Manu N. Lakshmanan

Coding and sampling for compressive x-ray diffraction tomography

Coded apertures and energy resolving detectors may be used to improve the sampling efficiency of x-ray tomography and increase the physical diversity of x-ray phenomena measured. Coding and decompressive inference enable increased molecular specificity, reduced exposure and scan times. We outline a specific coded aperture x-ray coherent scatter imaging architecture that demonstrates the potential of such schemes. Based on this geometry, we develop a physical model using both a semi-analytic and Monte Carlo-based framework, devise an experimental realization of the system, describe a reconstruction algorithm for estimating the object from raw data, and propose a classification scheme for identifying the material composition of the object at each location

Photon‐counting CT for simultaneous imaging of multiple contrast agents in the abdomen: An in vivo study

Purpose: To demonstrate the feasibility of spectral imaging using photon‐counting detector (PCD) x‐ray computed tomography (CT) for simultaneous material decomposition of three contrast agents in vivo in a large animal model. Methods: This Institutional Animal Care and Use Committee‐approved study used a canine model. Bismuth subsalicylate was administered orally 24–72 h before imaging. PCD CT was performed during intravenous administration of 40–60 ml gadoterate meglumine; 3.5 min later, iopamidol 370 was injected intravenously. Renal PCD CT images were acquired every 2 s for 5–6 min to capture the wash‐in and wash‐out kinetics of the contrast agents. Least mean squares linear material decomposition was used to calculate the concentrations of contrast agents in the aorta, renal cortex, renal medulla and renal pelvis. Results: Using reference vials with known concentrations of materials, we computed molar concentrations of the various contrast agents during each phase of CT scanning. Material concentration maps allowed simultaneous quantification of both arterial and delayed renal enhancement in a single CT acquisition. The accuracy of the material decomposition algorithm in a test phantom was −0.4 ± 2.2 mM, 0.3 ± 2.2 mM for iodine and gadolinium solutions, respectively. Peak contrast concentration of gadolinium and iodine in the aorta, renal cortex, and renal medulla were observed 16, 24, and 60 s after the start each injection, respectively. Conclusion: Photon‐counting spectral CT allowed simultaneous material decomposition of multiple contrast agents in vivo. Besides defining contrast agent concentrations, tissue enhancement at multiple phases was observed in a single CT acquisition, potentially obviating the need for multiphase CT scans and thus reducing radiation dose.

Dual-contrast agent photon-counting computed tomography of the heart: initial experience

To determine the feasibility of dual—contrast agent imaging of the heart using photon-counting detector (PCD) computed tomography (CT) to simultaneously assess both first-pass and late enhancement of the myocardium. An occlusion-reperfusion canine model of myocardial infarction was used. Gadolinium-based contrast was injected 10 min prior to PCD CT. Iodinated contrast was infused immediately prior to PCD CT, thus capturing late gadolinium enhancement as well as first-pass iodine enhancement. Gadolinium and iodine maps were calculated using a linear material decomposition technique and compared to single-energy (conventional) images. PCD images were compared to in vivo and ex vivo magnetic resonance imaging (MRI) and histology. For infarct versus remote myocardium, contrast-to-noise ratio (CNR) was maximal on late enhancement gadolinium maps (CNR 9.0 ± 0.8, 6.6 ± 0.7, and 0.4 ± 0.4, p < 0.001 for gadolinium maps, single-energy images, and iodine maps, respectively). For infarct versus blood pool, CNR was maximum for iodine maps (CNR 11.8 ± 1.3, 3.8 ± 1.0, and 1.3 ± 0.4, p < 0.001 for iodine maps, gadolinium maps, and single-energy images, respectively). Combined first-pass iodine and late gadolinium maps allowed quantitative separation of blood pool, scar, and remote myocardium. MRI and histology analysis confirmed accurate PCD CT delineation of scar. Simultaneous multi-contrast agent cardiac imaging is feasible with photon-counting detector CT. These initial proof-of-concept results may provide incentives to develop new k-edge contrast agents, to investigate possible interactions between multiple simultaneously administered contrast agents, and to ultimately bring them to clinical practice.

