Hepatic steatosis or fatty liver disease occurs when lipids accumulate within the liver and can lead to steatohepatitis, cirrhosis, liver cancer, and eventual liver failure requiring liver transplant. Conventional brightness mode (B-mode) ultrasound (US) is the most common noninvasive diagnostic imaging modality used to diagnose hepatic steatosis in clinics. However, it is mostly subjective or requires a reference organ such as the kidney or spleen with which to compare. This comparison can be problematic when the reference organ is diseased or absent. The current work presents an alternative approach to noninvasively detecting liver fat content using ultrasound-induced thermal strain imaging (US-TSI). This technique is based on the difference in the change in the speed of sound as a function of temperature between water- and lipid-based tissues. US-TSI was conducted using two system configurations including a mid-frequency scanner with a single linear array transducer (5-14 MHz) for both imaging and heating and a high-frequency (13-24 MHz) small animal imaging system combined with a separate custom-designed US heating transducer array. Fatty livers (n=10) with high fat content (45.6 ± 11.7%) from an obese mouse model and control livers (n=10) with low fat content (4.8± 2.9%) from wild-type mice were embedded in gelatin. Then, US imaging was performed before and after US induced heating. Heating time periods of ~3 s and ~9.2 s were used for the mid-frequency imaging and high-frequency imaging systems, respectively to induce temperature changes of approximately 1.5 °C. The apparent echo shifts that were induced as a result of sound speed change were estimated using 2D phase-sensitive speckle tracking. Following US-TSI, histology was performed to stain lipids and measure percentage fat in the mouse livers. Thermal strain measurements in fatty livers (−0.065±0.079%) were significantly (p<0.05) higher than those measured in control livers (−0.124±0.037%). Using histology as a gold standard to classify mouse livers, US-TSI had a sensitivity and specificity of 70% and 90%, respectively. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.775. This ex vivo study demonstrates the feasibility of using US-TSI to detect fatty livers and warrants further investigation of US-TSI as a diagnostic tool for hepatic steatosis.
Fatty liver disease; Obese mouse; Ultrasound thermal strain
Time-of-flight (TOF) information improves signal to noise ratio in Positron Emission Tomography (PET). Computation cost in processing TOF-PET sinograms is substantially higher than for nonTOF data because the data in each line of response is divided among multiple time of flight bins. This additional cost has motivated research into methods for rebinning TOF data into lower dimensional representations that exploit redundancies inherent in TOF data. We have previously developed approximate Fourier methods that rebin TOF data into either 3D nonTOF or 2D nonTOF formats. We refer to these methods respectively as FORET-3D and FORET-2D. Here we describe maximum a posteriori (MAP) estimators for use with FORET rebinned data. We first derive approximate expressions for the variance of the rebinned data. We then use these results to rescale the data so that the variance and mean are approximately equal allowing us to use the Poisson likelihood model for MAP reconstruction. MAP reconstruction from these rebinned data uses a system matrix in which the detector response model accounts for the effects of rebinning. Using these methods we compare performance of FORET-2D and 3D with TOF and nonTOF reconstructions using phantom and clinical data. Our phantom results show a small loss in contrast recovery at matched noise levels using FORET compared to reconstruction from the original TOF data. Clinical examples show FORET images that are qualitatively similar to those obtained from the original TOF-PET data but a small increase in variance at matched resolution. Reconstruction time is reduced by a factor of 5 and 30 using FORET3D+MAP and FORET2D+MAP respectively compared to 3D TOF MAP, which makes these methods attractive for clinical applications.
There are several emerging diagnostic and therapeutic applications of magnetic nanoparticles (mNPs) in medicine. This study examines the potential for developing an mNP imager that meets these emerging clinical needs with a low cost imaging solution that uses arrays of digitally controlled drive coils in a multiple-frequency, continuous-wave operating mode and compensated fluxgate magnetometers. The design approach is described and a mathematical model is developed to support measurement and imaging. A prototype is used to demonstrate active compensation of up to 185 times the primary applied magnetic field, depth sensitivity up to 2.5 cm (p < 0.01), and linearity over 5 dilutions (R2 > 0.98, p <0.001). System frequency responses show distinguishable readouts for iron oxide mNPs with single magnetic domain core diameters of 10 nm and 40 nm, and multi-domain mNPs with a hydrodynamic diameter of 100 nm. Tomographic images show a contrast-to-noise ratio of 23 for 0.5 ml of 12.5 mg Fe/ml mNPs at 1 cm depth. A demonstration involving the injection of mNPs into pork sausage shows the potential for use in biological systems. These results indicate that the proposed mNP imaging approach can potentially be extended to a larger array system with higher-resolution.
