Search

PurposePhoton counting CT (PCCT) provides spectral measurements for material decomposition. However, the image noise (at a fixed dose) depends on the source spectrum. Our study investigates the potential benefits from spectral optimization using fast kV switching and filtration to reduce noise in material decomposition.ApproachThe effect of the input spectra on noise performance in both two-basis material decomposition and three-basis material decomposition was compared using Cramer-Rao lower bound analysis in the projection domain and in a digital phantom study in the image domain. The fluences of different spectra were normalized using the CT dose index to maintain constant dose levels. Four detector response models based on Si or CdTe were included in the analysis.ResultsFor single kV scans, kV selection can be optimized based on the imaging task and object size. Furthermore, our results suggest that noise in material decomposition can be substantially reduced with fast kV switching. For two-material decomposition, fast kV switching reduces the standard deviation (SD) by ∼10% . For three-material decomposition, greater noise reduction in material images was found with fast kV switching (26.2% for calcium and 25.8% for iodine, in terms of SD), which suggests that challenging tasks benefit more from the richer spectral information provided by fast kV switching.ConclusionsThe performance of PCCT in material decomposition can be improved by optimizing source spectrum settings. Task-specific tube voltages can be selected for single kV scans. Also, our results demonstrate that utilizing fast kV switching can substantially reduce the noise in material decomposition for both two- and three-material decompositions, and a fixed Gd filter can further enhance such improvements for two-material decomposition.

Radiation detection can provide a reliable means of detecting radiological material. Such capabilities can help to prevent nuclear and/or radiological attacks, but reliable detection in uncontrolled surroundings requires algorithms that account for environmental background radiation. The Berkeley Data Cloud (BDC) facilitates the development of such methods by providing a framework to capture, store, analyze, and share data sets. In the era of big data, both the size and variety of data make it difficult to explore and find data sets of interest and manage the data. Thus, in the context of big data, visualization is critical for checking data consistency and validity, identifying gaps in data coverage, searching for data relevant to an analyst's use cases, and choosing input parameters for analysis. Downloading the data and exploring it on an analyst's desktop using traditional tools are no longer feasible due to the size of the data. This paper describes the design and implementation of a visualization system that addresses the problems associated with data exploration within the context of the BDC. The visualization system is based on a JavaScript front end communicating via REST with a back end web server.

The integrity of endothelial function in major arteries (EFMA) is a powerful independent predictor of heart attack and stroke. Existing ultrasound-based non-invasive assessment methods are technically challenging and suitable only for laboratory settings. EFMA, like blood pressure (BP), is both acutely and chronically affected by factors such as lifestyle and medication. Consequently, laboratory-based measurements cannot fully gauge the effects of medical interventions on EFMA. EFMA and BP have, arguably, comparable (but complementary) value in the assessment of cardiovascular health. Widespread deployment of EFMA assessment is thus a desirable clinical goal. To this end, we propose a device based on modifying the measurement protocol of a standard electronic sphygmomanometer. The protocol involves inflating the cuff to sub-diastolic levels to enable recording of the pulse waveform before and after vasodilatory stimulus. The mechanical unloading of the arterial wall provided by the cuff amplifies the distension that occurs with each pulse, which is measured as a pressure variation in the cuff. We show that the height of the rising edge of each pulse is proportional to the change in lumen area between diastole and systole. This allows the effect of vasodilatory stimuli on the artery to be measured with high sensitivity. We compare the proposed cuff flow-mediated dilation (cFMD) method to ultrasound flow-mediated dilation (uFMD). We find significant correlation (r = 0.55, p = 0.003, N = 27) between cFMD- and uFMD-based metrics obtained when the release of a 5 min cuff occlusion is employed to induce endothelial stimulus via reactive hyperemia. cFMD is approximately proportional to the square of uFMD, representing a typical increase in sensitivity to vasodilation of 300-600%. This study illustrates the potential for an individual to conveniently measure his/her EFMA by using a low-cost reprogrammed home sphygmomanometer.

Background: Edge‐on‐irradiated silicon detectors are currently being investigated for use in full‐body photon‐counting computed tomography (CT) applications. The low atomic number of silicon leads to a significant number of incident photons being Compton scattered in the detector, depositing a part of their energy and potentially being counted multiple times. Even though the physics of Compton scatter is well established, the effects of Compton interactions in the detector on image quality for an edge‐on‐irradiated silicon detector have still not been thoroughly investigated. Purpose: To investigate and explain effects of Compton scatter on low‐frequency detective quantum efficiency (DQE) for photon‐counting CT using edge‐on‐irradiated silicon detectors. Methods: We extend an existing Monte Carlo model of an edge‐on‐irradiated silicon detector with 60 mm active absorption depth, previously used to evaluate spatial‐frequency‐based performance, to develop projection and image domain performance metrics for pure density and pure spectral imaging tasks with 30 and 40 cm water backgrounds. We show that the lowest energy threshold of the detector can be used as an effective discriminator of primary counts and cross‐talk caused by Compton scatter. We study the developed metrics as functions of the lowest threshold energy for root‐mean‐square electronic noise levels of 0.8, 1.6, and 3.2 keV, where the intermediate level 1.6 keV corresponds to the noise level previously measured on a single sensor element in isolation. We also compare the performance of a modeled detector with 8, 4, and 2 optimized energy bins to a detector with 1‐keV‐wide bins. Results: In terms of low‐frequency DQE for density imaging, there is a tradeoff between using a threshold low enough to capture Compton interactions and avoiding electronic noise counts. For 30 cm water phantom, 4 energy bins, and a root‐mean‐square electronic noise of 0.8, 1.6, and 3.2 keV, it is optimal to put the lowest energy threshold at 3, 6, and 1 keV, which gives optimal projection‐domain DQEs of 0.64, 0.59, and 0.52, respectively. Low‐frequency DQE for spectral imaging also benefits from measuring Compton interactions with respective optimal thresholds of 12, 12, and 13 keV. No large dependence on background thickness was observed. For the intermediate noise level (1.6 keV), increasing the lowest threshold from 5 to 35 keV increases the variance in a iodine basis image by 60%–62% (30 cm phantom) and 67%–69% (40 cm phantom), with 8 bins. Both spectral and density DQE are adversely affected by increasing the electronic noise level. Image‐domain DQE exhibits similar qualitative behavior as projection‐domain DQE. Conclusions: Compton interactions contribute significantly to the density imaging performance of edge‐on‐irradiated silicon detectors. With the studied detector topology, the benefit of counting primary Compton interactions outweighs the penalty of multiple counting at all lowest threshold energies. Compton interactions also contribute significantly to the spectral imaging performance for measured energies above 10 keV. [ABSTRACT FROM AUTHOR]

