Search

Pulmonary embolism (PE), along with the closely related condition of deep vein thrombosis, affect an estimated 600,000 patients in the US per year. Untreated, PE carries a mortality rate of 30%. Because many patients experience mild or non-specific symptoms, imaging studies are necessary for definitive diagnosis of PE. Iodinated CT pulmonary angiography (CTPA) is recommended for most patients, while nuclear medicine-based ventilation/perfusion (V/Q) scans are reserved for patients in whom the use of iodine is contraindicated. Magnetic particle imaging (MPI) is an emerging tracer imaging modality with high image contrast (no tissue background signal) and sensitivity to superparamagnetic iron oxide (SPIO) tracer. Importantly, unlike CT or nuclear medicine, MPI uses no ionizing radiation. Further, MPI is not derived from magnetic resonance imaging (MRI); MPI directly images SPIO tracers via their strong electronic magnetization, enabling deep imaging of anatomy including within the lungs, which is very challenging with MRI. Here, the first high-contrast in vivo MPI lung perfusion images of rats are shown using a novel lung perfusion agent, MAA-SPIOs.

Cancer remains one of the leading causes of death worldwide. Biomedical imaging plays a crucial role in all phases of cancer management. Physicians often need to choose the ideal diagnostic imaging modality for each clinical presentation based on complex trade-offs between spatial resolution, sensitivity, contrast, access, cost, and safety. Magnetic particle imaging (MPI) is an emerging tracer imaging modality that detects superparamagnetic iron oxide (SPIO) nanoparticle tracer with high image contrast (zero tissue background signal), high sensitivity (200 nM Fe) with linear quantitation and zero signal depth attenuation. MPI is also safe in that it uses safe, in some cases even clinically approved tracers and no ionizing radiation. The superb contrast, sensitivity, safety, and ability to image anywhere in the body lends MPI great promise for cancer imaging. In this study, we show for the first time the use of MPI for in vivo cancer imaging with systemic tracer administration. Here, long circulating MPI-tailored SPIOs were created and administered intravenously in tumor bearing rats. The tumor was highlighted with tumor-to-background ratio of up to 50. The nanoparticle dynamics in the tumor was also well appreciated, with initial wash-in on the tumor rim, peak uptake at 6 hours, and eventual clearance beyond 48 hours. Lastly, we demonstrate the quantitative nature of MPI through compartmental fitting in vivo.

Despite advances in treatment, up to 30% of breast cancer patients experience disease recurrence accompanied by more aggressive disease and poorer prognosis. Treatment of breast cancer is complicated by the presence of multiple breast cancer subtypes, including: luminal, Her2 overexpressing, and aggressive basal-like breast cancers. Identifying new biomarkers specific to breast cancer subtypes could enhance the prediction of patient prognosis and contribute to improved treatment strategies. The microenvironment influences breast cancer progression through expression of growth factors, angiogenic factors and other soluble proteins. In particular, chemokine C-C ligand 2 (CCL2) regulates macrophage recruitment to primary tumors and signals to cancer cells to promote breast tumor progression. Here we employed a software-based approach to evaluate the prognostic significance of CCL2 protein expression in breast cancer subtypes in relation to its expression in the epithelium or stroma or in relation to fibroblast-specific protein 1 (Fsp1), a mesenchymal marker. Immunohistochemistry analysis of tissue microarrays revealed that CCL2 significantly correlated with Fsp1 expression in the stroma and tumor epithelium of invasive ductal carcinoma. In the overall cohort of invasive ductal carcinomas (n=427), CCL2 and Fsp1 expression in whole tissues, stroma and epithelium were inversely associated with cancer stage and tumor size. When factoring in molecular subtype, stromal CCL2 was observed to be most highly expressed in basal-like breast cancers. By Cox regression modeling, stromal CCL2, but not epithelial CCL2, expression was significantly associated with decreased recurrence-free survival. Furthermore, stromal CCL2 (HR=7.51 P=0.007) was associated with a greater hazard than cancer stage (HR=2.45, P=0.048) in multivariate analysis. These studies indicate that stromal CCL2 is associated with decreased recurrence-free survival in patients with basal-like breast cancer, with important implications on the use of stromal markers for predicting patient prognosis.

Magnetic Particle Imaging (MPI) is a novel non-invasive biomedical imaging modality that uses safe magnetite nanoparticles as tracers. Controlled synthesis of iron oxide nanoparticles (NPs) with tuned size-dependent magnetic relaxation properties is critical for the development of MPI. Additional functionalization of these NPs for other imaging modalities (e.g. MRI and fluorescent imaging) would accelerate screening of the MPI tracers based on their in vitro and in vivo performance in pre-clinical trials. Here, we conjugated two different types of poly-ethylene-glycols (NH2-PEG-NH2 and NH2-PEG-FMOC) to monodisperse carboxylated 19.7 nm NPs by amide bonding. Further, we labeled these NPs with Cy5.5 near infra-red fluorescent (NIRF) molecules. Bi-functional PEG (NH2-PEG-NH2) resulted in larger hydrodynamic size (∼98 nm vs. ∼43 nm) of the tracers, due to inter-particle crosslinking. Formation of such clusters impacted the multimodal imaging performance and pharmacokinetics of these tracers. We found that MPI signal intensity of the tracers in blood depends on their plasmatic clearance pharmacokinetics. Whole body mice MPI/MRI/NIRF, used to study the biodistribution of the injected NPs, showed primary distribution in liver and spleen. Biodistribution of tracers and their clearance pathway was further confirmed by MPI and NIRF signals from the excised organs where the Cy5.5 labeling enabled detailed anatomical mapping of the tracers.in tissue sections. These multimodal MPI tracers, combining the strengths of each imaging modality (e.g. resolution, tracer sensitivity and clinical use feasibility) pave the way for various in vitro and in vivo MPI applications.