The benefits of respiratory gating in quantitative PET/CT vary tremendously between individual patients. Respiratory pattern is among many patient-specific characteristics that are thought to play an important role in gating-induced imaging improvements. However, the quantitative relationship between patient-specific characteristics of respiratory pattern and improvements in quantitative accuracy from respiratory-gated PET/CT has not been well established. If such a relationship could be estimated, then patient-specific respiratory patterns could be used to prospectively select appropriate motion compensation during image acquisition on a per-patient basis. This study was undertaken to develop a novel statistical model that predicts quantitative changes in PET/CT imaging due to respiratory gating. Free-breathing static FDG-PET images without gating and respiratory-gated FDG-PET images were collected from twenty-two lung and liver cancer patients on a PET/CT scanner. PET imaging quality was quantified with peak standardized uptake value (SUVpeak) over lesions-of interest. Relative differences in SUVpeak between static and gated PET images were calculated to indicate quantitative imaging changes due to gating. A comprehensive multidimensional extraction of the morphological and statistical characteristics of respiratory patterns was conducted, resulting in 16 features that characterize representative patterns of a single respiratory trace. The 6 most informative features were subsequently extracted using a stepwise feature selection approach. The multiple-regression model was trained and tested based on a leave-one-subject-out cross validation. The predicted quantitative improvements in PET imaging achieved an accuracy higher than 90% using a criterion with a dynamic error-tolerance range for SUVpeak values. The results of this study suggest that our prediction framework could be applied to determine which patients would likely to benefit from respiratory motion compensation when clinicians quantitatively assess PET/CT for therapy target definition and response assessment.
patient classification; respiratory motion; PET/CT; respiratory gating
Recent developments in radiotherapy therapy demand high computation powers to solve challenging problems in a timely fashion in a clinical environment. Graphics processing unit (GPU), as an emerging high-performance computing platform, has been introduced to radiotherapy. It is particularly attractive due to its high computational power, small size, and low cost for facility deployment and maintenance. Over the past a few years, GPU-based high-performance computing in radiotherapy has experienced rapid developments. A tremendous amount of studies have been conducted, in which large acceleration factors compared with the conventional CPU platform have been observed. In this article, we will first give a brief introduction to the GPU hardware structure and programming model. We will then review the current applications of GPU in major imaging-related and therapy-related problems encountered in radiotherapy. A comparison of GPU with other platforms will also be presented.
Thick, segmented crystalline scintillators have shown increasing promise as replacement x-ray converters for the phosphor screens currently used in active matrix flat-panel imagers (AMFPIs) in radiotherapy, by virtue of providing over an order of magnitude improvement in the DQE. However, element-to-element misalignment in current segmented scintillator prototypes creates a challenge for optimal registration with underlying AMFPI arrays, resulting in degradation of spatial resolution. To overcome this challenge, a methodology involving the use of a relatively high resolution AMFPI array in combination with novel binning techniques is presented. The array, which has a pixel pitch of 0.127 mm, was coupled to prototype segmented scintillators based on BGO, LYSO and CsI:Tl materials, each having a nominal element-to-element pitch of 1.016 mm and thickness of ~1 cm. The AMFPI systems incorporating these prototypes were characterized at a radiotherapy energy of 6 MV in terms of MTF, NPS, DQE, and reconstructed images of a resolution phantom acquired using a cone-beam CT geometry. For each prototype, the application of 8×8 pixel binning to achieve a sampling pitch of 1.016 mm was optimized through use of an alignment metric which minimized misregistration and thereby improved spatial resolution. In addition, the application of alternative binning techniques that exclude the collection of signal near septal walls resulted in further significant improvement in spatial resolution for the BGO and LYSO prototypes, though not for the CsI:Tl prototype due to the large amount of optical cross-talk resulting from significant light spread between scintillator elements in that device. The efficacy of these techniques for improving spatial resolution appears to be enhanced for scintillator materials that exhibit mechanical hardness, high density and high refractive index, such as BGO. Moreover, materials that exhibit these properties as well as offer significantly higher light output than BGO, such as CdWO4, should provide the additional benefit of preserving DQE performance.