Design and implementation of coded aperture coherent scatter spectral imaging of cancerous and healthy breast tissue samples

Abstract. A scatter imaging technique for the differentiation of cancerous and healthy breast tissue in a heterogeneous sample is introduced in this work. Such a technique has potential utility in intraoperative margin assessment during lumpectomy procedures. In this work, we investigate the feasibility of the imaging method for tumor classification using Monte Carlo simulations and physical experiments. The coded aperture coherent scatter spectral imaging technique was used to reconstruct three-dimensional (3-D) images of breast tissue samples acquired through a single-position snapshot acquisition, without rotation as is required in coherent scatter computed tomography. We perform a quantitative assessment of the accuracy of the cancerous voxel classification using Monte Carlo simulations of the imaging system; describe our experimental implementation of coded aperture scatter imaging; show the reconstructed images of the breast tissue samples; and present segmentations of the 3-D images in order to identify the cancerous and healthy tissue in the samples. From the Monte Carlo simulations, we find that coded aperture scatter imaging is able to reconstruct images of the samples and identify the distribution of cancerous and healthy tissues (i.e., fibroglandular, adipose, or a mix of the two) inside them with a cancerous voxel identification sensitivity, specificity, and accuracy of 92.4%, 91.9%, and 92.0%, respectively. From the experimental results, we find that the technique is able to identify cancerous and healthy tissue samples and reconstruct differential coherent scatter cross sections that are highly correlated with those measured by other groups using x-ray diffraction. Coded aperture scatter imaging has the potential to provide scatter images that automatically differentiate cancerous and healthy tissue inside samples within a time on the order of a minute per slice.

Optimization of a coded aperture coherent scatter spectral imaging system for medical imaging

Coherent scatter X-ray imaging is a technique that provides spatially-resolved information about the molecular structure of the material under investigation, yielding material-specific contrast that can aid medical diagnosis and inform treatment. In this study, we demonstrate a coherent-scatter imaging approach based on the use of coded apertures (known as coded aperture coherent scatter spectral imaging1, 2) that enables fast, dose-efficient, high-resolution scatter imaging of biologically-relevant materials. Specifically, we discuss how to optimize a coded aperture coherent scatter imaging system for a particular set of objects and materials, describe and characterize our experimental system, and use the system to demonstrate automated material detection in biological tissue.

Volumetric x-ray coherent scatter imaging of cancer in resected breast tissue: a Monte Carlo study using virtual anthropomorphic phantoms.

Breast cancer patients undergoing surgery often choose to have a breast conserving surgery (BCS) instead of mastectomy for removal of only the breast tumor. If post-surgical analysis such as histological assessment of the resected tumor reveals insufficient healthy tissue margins around the cancerous tumor, the patient must undergo another surgery to remove the missed tumor tissue. Such re-excisions are reported to occur in 20%-70% of BCS patients. A real-time surgical margin assessment technique that is fast and consistently accurate could greatly reduce the number of re-excisions performed in BCS. We describe here a tumor margin assessment method based on x-ray coherent scatter computed tomography (CSCT) imaging and demonstrate its utility in surgical margin assessment using Monte Carlo simulations. A CSCT system was simulated in GEANT4 and used to simulate two virtual anthropomorphic CSCT scans of phantoms resembling surgically resected tissue. The resulting images were volume-rendered and found to distinguish cancerous tumors embedded in complex distributions of adipose and fibroglandular breast tissue (as is expected in the breast). The images exhibited sufficient spatial and spectral (i.e. momentum transfer) resolution to classify the tissue in any given voxel as healthy or cancerous. ROC analysis of the classification accuracy revealed an area under the curve of up to 0.97. These results indicate that coherent scatter imaging is promising as a possible fast and accurate surgical margin assessment technique.

Simulations of Breast Cancer Imaging Using Gamma-Ray Stimulated Emission Computed Tomography

Here, we present an innovative imaging technology for breast cancer using gamma-ray stimulated spectroscopy based on the nuclear resonance fluorescence (NRF) technique. In NRF, a nucleus of a given isotope selectively absorbs gamma rays with energy exactly equal to one of its quantized energy states, emitting an outgoing gamma ray with energy nearly identical to that of the incident gamma ray. Due to its application of NRF, gamma-ray stimulated spectroscopy is sensitive to trace element concentration changes, which are suspected to occur at early stages of breast cancer, and therefore can be potentially used to noninvasively detect and diagnose cancer in its early stages. Using Monte-Carlo simulations, we have designed and demonstrated an imaging system that uses gamma-ray stimulated spectroscopy for visualizing breast cancer. We show that gamma-ray stimulated spectroscopy is able to visualize breast cancer lesions based primarily on the differences in the concentrations of trace elements between diseased and healthy tissue, rather than differences in density that are crucial for X-ray mammography. The technique shows potential for early breast cancer detection; however, improvements are needed in gamma-ray laser technology for the technique to become a clinically feasible method of detecting and diagnosing cancer at early stages.

