, resulting in overlapped anatomical breast structures that may obscure the detection and visualization of MCs. One possible solution to this problem is the use of cone beam computed tomography (CBCT) with a flat-panel (FP) digital detector. Although feasibility studies of CBCT techniques for breast imaging have yielded promising results, they have not shown how radiation dose and x-ray tube voltage affect the accuracy with which MCs are detected by CBCT experimentally. We therefore conducted a phantom study using a FP-based CBCT system with various mean glandular doses and kVp values. An experimental CBCT scanner was constructed with a data acquisition rate of 7.5 frames/s. 10.5 and 14.5 cm diameter breast phantoms made of gelatin were used to simulate uncompressed breasts consisting of 100% glandular tissue. Eight different MC sizes of calcium carbonate grains, ranging from 180-200 microm to 355-425 microm, were used to simulate MCs. MCs of the same size were arranged to form a 5 x 5 MC cluster and embedded in the breast phantoms. These MC clusters were positioned at 2.8 cm away from the center of the breast phantoms. The phantoms were imaged at 60, 80, and 100 kVp. With a single scan (360 degrees), 300 projection images were acquired with 0.5 x, 1x, and 2x mean glandular dose limit for 10.5 cm phantom and with 1x, 2x, and 4x for 14.5 cm phantom. A Feldkamp algorithm with a pure ramp filter was used for image reconstruction. The normalized noise level was calculated for each x-ray tube voltage and dose level. The image quality of the CBCT images was evaluated by counting the number of visible MCs for each MC cluster for various conditions. The average percentage of the visible MCs was computed and plotted as a function of the MGD, the kVp, and the average MC size. The results showed that the MC visibility increased with the MGD significantly but decreased with the breast size. The results also showed that the x-ray tube voltage affects the detection of MCs under different circumstances. With a 50% threshold, the minimum detectable MC sizes for the 10.5 cm phantom were 348(+/-2), 288(+/-7), 257(+/-2) microm at 3, 6, and 12 mGy, respectively. Those for the 14.5 cm phantom were 355 (+/-1), 307 (+/-7), 275 (+/-5) microm at 6, 12, and 24 mGy, respectively. With a 75% threshold, the minimum detectable MC sizes for the 10.5 cm phantom were 367 (+/-1), 316 (+/-7), 265 (+/-3) microm at 3, 6, and 12 mGy, respectively. Those for the 14.5 cm phantom were 377 (+/-3), 334 (+/-5), 300 (+/-2) microm at 6, 12, and 24 mGy, respectively.

Attenuating trabecular morphology associated with low magnesium diet evaluated using micro computed tomography.

OBJECTIVE: The literature shows that bone mineral density (BMD) and the geometric architecture of trabecular bone in the femur may be affected by inadequate dietary intake of Mg. In this study, we used microcomputed tomography (micro-CT) to characterize and quantify the impact of a low-Mg diet on femoral trabecular bones in mice. MATERIALS AND METHODS: Four-week-old C57BL/6J male mice were randomly assigned to 2 groups and supplied either a normal or low-Mg diet for 8weeks. Samples of plasma and urine were collected for biochemical analysis, and femur tissues were removed for micro-CT imaging. In addition to considering standard parameters, we regarded trabecular bone as a cylindrical rod and used computational algorithms for a technical assessment of the morphological characteristics of the bones. BMD (mg-HA/cm3) was obtained using a standard phantom. RESULTS: We observed a decline in the total tissue volume, bone volume, percent bone volume, fractal dimension, number of trabecular segments, number of connecting nodes, bone mineral content (mg-HA), and BMD, as well as an increase in the structural model index and surface-area-to-volume ratio in low-Mg mice. Subsequently, we examined the distributions of the trabecular segment length and radius, and a series of specific local maximums were identified. The biochemical analysis revealed a 43% (96%) decrease in Mg and a 40% (71%) decrease in Ca in plasma (urine excretion). CONCLUSIONS: This technical assessment performed using micro-CT revealed a lower population of femoral trabecular bones and a decrease in BMD at the distal metaphysis in the low-Mg mice. Examining the distributions of the length and radius of trabecular segments showed that the average length and radius of the trabecular segments in low-Mg mice are similar to those in normal mice.

Noise simulation in cone beam CT imaging with parallel computing.