Megavoltage cone-beam CT; flat-panel imager; electronic portal imaging device; segmented crystalline scintillators; high x-ray detection efficiency
Earlier work on Objective Assessment of Image Quality (OAIQ) focused largely on estimation or classification tasks in which the desired outcome of imaging is accurate diagnosis. This paper develops a general framework for assessing imaging quality on the basis of therapeutic outcomes rather than diagnostic performance. By analogy to Receiver Operating Characteristic (ROC) curves and their variants as used in diagnostic OAIQ, the method proposed here utilizes the Therapy Operating Characteristic or TOC curves, which are plots of the probability of tumor control vs. the probability of normal-tissue complications as the overall dose level of a radiotherapy treatment is varied. The proposed figure of merit is the area under the TOC curve, denoted AUTOC. This paper reviews an earlier exposition of the theory of TOC and AUTOC, which was specific to the assessment of image-segmentation algorithms, and extends it to other applications of imaging in external-beam radiation treatment as well as in treatment with internal radioactive sources. For each application, a methodology for computing the TOC is presented. A key difference between ROC and TOC is that the latter can be defined for a single patient rather than a population of patients.
We designed and constructed an in vivo dosimetry system using plastic scintillation detectors (PSDs) to monitor dose to the rectal wall in patients undergoing intensity-modulated radiation therapy for prostate cancer. Five patients were enrolled in an Institutional Review Board–approved protocol for twice weekly in vivo dose monitoring with our system, resulting in a total of 142 in vivo dose measurements. PSDs were attached to the surface of endorectal balloons used for prostate immobilization to place the PSDs in contact with the rectal wall. Absorbed dose was measured in real time and the total measured dose was compared with the dose calculated by the treatment planning system on the daily CT image dataset. The mean difference between measured and calculated doses for the entire patient population was −0.4% (standard deviation 2.8%). The mean difference between daily measured and calculated doses for each patient ranged from −3.3% to 3.3% (standard deviation ranged from 5.6% to 7.1% for 4 patients and was 14.0% for the last, for whom optimal positioning of the detector was difficult owing to the patient’s large size). Patients tolerated the detectors well and the treatment workflow was not compromised. Overall, PSDs performed well as in vivo dosimeters, providing excellent accuracy, real-time measurement, and reusability.
Previous methods to estimate the inherent accuracy of deformable image
registration (DIR) have typically been performed relative to a known ground
truth, such as tracking of anatomic landmarks or known deformations in a
physical or virtual phantom. In this study, we propose a new approach to
estimate the spatial geometric uncertainty of DIR using statistical sampling
techniques that can be applied to the resulting deformation vector fields (DVFs)
for a given registration. The proposed DIR performance metric, the distance
discordance metric (DDM), is based on the variability in the distance between
corresponding voxels from different images, which are co-registered to the same
voxel at location (X) in an arbitrarily chosen “reference”
image. The DDM value, at location (X) in the reference image, represents the
mean dispersion between voxels, when these images are registered to other images
in the image set. The method requires at least four registered images to
estimate the uncertainty of the DIRs, both for inter-and intra-patient DIR. To
validate the proposed method, we generated an image set by deforming a software
phantom with known DVFs. The registration error was computed at each voxel in
the “reference” phantom and then compared to DDM, inverse
consistency error (ICE), and transitivity error (TE) over the entire phantom.
The DDM showed a higher Pearson correlation (Rp) with the actual
error (Rp ranged from 0.6 to 0.9) in comparison with ICE and TE
(Rp ranged from 0.2 to 0.8). In the resulting spatial DDM map,
regions with distinct intensity gradients had a lower discordance and therefore,
less variability relative to regions with uniform intensity. Subsequently, we
applied DDM for intra-patient DIR in an image set of 10 longitudinal computed
tomography (CT) scans of one prostate cancer patient and for inter-patient DIR
in an image set of 10 planning CT scans of different head and neck cancer
patients. For both intra- and inter-patient DIR, the spatial DDM map showed
large variation over the volume of interest (the pelvis for the prostate patient
and the head for the head and neck patients). The highest discordance was
observed in the soft tissues, such as the brain, bladder, and rectum, due to
higher variability in the registration. The smallest DDM values were observed in
the bony structures in the pelvis and the base of the skull. The proposed
metric, DDM, provides a quantitative tool to evaluate the performance of DIR
when a set of images is available. Therefore, DDM can be used to estimate and
visualize the uncertainty of intra- and/or inter-patient DIR based on the
variability of the registration rather than the absolute registration error.