Quantitative Assessment of Lesion Detection Accuracy, Resolution, and Reconstruction Algorithms in Neutron Stimulated Emission Computed Tomography

We present a quantitative analysis of the image quality obtained using filtered back-projection (FBP) with Ram-Lak filtering and maximum likelihood-expectation maximization (ML-EM)-with no postreconstruction filtering in either case-in neutron stimulated emission computed tomography (NSECT) imaging using Monte Carlo simulations in the context of clinically relevant models of liver iron overload. The ratios of pixel intensities for several regions of interest and lesion shape detection using an active-contours segmentation algorithm are assessed for accuracy across different scanning configurations and reconstruction algorithms. The modulation transfer functions (MTFs) are also computed for the cases under study and are applied to determine a minimum detectable lesion spacing as a form of sensitivity analysis. The accuracy of NSECT imaging in measuring relative tissue concentration is presented for simulated clinical liver cases. When using the 15th iteration, ML-EM provides at least 25% better resolution than FBP and proves to be highly robust under low-signal high-noise conditions prevalent in NSECT. However, FBP gives more accurate lesion pixel intensity ratios and size estimates in some cases; due to advantages provided by both reconstruction algorithms, it is worth exploring the development of an algorithm that is a hybrid of the two. We also show that NSECT imaging can be used to accurately detect 3-cm lesions in backgrounds that are a significant fraction (one-quarter) of the concentration of the lesion, down to a 4-cm spacing between lesions.

X-ray coherent scatter imaging for surgical margin detection: a Monte Carlo study

Instead of having the entire breast removed (a mastectomy), breast cancer patients often receive a breast con- serving surgery (BCS) for removal of only the breast tumor. If post-surgery analysis reveals ta missed margin around the tumor tissue excised through the BCS procedure, the physician must often call the patient back for another surgery, which is both difficult and risky for the patient. If this “margin detection” could be performed during the BCS procedure itself, the surgical team could use the analysis to ensure that all tumor tissue was removed in a single surgery, thereby potentially reducing the number of call backs from breast cancer surgery. We describe here a potential technique to detect surgical tumor margins in breast cancer using x-ray coherent scatter imaging. In this study, we demonstrate the imaging ability of this technique using Monte Carlo simulations.

Coded aperture coherent scatter imaging for breast cancer detection: a Monte Carlo evaluation

It is known that conventional x-ray imaging provides a maximum contrast between cancerous and healthy fibroglandular breast tissues of 3% based on their linear x-ray attenuation coefficients at 17.5 keV, whereas coherent scatter signal provides a maximum contrast of 19% based on their differential coherent scatter cross sections. Therefore in order to exploit this potential contrast, we seek to evaluate the performance of a coded- aperture coherent scatter imaging system for breast cancer detection and investigate its accuracy using Monte Carlo simulations. In the simulations we modeled our experimental system, which consists of a raster-scanned pencil beam of x-rays, a bismuth-tin coded aperture mask comprised of a repeating slit pattern with 2-mm periodicity, and a linear-array of 128 detector pixels with 6.5-keV energy resolution. The breast tissue that was scanned comprised a 3-cm sample taken from a patient-based XCAT breast phantom containing a tomosynthesis- based realistic simulated lesion. The differential coherent scatter cross section was reconstructed at each pixel in the image using an iterative reconstruction algorithm. Each pixel in the reconstructed image was then classified as being either air or the type of breast tissue with which its normalized reconstructed differential coherent scatter cross section had the highest correlation coefficient. Comparison of the final tissue classification results with the ground truth image showed that the coded aperture imaging technique has a cancerous pixel detection sensitivity (correct identification of cancerous pixels), specificity (correctly ruling out healthy pixels as not being cancer) and accuracy of 92.4%, 91.9% and 92.0%, respectively. Our Monte Carlo evaluation of our experimental coded aperture coherent scatter imaging system shows that it is able to exploit the greater contrast available from coherently scattered x-rays to increase the accuracy of detecting cancerous regions within the breast.