We developed a computer noise simulation model for cone beam computed tomography imaging using a general purpose PC cluster. This model uses a mono-energetic x-ray approximation and allows us to investigate three primary performance components, specifically quantum noise, detector blurring and additive system noise. A parallel random number generator based on the Weyl sequence was implemented in the noise simulation and a visualization technique was accordingly developed to validate the quality of the parallel random number generator. In our computer simulation model, three-dimensional (3D) phantoms were mathematically modelled and used to create 450 analytical projections, which were then sampled into digital image data. Quantum noise was simulated and added to the analytical projection image data, which were then filtered to incorporate flat panel detector blurring. Additive system noise was generated and added to form the final projection images. The Feldkamp algorithm was implemented and used to reconstruct the 3D images of the phantoms. A 24 dual-Xeon PC cluster was used to compute the projections and reconstructed images in parallel with each CPU processing 10 projection views for a total of 450 views. Based on this computer simulation system, simulated cone beam CT images were generated for various phantoms and technique settings. Noise power spectra for the flat panel x-ray detector and reconstructed images were then computed to characterize the noise properties. As an example among the potential applications of our noise simulation model, we showed that images of low contrast objects can be produced and used for image quality evaluation.

X-ray imaging characterization of femoral bones in aging mice with osteopetrotic disorder.

Aging mice with a rare osteopetrotic disorder in which the entire space of femoral bones are filled with trabecular bones are used as our research platform. A complete study is conducted with a micro computed tomography (CT) system to characterize the bone abnormality. Technical assessment of femoral bones includes geometric structure, biomechanical strength, bone mineral density (BMD), and bone mineral content (BMC). Normal aging mice of similar ages are included for comparisons. In our imaging work, we model the trabecular bone as a cylindrical rod and new quantitative which are not previously discussed are developed for advanced analysis, including trabecular segment length, trabecular segment radius, connecting node number, and distribution of trabecular segment radius. We then identified a geometric characteristic in which there are local maximums (0.0049, 0.0119, and 0.0147 mm) in the structure of trabecular segment radius. Our calculations show 343% higher in percent trabecular bone volume at distal-metaphysis; 38% higher in cortical thickness at mid-diaphysis; 11% higher in cortical cross-sectional moment of inertia at mid-diaphysis; 42% higher in cortical thickness at femur neck; 26% higher in cortical cross-sectional moment of inertia at femur neck; 31% and 395% higher in trabecular BMD and BMC at distal-metaphysis; 17% and 27% higher in cortical BMD and BMC at distal-metaphysis; 9% and 53% higher in cortical BMD and BMC at mid-diaphysis; 25% and 64% higher in cortical BMD and BMC at femur neck. Our new quantitative parameters and findings may be extended to evaluate the treatment response for other similar bone disorders.

Geometric random inner products: a family of tests for random number generators.

We present a computational scheme, GRIP (geometric random inner products), for testing the quality of random number generators. The GRIP formalism utilizes geometric probability techniques to calculate the average scalar products of random vectors distributed in geometric objects, such as circles and spheres. We show that these average scalar products define a family of geometric constants which can be used to evaluate the quality of random number generators. We explicitly apply the GRIP tests to several random number generators frequently used in Monte Carlo simulations, and demonstrate a statistical property for good random number generators.

Fast Monte Carlo simulation of DNA damage induction by Auger-electron emission.

PURPOSE: A local damage model (LDM) was developed to estimate the biological efficiency of Auger-electron-emitting radionuclides. MATERIALS AND METHODS: The LDM required information on the local dose distribution, local energy spectrum, and clustered DNA damage yields in the cell nucleus. To apply the model, the nucleus was divided into concentric shells where each shell contributed its own local dose, energy spectrum, and damage yield. The local doses and energy spectra were computed using the PENELOPE (PENetration and Energy LOss of Positrons and Electrons) code. The DNA damage yields were estimated using the MCDS (Monte Carlo damage simulation) code. RESULTS: For a typical 4-µm radius mammalian cell nucleus, the absorbed doses to the cell nucleus per unit cumulated activity, equal to 0.0065, 0.00418, 0.0028, 0.0027 and 0.0015 Gy Bq(-1) s(-1) for (125)I, (119)Sb, (123)I, (111)In and (99m)Tc, were within 6% difference with the MIRD (Medical Internal Radiation Dose) published data. The simulated total (simple and complex) single-strand break (SSB) and double-strand break (DSB) yields were in the same order, i.e., (125)I > (119)Sb > (123)I > (111)In > (99m)Tc. The agreement between present results and literature data for the DNA damage yields was generally good. More than 75% of the total SSB and DSB yields were contributed from regions within 2.5 µm of the nucleus center. CONCLUSIONS: The proposed methodology was computationally efficient and could be applied to other irradiation geometries such as cell clusters.