Deformable image registration; distance discordance; uncertainty; inaccuracy
A factorized system matrix utilizing an image domain resolution model is attractive in fully 3D TOF PET image reconstruction using list-mode data. In this paper, we study a factored model based on sparse matrix factorization that is comprised primarily of a simplified geometrical projection matrix and an image blurring matrix. Beside the commonly-used Siddon's raytracer, we propose another more simplified geometrical projector based on the Bresenham's raytracer which further reduces the computational cost. We discuss in general how to obtain an image blurring matrix associated with a geometrical projector, and provide theoretical analysis that can be used to inspect the efficiency in model factorization. In simulation studies, we investigate the performance of the proposed sparse factorization model in terms of spatial resolution, noise properties and computational cost. The quantitative results reveal that the factorization model can be as efficient as a nonfactored model such as the analytical model while its computational cost can be much lower. In addition we conduct Monte Carlo simulations to identify the conditions under which the image resolution model can become more efficient in terms of image contrast recovery. We verify our observations using the provided theoretical analysis. The result offers a general guide to achieve optimal reconstruction performance based on a sparse factorization model with an only image domain resolution model.
Detection of molecular targeted microbubbles plays a foundational role in ultrasound-based molecular imaging and targeted gene or drug delivery. In this paper, an empirical model describing the binding dynamics of targeted microbubbles in response to modulated acoustic radiation forces in large vessels is presented and experimentally verified using tissue-mimicking flow phantoms. Higher flow velocity and microbubble concentration led to faster detaching rates for specifically bound microbubbles (p < 0.001). Higher time-averaged acoustic radiation force intensity led to faster attaching rates and a higher saturation level of specifically bound microbubbles (p < 0.05). The level of residual microbubble signal in targeted experiments after cessation of radiation forces was the only response parameter that was reliably different between targeted and control experiments (p < 0.05). A related parameter, the ratio of residual-to-saturated microbubble signal (Rresid), is proposed as a measurement that is independent of absolute acoustic signal magnitude and therefore able to reliably detect targeted adhesion independently of control measurements (p < 0.01). These findings suggest the possibility of enhanced detection of specifically bound microbubbles in real-time, using relatively short imaging protocols (approximately 3 min), without waiting for free microbubble clearance.
Detecting cancerous lesions is a major clinical application in emission
tomography. In previous work, we have studied penalized maximum-likelihood (PML)
image reconstruction for the detection task and proposed a method to design a
shift-invariant quadratic penalty function to maximize detectability of a lesion
at a known location in a two dimensional (2D) image. Here we extend the
regularization design to maximize detectability of lesions at unknown locations
in fully 3D PET. We used a multiview channelized Hotelling observer (mvCHO) to
assess the lesion detectability in 3D images to mimic the condition where a
human observer examines three orthogonal views of a 3D image for lesion
detection. We derived simplified theoretical expressions that allow fast
prediction of the detectability of a 3D lesion. The theoretical results were
used to design the regularization in PML reconstruction to improve lesion
detectability. We conducted computer-based Monte Carlo simulations to compare
the optimized penalty with the conventional penalty for detecting lesions of
various sizes. Only true coincidence events were simulated. Lesion detectability
was also assessed by two human observers, whose performances agree well with
that of the mvCHO. Both the numerical observer and human observer results showed
a statistically significant improvement in lesion detection by using the
proposed penalty function compared to using the conventional penalty
The goals of this study were (1) to characterize the optical artefacts affecting measurement accuracy in a volumetric liquid scintillation detector, and (2) to develop methods to correct for these artefacts. The optical artefacts addressed were photon scattering, refraction, camera perspective, vignetting, lens distortion, the lens point spread function, stray radiation, and noise in the camera. These artefacts were evaluated by theoretical and experimental means, and specific correction strategies were developed for each artefact. The effectiveness of the correction methods was evaluated by comparing raw and corrected images of the scintillation light from proton pencil beams against validated Monte Carlo calculations. Blurring due to the lens and refraction at the scintillator tank-air interface were found to have the largest effect on the measured light distribution, and lens aberrations and vignetting were important primarily at the image edges. Photon scatter in the scintillator was not found to be a significant source of artefacts. The correction methods effectively mitigated the artefacts, increasing the average gamma analysis pass rate from 66% to 98% for gamma criteria of 2% dose difference and 2 mm distance to agreement. We conclude that optical artefacts cause clinically meaningful errors in the measured light distribution, and we have demonstrated effective strategies for correcting these optical artefacts.
