Management of bilateral idiopathic renal hematuria in a dog with silver nitrate

Renal hematuria has limited treatment options. This report describes management of bilateral idiopathic renal hematuria in a dog with surgically assisted installation of 0.5% silver nitrate solution. Initial treatment resulted in freedom from clinical signs or recurrent anemia for 10 months; however, recurrence of bleeding following a nephrectomy resulted in euthanasia

Algorithm Development for Intrafraction Radiotherapy Beam Edge Verification from Cherenkov Imaging.

Imaging of Cherenkov light emission from patient tissue during fractionated radiotherapy has been shown to be a possible way to visualize beam delivery in real time. If this tool is advanced as a delivery verification methodology, then a sequence of image processing steps must be established to maximize accurate recovery of beam edges. This was analyzed and developed here, focusing on the noise characteristics and representative images from both phantoms and patients undergoing whole breast radiotherapy. The processing included temporally integrating video data into a single, composite summary image at each control point. Each image stack was also median filtered for denoising and ultimately thresholded into a binary image, and morphologic small hole removal was used. These processed images were used for day-to-day comparison computation, and either the Dice coefficient or the mean distance to conformity values can be used to analyze them. Systematic position shifts of the phantom up to 5 mm approached the observed variation values of the patient data. This processing algorithm can be used to analyze the variations seen in patients being treated concurrently with daily Cherenkov imaging to quantify the day-to-day disparities in delivery as a quality audit system for position/beam verification

Raman Spectroscopy Detects Distant Invasive Brain Cancer Cells Centimeters beyond MRI Capability in Humans

Surgical treatment of brain cancer is limited by the inability of current imaging capabilities such as magnetic resonance imaging (MRI) to detect the entirety of this locally invasive cancer. This results in residual cancer cells remaining following surgery, leading to recurrence and death. We demonstrate that intraoperative Raman spectroscopy can detect invasive cancer cells centimeters beyond pathological T1-contrast-enhanced and T2-weighted MRI signals. This intraoperative optical guide can be used to detect invasive cancer cells and minimize post-surgical cancer burden. The detection of distant invasive cancer cells beyond MRI signal has the potential to increase the effectiveness of surgery and directly lengthen patient survival

A phase field method for tomographic reconstruction from limited data.

Classical tomographic reconstruction methods fail for problems in which there is extreme temporal and spatial sparsity in the measured data. Reconstruction of coronal mass ejections (CMEs), a space weather phenomenon with potential negative effects on the Earth, is one such problem. However, the topological complexity of CMEs renders recent limited data reconstruction methods inapplicable. We propose an energy function, based on a phase field level set framework, for the joint segmentation and tomographic reconstruction of CMEs from measurements acquired by coronagraphs, a type of solar telescope. Our phase field model deals easily with complex topologies, and is more robust than classical methods when the data are very sparse. We use a fast variational algorithm that combines the finite element method with a trust region variant of Newton’s method to minimize the energy. We compare the results obtained with our model to classical regularized tomography for synthetic CME-like images

Improved Sensitivity to Fluorescence for Cancer Detection in Wide-Field Image-Guided Neurosurgery

In glioma surgery, Protoporphyrin IX (PpIX) fluorescence may identify residual tumor that could be resected while minimizing damage to normal brain. We demonstrate that improved sensitivity for wide-field spectroscopic fluorescence imaging is achieved with minimal disruption to the neurosurgical workflow using an electron-multiplying charge-coupled device (EMCCD) relative to a state-of-the-art CMOS system. In phantom experiments the EMCCD system can detect at least two orders-of-magnitude lower PpIX. Ex vivo tissue imaging on a rat glioma model demonstrates improved fluorescence contrast compared with neurosurgical fluorescence microscope technology, and the fluorescence detection is confirmed with measurements from a clinically-validated spectroscopic probe. Greater PpIX sensitivity in wide-field fluorescence imaging may improve the residual tumor detection during surgery with consequent impact on survival

Workflow for real-time in-vivo Cherenkov-excited luminescence imaging during radiotherapy