Retention assessment of magnetic nanoparticles in rat arteries with micro-computed tomography.

Magnetic nanoparticles (MNPs) may serve as carriers for pharmacological agents to the target in a magnetic-force guiding system. It is essential to achieve effective retention of MNPs through the external magnet placement. However, it is difficult to estimate the retention efficiency of MNPs and validate the experimental strategies. Micro-CT was used to identify the spatial distribution of MNP retention and image analysis is then extended to evaluate the MNP delivery efficiency. Male Sprague Dawley rats were anesthetized to expose abdominal arteries with an NdFeB magnet of 4.9 kG placed by the left iliac artery. After a 20 min equilibrium period, arteries were ligated, removed and fixed in a paraformaldehyde solution. Experiments were performed with intravenous injection in our platform with two independent groups. MNPs were used in the first group, while chemical compounds of recombinant tissue plaminogen activator were attached to MNPs as rtPA (recombinant tissue plaminogen activator)-MNPs in the second group. Image analysis of micro-CT shows the average retention volume of MNPs and rtPA-MNPs in the left iliac arteries is 9.3 and 6.3 fold of that in the right. Large local aggregation of MNPs and rtPA-MNPs in the left iliac arteries is the consequence of external magnet placement, suggesting feasibility of magnetic targeting through the intravenous administration. We also determined that on average 0.57% and 0.064% of MNPs and rtPA-MNPs respectively were retained in the left iliac artery. It was estimated that the average rtPA concentration of 60.16 µg mL(-1) may be achieved with rtPA-MNPs. With the micro-CT imaging approach, we accomplished visualization of the aggregation of retained particles; reconstructed 3D distribution of relative retention; estimated the average particle number of local retention; determined efficiency of targeted delivery. In particular, our quantitative image assessment suggests that intravenous administration of rtPA-MNPs may retain local concentration of rtPA high enough to induce thrombolysis.

Manipulation of magnetic nanoparticle retention and hemodynamic consequences in microcirculation: assessment by laser speckle imaging.

Magnetic nanoparticles (MNPs) have been proposed for targeted or embolization therapeutics. How MNP retention occurs in circulation may critically determine local hemodynamics, tissue distribution of MNPs, and the therapeutic effects. We attempted to establish a microcirculation model to study the magnetic capture of MNPs in small vessels and to determine the factors affecting MNP retention. Two-dimensional hemodynamic changes in response to magnet-induced MNP retention in the microvessels of the cremaster muscle in vivo were observed in a real-time manner using a laser speckle imaging technique. Changes in tissue perfusion of the cremaster muscle appeared to be closely correlated with the location of the magnet placement underneath the muscle in response to intra-arterial administration of dextran-coated MNPs. Magnet-related retention was observed along the edge of the magnet, as corroborated by the results of histology analysis and microcomputed tomography. In these preparations, tissue iron content almost doubled, as revealed by inductively coupled plasma optical emission spectroscopy. In addition, MNP retention was associated with reduced downstream flow in a dose-dependent manner. Dissipation of MNPs (5 mg/kg) occurred shortly after removal of the magnet, which was associated with significant recovery of tissue flow. However, MNP dissipation did not easily occur after administration of a higher MNP dose (10 mg/kg) or prolonged exposure to the magnetic field. An ultrasound after removal of the magnet may induce the partial dispersion of MNPs and thus partially improve hemodynamics. In conclusion, our results revealed the important correlation of local MNP retention and hemodynamic changes in microcirculation, which can be crucial in the application of MNPs for effective targeted therapeutics.

Comparison of two detector systems for cone beam CT small animal imaging - a preliminary study.

To compare two detector systems - one based on the charge-coupled device (CCD) and image amplifier, the other based on a-Si/CsI flat panel, for cone beam computed-tomography (CT) imaging of small animals.A high resolution, high framing rate detector system for the cone beam CT imaging of small animals was developed. The system consists of a 2048x3072x12 bit CCD optically coupled to an image amplifier and an x-ray phosphor screen. The CCD has an intrinsic pixel size of 12 mum but the effective pixel size can be adjusted through the magnification adjustment of the optical coupling systems. The system is used in conjunction with an x-ray source and a rotating stage for holding and rotating the scanned object in the cone beam CT imaging experiments. The advantages of the system include but are not limited to the ability to adjust the effective pixel size and to achieve extremely high spatial resolution and temporal resolution. However, the need to use optical coupling compromises the detective quanta efficiency (DQE) of the system. In this paper, the imaging characteristics of the system were presented and compared with those of an a-Si/CsI flat-panel detector system.