X-ray fluorescence computed tomography (XFCT) imaging has been focused on the detection of K-shell X-rays. The potential utility of L-shell x-ray XFCT is, however, not well studied. Here we report the first Monte Carlo (MC) simulation of preclinical L-shell XFCT imaging of Cisplatin. We built MC models for both L- and K-shell XFCT with different excitation energies (15 and 30 keV for L-shell and 80 keV for K-shell XFCT). Two small-animal sized imaging phantoms of 2-cm and 4-cm diameter containing a series of objects of 0.6 to 2.7 mm in diameter at 0.7 to 16 mm depths with 10 to 250 μg/mL concentrations of Pt are used in the study. Transmitted and scattered x-rays were collected with photon-integrating transmission detector and photon-counting detector arc, respectively. Collected data were rearranged into XFCT and transmission CT sinograms for image reconstruction. XFCT images were reconstructed with filtered back-projection (FBP) and with iterative maximum-likelihood expectation maximization (ML-EM) without and with attenuation correction. While K-shell XFCT was capable of providing accurate measurement of Cisplatin concentration, its sensitivity was 4.4 and 3.0 times lower than that of L-shell XFCT with 15 keV excitation beam for the 2-cm and 4-cm diameter phantom, respectively. With inclusion of excitation and fluorescence beam attenuation correction, we found that L-shell XFCT was capable of providing fairly accurate information of Cisplatin concentration distribution. With a dose of 29 and 58 mGy, clinically relevant Cisplatin Pt concentrations of 10 μg/mg could be imaged with L-shell XFCT inside a 2-cm and 4-cm diameter object, respectively.
x-ray fluorescence; L-shell; CT; Monte Carlo; Cisplatin; molecular imaging
We investigated the effect of different imaging parameters such as dose, beam energy, energy resolution, and number of energy bins on image quality of K-edge spectral computed tomography (CT) of gold nanoparticles (GNP) accumulated in an atherosclerotic plaque. Maximum likelihood technique was employed to estimate the concentration of GNP, which served as a targeted intravenous contrast material intended to detect the degree of plaque's inflammation. The simulations studies used a single slice parallel beam CT geometry with an X-ray beam energy ranging between 50 and 140 kVp. The synthetic phantoms included small (3 cm in diameter) cylinder and chest (33x24 cm2) phantom, where both phantoms contained tissue, calcium, and gold. In the simulation studies GNP quantification and background (calcium and tissue) suppression task were pursued. The X-ray detection sensor was represented by an energy resolved photon counting detector (e.g., CdZnTe) with adjustable energy bins. Both ideal and more realistic (12% FWHM energy resolution) implementations of photon counting detector were simulated. The simulations were performed for the CdZnTe detector with pixel pitch of 0.5-1 mm, which corresponds to the performance without significant charge sharing and cross-talk effects. The Rose model was employed to estimate the minimum detectable concentration of GNPs. A figure of merit (FOM) was used to optimize the X-ray beam energy (kVp) to achieve the highest signal-to-noise ratio (SNR) with respect to patient dose. As a result, the successful identification of gold and background suppression was demonstrated. The highest FOM was observed at 125 kVp X-ray beam energy. The minimum detectable GNP concentration was determined to be approximately 1.06 μmol/mL (0.21 mg/mL) for an ideal detector and about 2.5 μmol/mL (0.49 mg/mL) for more realistic (12% FWHM) detector. The studies show the optimal imaging parameters at lowest patient dose using an energy resolved photon counting detector to image GNP in an atherosclerotic plaque.