Radiotherapy is a common method for treating tumors, however, radiosensitivity can vary between tumor types or within the tumor microenvironment. The ability to deliver oxygen is crucial for the generation of reactive oxygen species resulting in increased localized cytotoxic effects. Alternatively, hypoxic tumors are thought to indicate a poor prognosis and may benefit from more aggressive treatments, yet identifying tumor hypoxia early in the course of a multi-week fractionated dose regimen is currently impractical. Using a time-gated imaging system and oxygen-sensitive phosphorescent compound (PtG4) we are able to estimate in vivo pO2 distribution at a rate of 2.6 estimates per second, which corresponds to 50+ values during a common 2Gy dose fraction. While our previous work has reported using Cherenkov-excited luminescence to estimate in vivo pO2 during external beam radiotherapy, the dose required was often greater than a standard fraction and camera acquisition parameters required modification during treatments, resulting in interrupted workflows. The current method utilizes custom control software which cycles through camera timing parameters during acquisition. Python code using the web-based user interface JupyterLab allows for interactive analysis of the resulting image stack without the need to pay expensive licensing fees for scientific computing packages. Using open source libraries, the analysis code is able to split the image stack into respective Cherenkov excitation and phosphorescence images, which can then be further automatically segmented to find regions of interest including the subject and phosphorescent region. The intensity of the regions in the phosphorescence images are used to estimate the compound lifetime, which can then be used in the Stern-Volmer relationship to estimate pO2. This entire process does not compromise clinical workflow and is able to provide a pO2 estimate within minutes after delivering the fractionated dose, providing clinicians early feedback about trends in tumor hypoxia. The current method has been validated with both direct injection of 50mM PtG4 in Matrigel in a mouse flank, and 24hrs post IV injection of mouse with MDA-MB-231 tumor implanted in the flank. The mouse with the direct injection was imaged under anesthesia and while awake and mobile to test the ability of the automated segmentation algorithm (Figure below). While the signal from the IV injection was less intense, simultaneous imaging using the previously reported method and current method resulted in similar lifetime estimates. While oxygen-sensitive PtG4 exhibits a lifetime between 16ms under atmospheric oxygen and 47ms when deprived of oxygen, other compounds have also been investigated. Europium chelate nanoparticle (~600ms), Iridium-based small molecules (~5ms), Si nanoparticles (~60ms), and UV-sensitive tattoo inks (~15ms) have all been imaged using Cherenkov-excitation. Camera time-gating can be utilized to discriminate these compound when mixed in the same field, allowing for additional tools in the realm of contrast enhancement during radiotherapy imaging. Ongoing studies with PtG4 and other compounds are being conducted to further improve system sensitivity and refine imaging workflows so they are more clinically translatable. Please click Additional Files below to see the full abstract

Fast Segmentation and High-Quality Three-Dimensional Volume Mesh Creation from Medical Images for Diffuse Optical Tomography

Multimodal approaches that combine near-infrared (NIR) and conventional imaging modalities have been shown to improve optical parameter estimation dramatically and thus represent a prevailing trend in NIR imaging. These approaches typically involve applying anatomical templates from magnetic resonance imaging/computed tomography/ultrasound images to guide the recovery of optical parameters. However, merging these data sets using current technology requires multiple software packages, substantial expertise, significant time-commitment, and often results in unacceptably poor mesh quality for optical image reconstruction, a reality that represents a significant roadblock for translational research of multimodal NIR imaging. This work addresses these challenges directly by introducing automated digital imaging and communications in medicine image stack segmentation and a new one-click three-dimensional mesh generator optimized for multimodal NIR imaging, and combining these capabilities into a single software package (available for free download) with a streamlined workflow. Image processing time and mesh quality benchmarks were examined for four common multimodal NIR use-cases (breast, brain, pancreas, and small animal) and were compared to a commercial image processing package. Applying these tools resulted in a fivefold decrease in image processing time and 62% improvement in minimum mesh quality, in the absence of extra mesh postprocessing. These capabilities represent a significant step toward enabling translational multimodal NIR research for both expert and nonexpert users in an open-source platform

A phase field method for tomographic reconstruction from limited data

Classical tomographic reconstruction methods fail for problems in which there is extreme temporal and spatial sparsity in the measured data. Reconstruction of coronal mass ejections (CMEs), a space weather phenomenon with potential negative effects on the Earth, is one such problem. However, the topological complexity of CMEs renders recent limited data reconstruction methods inapplicable. We propose an energy function, based on a phase field level set framework, for the joint segmentation and tomographic reconstruction of CMEs from measurements acquired by coronagraphs, a type of solar telescope. Our phase field model deals easily with complex topologies, and is more robust than classical methods when the data are very sparse. We use a fast variational algorithm that combines the finite element method with a trust region variant of Newton’s method to minimize the energy. We compare the results obtained with our model to classical regularized tomography for synthetic CME-like images