Gastrointestinal (GI) bleeding causes more than 300,000 hospitalizations per year in the United States. Imaging plays a crucial role in accurately locating the source of the bleed for timely intervention. Magnetic Particle Imaging (MPI) is an emerging clinically translatable imaging modality that images superparamagnetic iron-oxide (SPIO) tracers with extraordinary contrast and sensitivity. This linearly quantitative modality has zero background tissue signal and zero signal depth attenuation. MPI is also safe: there is zero ionizing radiation exposure to the patient and clinically approved tracers can be used with MPI. In this study, we demonstrate the use of MPI along with long-circulating, PEG-stabilized SPIOs for rapid in vivo detection and quantification of GI bleed. A mouse model genetically predisposed to GI polyp development (ApcMin/+) was used for this study, and heparin was used as an anticoagulant to induce acute GI bleeding. We then injected MPI-tailored, long-circulating SPIOs through the tail vein, and tracked the tracer biodistribution over time using our custom-built high resolution field-free line (FFL) MPI scanner. Dynamic MPI projection images captured tracer accumulation in the lower GI tract with excellent contrast. Quantitative analysis of the MPI images show that the mice experienced GI bleed rates between 1 and 5 μL/min. Although there are currently no human scale MPI systems, and MPI-tailored SPIOs need to undergo further development and evaluation, clinical translation of the technique is achievable. The robust contrast, sensitivity, safety, ability to image anywhere in the body, along with long-circulating SPIOs lends MPI outstanding promise as a clinical diagnostic tool for GI bleeding.

Magnetic particle imaging (MPI) is an emerging tracer-based medical imaging modality that images non-radioactive, kidney-safe superparamagnetic iron oxide (SPIO) tracers. MPI offers quantitative, high-contrast and high-SNR images, so MPI has exceptional promise for applications such as cell tracking, angiography, brain perfusion, cancer detection, traumatic brain injury and pulmonary imaging. In assessing MPI's utility for applications mentioned above, it is important to be able to assess tracer short-term biodistribution as well as long-term clearance from the body. Here, we describe the biodistribution and clearance for two commonly used tracers in MPI: Ferucarbotran (Meito Sangyo Co., Japan) and LS-oo8 (LodeSpin Labs, Seattle, WA). We successfully demonstrate that 3D MPI is able to quantitatively assess short-term biodistribution, as well as long-term tracking and clearance of these tracers in vivo.

This paper investigates thermal ablation of cancer tissue as a controls problem. The objective is to deliver a lethal dose of thermal energy to the cancer tissue without damaging critical nearby healthy tissue. This paper proposes a model predictive controller (MPC) to regulate thermal dose. The model predictive controller guarantees the safety of the healthy tissue surrounding the tumor. The cost function rewards killing cancer cells. Simulations comparing the proposed controller with the bang-bang controller used in practice demonstrate that the MPC controller kills more of the cancer tissue and requires less time than the bang-bang controller, while damaging less healthy tissue.

An important criterion for identifying complicated objects with multiple attributes is the use of domain knowledge which reflects the precise spatial linking of the constituent attributes. Hence, simply detecting the presence of the low-level attributes that constitute the object, even in cases where these attributes might be detected in spatial proximity to each other is usually not a robust strategy. The O'Callaghan neighborhood is an ideal vehicle for characterizing objects comprised of multiple attributes spatially connected to each other in a precise fashion because it allows for modeling and imposing spatial distance and directional constraints on the object attributes. In this work we apply the O'Callaghan neighborhood to the problem of tubule identification on hematoxylin and eosin (H & E) stained breast cancer (BCa) histopathology, where a tubule is characterized by a central lumen surrounded by cytoplasm and a ring of nuclei around the cytoplasm. The detection of tubules is important because tubular density is an important predictor in cancer grade determination. In the context of ER+ BCa, grade has been shown to be strongly linked to disease aggressiveness and patient outcome. The more standard pattern recognition approaches to detection of complex objects typically involve training classifiers for low-level attributes individually. For tubule detection, the spatial proximity of lumen, cytoplasm, and nuclei might suggest the presence of a tubule. However such an approach could also suffer from false positive errors due to the presence of fat, stroma, and other lumen-like areas that could be mistaken for tubules. In this work, tubules are identified by imposing spatial and distance constraints using O'Callaghan neighborhoods between the ring of nuclei around each lumen. In this work, cancer nuclei in each image are found via a color deconvolution scheme, which isolates the hematoxylin stain, thereby enabling automated detection of individual cell nuclei. The potential lumen areas are segmented using a Hierarchical Normalized Cut (HNCut) initialized Color Gradient based Active Contour model (CGAC). The HNCut algorithm detects lumen-like areas within the image via pixel clustering across multiple image resolutions. The pixel clustering provides initial contours for the CGAC. From the initial contours, the CGAC evolves to segment the boundaries of the potential lumen areas. A set of 22 graph-based image features characterizing the spatial linking between the tubular attributes is extracted from the O'Callaghan neighborhood in order to distinguish true from false lumens. Evaluation on 1226 potential lumen areas from 14 patient studies produces an area under the receiver operating characteristic curve (AUC) of 0.91 along with the ability to classify true lumen with 86% accuracy. In comparison to manual grading of tubular density over 105 images, our method is able to distinguish histopathology images with low and high tubular density at 89% accuracy (AUC = 0.94).