The direct dose mapping (DDM) and energy/mass transfer mapping (EMT) are two essential algorithms for accumulating the dose from different anatomic phases to the reference phase when there is organ motion or tumor/tissue deformation during the delivery of radiation therapy. DDM is based on interpolation of the dose values from one dose grid to another and thus lacks rigor in defining the dose when there are multiple dose values mapped to one dose voxel in the reference phase due to tissue/tumor deformation. On the other hand, EMT counts the total energy and mass transferred to each voxel in the reference phase and calculates the dose by dividing the energy by mass. Therefore it is based on fundamentally sound physics principles. In this study, we implemented the two algorithms and integrated them within the Eclipse TPS. We then compared the clinical dosimetric difference between the two algorithms for 10 lung cancer patients receiving stereotactic radiosurgery treatment, by accumulating the delivered dose to the end-of-exhale (EE) phase. Specifically, the respiratory period was divided into 10 phases and the dose to each phase was calculated and mapped to the EE phase and then accumulated. The displacement vector field (DVF) generated by Demons-based registration of the source and reference images was used to transfer the dose and energy. The DDM and EMT algorithms produced noticeably different cumulative dose in the regions with sharp mass density variations and/or high dose gradients. For the PTV and ITV minimum dose, the difference was up to 11% and 4% respectively. This suggests that DDM might not be adequate for obtaining an accurate dose distribution of the cumulative plan, instead, EMT should be considered.
Voxel based estimation of PET images, generally referred to as parametric imaging, can provide invaluable information about the heterogeneity of an imaging agent in a given tissue. Due to high level of noise in dynamic images, however, the estimated parametric image is often noisy and unreliable. Several approaches have been developed to address this challenge, including spatial noise reduction techniques, cluster analysis, and spatial constrained weighted nonlinear least square (SCWNLS) methods. In this study, we develop and test several noise reduction techniques combined with SCWNLS using simulated dynamic PET images. Both spatial smoothing filters and wavelet based noise reduction techniques are investigated. In addition, 12 different parametric imaging methods are compared using simulated data. With the combination of noise reduction techniques and SCWNLS methods, more accurate parameter estimation can be achieved than either of the two techniques alone. A less than 10% relative root-mean-square-error is achieved with the combined approach in the simulation study. The wavelet denoising based approach is less sensitive to noise and provides more accurate parameter estimation at higher noise levels. Further evaluation of the proposed methods is performed using actual small animal PET datasets. We expect that the proposed method would be useful for cardiac, neurological and oncologic applications.
parametric imaging; small animal imaging; PET; wavelet denoising
Following cancer radiotherapy, reconstruction of doses to organs, other than the target organ, is of interest for retrospective health risk studies. Reliable estimation of doses to organs that may be partially within or fully outside the treatment field requires reliable knowledge of the location and size of the organs, e.g., the stomach, which is at risk from abdominal irradiation. The stomach location and size are known to be highly variable between individuals, but have been little studied. Moreover, for treatments conducted years ago, medical images of patients are usually not available in medical records to locate the stomach. In light of the poor information available to locate the stomach in historical dose reconstructions, the purpose of this work was to investigate the variability of stomach location and size among adult male patients and to develop prediction models for the stomach location and size using predictor variables generally available in medical records of radiotherapy patients treated in the past. To collect data on stomach size and position, we segmented the contours of the stomach and of the skeleton on contemporary Computed Tomography (CT) images for 30 male patients in supine position. The location and size of the stomach was found to depend on body mass index (BMI), ponderal index (PI), and age. For example, the anteroposterior dimension of the stomach was found to increase with increasing BMI (≈0.25 cm per kg/m2) whereas its craniocaudal dimension decreased with increasing PI (≈ −3.3 cm per kg/m3) and its transverse dimension increased with increasing PI (≈ 2.5 cm per kg/m3). Using the prediction models, we generated three dimensional computational stomach models from a deformable hybrid phantom for three patients of different BMI. Based on a typical radiotherapy treatment, we simulated radiotherapy treatments on the predicted stomach models and on the CT images of the corresponding patients. Those dose calculations demonstrated good agreement between predicted and actual stomachs compared with doses derived from a reference model of the body that might be used in the absence of individual CT scan data.
stomach; size and location; predictive models; radiation dose
Murine models are used extensively in biological and translational research. For many of these studies it is necessary to access the vasculature for the injection of biologically active agents. Among the possible methods for accessing the mouse vasculature, tail vein injections are a routine but critical step for many experimental protocols. To perform successful tail vein injections, a high skill set and experience is required, leaving most scientists ill-suited to perform this task. This can lead to a high variability between injections, which can impact experimental results. To allow more scientists to perform their own tail vein injections and to decrease the variability between injections a vascular access system (VAS) that semi-automatically inserts a needle into the tail vein of a mouse was developed. The VAS uses near infrared (NIR) light, image processing techniques, computer controlled motors, and a pressure feedback system to insert the needle and to validate its proper placement within the vein. The VAS was tested by injecting a commonly used radiolabeled probe (FDG) into the tail veins of five mice. These mice were then imaged using micro-positron emission tomography (PET) to measure the percentage of the injected probe remaining in the tail. These studies showed that, on average, the VAS leaves 3.4% of the injected probe in the tail. With these preliminary results, the VAS system demonstrates the potential for improving the accuracy of tail vein injections in mice.