Pilot Study Assessment of Dynamic Vascular Changes in Breast Cancer with Near-Infrared Tomography from Prospectively Targeted Manipulations of Inspired End-Tidal Partial Pressure of Oxygen and Carbon Dioxide

The dynamic vascular changes in the breast resulting from manipulation of both inspired end-tidal partial pressure of oxygen and carbon dioxide were imaged using a 30 s per frame frequency-domain near-infrared spectral (NIRS) tomography system. By analyzing the images from five subjects with asymptomatic mammography under different inspired gas stimulation sequences, the mixture that maximized tissue vascular and oxygenation changes was established. These results indicate maximum changes in deoxy-hemoglobin, oxygen saturation, and total hemoglobin of 21, 9, and 3%, respectively. Using this inspired gas manipulation sequence, an individual case study of a subject with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC) was analyzed. Dynamic NIRS imaging was performed at different time points during treatment. The maximum tumor dynamic changes in deoxy-hemoglobin increased from less than 7% at cycle 1, day 5 (C1, D5) to 17% at (C1, D28), which indicated a complete response to NAC early during treatment and was subsequently confirmed pathologically at the time of surgery

Imaging Targeted-Agent Binding In Vivo with Two Probes

An approach to quantitatively image targeted-agent binding rate in vivo is demonstrated with dual-probe injection of both targeted and nontargeted fluorescent dyes. Images of a binding rate constant are created that reveal lower than expected uptake of epidermal growth factor in an orthotopic xenograft pancreas tumor (2.3×10−5 s−1), as compared to the normal pancreas (3.4×10−5 s−1). This approach allows noninvasive assessment of tumor receptor targeting in vivo to determine the expected contrast, spatial localization, and efficacy in therapeutic agent delivery. Targeting therapeutic drugs to tumors based on their overexpression of cellular receptors is widely researched and has important clinical success.1, 2 Yet there are essentially no good tools to assess the in vivo receptor expression contrast between tumor as compared to normal surrounding tissue.3, 4 In tumors with very high molecular signaling such as in the pancreas,4, 5 it is not obvious when a particular receptor is actually up-regulated as compared to the surrounding normal tissue versus upregulated without biopsy. Imaging of receptor status in vivo is problematic, because the majority of any targeted agent in vivo is often not cell-associated yet. Thus, any single image simply provides a measure of the whole tissue concentration rather than the bound concentration. Delivery from the vascular supply to tumor cells requires transvascular leakage, followed by diffusion through the interstitial space, and binding to the targeted receptor followed by possible internalization.6 As such, imaging concentration values in vivo usually do not provide information about binding,7 since most of the agent is in the interstitial space. In this work, we demonstrate a new methodology for quantitative imaging of effective binding rate in vivo, using the difference in fluorescence signal between a targeted and untargeted agent. We use this to demonstrate that a tumor known to have high EGFR expression in vitro 5 actually has lower EGF activity than the surrounding normal pancreas in vivo. Most contrast agent imaging has been interpreted with a simple pharmacokinetic model that is designed with as few compartments and rate constants as possible to not overinterpret the data. A three compartment model [Fig. 1 ] can be used effectively to model targeted agent delivery in the tumor, which includes compartments for 1. the concentration of drug in the plasma within the vasculature, 2. the concentration in the interstitial space of the tissue, and 3. the cellular-associated fraction of drug.7 The dominant fast rates in this model are transvascular delivery of contrast agent through rate constantK12 role= presentation \u3eK12 , and then cell-associating rate constant due to binding and uptake, K23 role= presentation \u3eK23 . The dominant clearance from the plasma is given by excretion mechanisms, such as those in the liver and kidneys, through rate constant Ke role= presentation \u3eKe . Then the slowest rates tend to be those involved in backflow from the interstitial space to the vasculature K21 role= presentation \u3eK21 , and from the cell-associated space to the interstitial space K32 role= presentation \u3eK32 . Each of these is shown in the illustration of the model in Fig. 1