MR images often provide superior anatomic and functional information over CT images, but generally are not used alone without CT images for radiotherapy treatment planning and image guidance. This study aims to investigate the potential of probabilistic classification of voxels from multiple MRI contrasts to generate synthetic CT (“MRCT”) images. The method consists of (1) acquiring multiple MRI volumes: T1-weighted, T2-weighted, two echoes from a ultra-short TE (UTE) sequence, and calculated fat and water image volumes using a Dixon method, (2) classifying tissues using fuzzy c-means clustering with a spatial constraint, (3) assigning attenuation properties with weights based on the probability of individual tissue class being present in each voxel, and (4) generating a MRCT image volume from the sum of attenuation properties in each voxel. The capability of each MRI contrast to differentiate tissues of interest was investigated based on a retrospective analysis of ten patients. For one prospective patient, the correlation of skull intensities between CT and MR was investigated, the discriminatory power of MRI in separating air from bone was evaluated, and the generated MRCT image volume was qualitatively evaluated. Our analyses showed that one MRI volume was not sufficient to separate all tissue types, and T2-weighted images was more sensitive to bone density variation compared to other MRI image types. The short echo UTE image showed significant improvement in contrasting air versus bone, but could not completely separate air from bone without false labeling. Generated MRCT and CT images showed similar contrast between bone and soft/solid tissues. These results demonstrate the potential of the presented method to generate synthetic CT images to support the workflow of Radiation Oncology treatment planning and image guidance.
Forty post-mortem breasts were imaged with a flat-panel based cone-beam x-ray CT system at 50 kVp. The feasibility of breast density quantification has been investigated using standard histogram thresholding and an automatic segmentation method based on the fuzzy c-means algorithm (FCM). The breasts were chemically decomposed into water, lipid, and protein immediately after image acquisition was completed. The percent fibroglandular volume (%FGV) from chemical analysis was used as the gold standard for breast density comparison. Both image-based segmentation techniques showed good precision in breast density quantification with high linear coefficients between the right and left breast of each pair. When comparing with the gold standard using %FGV from chemical analysis, Pearson’s r-values were estimated to be 0.983 and 0.968 for the FCM clustering and the histogram thresholding techniques, respectively. The standard error of the estimate (SEE) was also reduced from 3.92% to 2.45% by applying the automatic clustering technique. The results of the postmortem study suggested that breast tissue can be characterized in terms of water, lipid and protein contents with high accuracy by using chemical analysis, which offers a gold standard for breast density studies comparing different techniques. In the investigated image segmentation techniques, the FCM algorithm had high precision and accuracy in breast density quantification. In comparison to conventional histogram thresholding, it was more efficient and reduced inter-observer variation.
breast density; fuzzy c-means; segmentation; cone-beam computed tomography
Despite the early recognition of the potential of proton imaging to assist proton therapy the modality is still removed from clinical practice, with various approaches in development. For proton-counting radiography applications such as Computed Tomography (CT), the Water-Equivalent-Path-Length (WEPL) that each proton has travelled through an imaged object must be inferred. Typically, scintillator-based technology has been used in various energy/range telescope designs. Here we propose a very different alternative of using radiation-hard CMOS Active Pixel Sensor (APS) technology. The ability of such a sensor to resolve the passage of individual protons in a therapy beam has not been previously shown. Here, such capability is demonstrated using a 36 MeV cyclotron beam (University of Birmingham Cyclotron, Birmingham, UK) and a 200 MeV clinical radiotherapy beam (iThemba LABS, Cape Town, SA). The feasibility of tracking individual protons through multiple CMOS layers is also demonstrated using a two-layer stack of sensors. The chief advantages of this solution are the spatial discrimination of events intrinsic to pixelated sensors, combined with the potential provision of information on both the range and residual energy of a proton. The challenges in developing a practical system are discussed.