We present an in situ calibration technique for the LBNL Positron Emission Mammography (PEM) detector module that is capable of measuring depth of interaction. The detector module consists of 64 LSO crystals coupled on one end to a single photomultiplier tube (PMT) and on the opposite end to a 64 pixel array of silicon photodiodes (PD). The PMT provides an accurate timing pulse, the PDs identify the crystal of interaction, the sum provides a total energy signal, and the G=PD/(PD+PMT) ratio determines the depth of interaction. We calibrate using the Lu-176 natural background radiation of the LSO crystals. We determine the relative gain (K) of the PMT and PD by minimizing the skewness of the G distribution. We determine the depth dependence (alpha) from the fwhm of the G distribution with optimal K, and confirm this a to G-width correlation by measuring alpha for crystals excited at a specified depth with side-incident gamma rays. We also evaluate the performance of calibrated detector modules, presenting energy and depth of interaction resolution results.

We present the construction status of the LBNL Positron Emission Mammography (PEM) camera, which utilizes a PET detector module with depth of interaction measurement consisting of 64 LSO crystals (3x3x30 mm3) coupled on one end to a single photomultiplier tube (PMT) and on the opposite end to a 64 pixel array of silicon photodiodes (PDs). The PMT provides an accurate timing pulse, the PDs identify the crystal of interaction, the sum provides a total energy signal, and the PD/(PD+PMT) ratio determines the depth of interaction. We have completed construction of all 42 PEM detector modules. All data acquisition electronics have been completed, fully tested and loaded onto the gantry. We have demonstrated that all functions of the custom IC work using the production rigid-flex boards and data acquisition system. Preliminary detector module characterization and coincidence data have been taken using the production system, including initial images.

Quantitative reconstruction of cone beam X-ray computed tomography (CT) datasets requires accurate modeling of scatter, beam-hardening, beam profile, and detector response. Typically, commercial imaging systems use fast empirical corrections that are designed to reduce visible artifacts due to incomplete modeling of the image formation process. In contrast, Monte Carlo (MC) methods are much more accurate but are relatively slow. Scatter kernel superposition (SKS) methods offer a balance between accuracy and computational practicality. We show how a single SKS algorithm can be employed to correct both kilovoltage (kV) energy (diagnostic) and megavoltage (MV) energy (treatment) X-ray images. Using MC models of kV and MV imaging systems, we map intensities recorded on an amorphous silicon flat panel detector to water-equivalent thicknesses (WETs). Scatter-grams are derived from acquired projection images using scatter kernels indexed by the local WET values and are then iteratively refined using a scatter magnitude bounding scheme that allows the algorithm to accommodate the very high scatter-to-primary ratios encountered in kV imaging. The algorithm recovers radiological thicknesses to within 9% of the true value at both kV and megavolt energies. Nonuniformity in CT reconstructions of homogeneous phantoms is reduced by an average of 76% over a wide range of beam energies and phantom geometries. Index Terms--Biomedical applications of radiation, Monte Carlo (MC) methods, radiation hardening, X-ray detectors, X-ray imaging, X-ray scattering, X-ray tomography.

Objectives: Because radiotherapy is indispensible for treating cervical cancer, it is critical to accurately and efficiently delineate the radiation targets. We evaluated a deep learning (DL)‐based auto‐segmentation algorithm for automatic contouring of clinical target volumes (CTVs) in cervical cancers. Methods: Computed tomography (CT) datasets from 535 cervical cancers treated with definitive or postoperative radiotherapy were collected. A DL tool based on VB‐Net was developed to delineate CTVs of the pelvic lymph drainage area (dCTV1) and parametrial area (dCTV2) in the definitive radiotherapy group. The training/validation/test number is 157/20/23. CTV of the pelvic lymph drainage area (pCTV1) was delineated in the postoperative radiotherapy group. The training/validation/test number is 272/30/33. Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD) were used to evaluate the contouring accuracy. Contouring times were recorded for efficiency comparison. Results: The mean DSC, MSD, and HD values for our DL‐based tool were 0.88/1.32 mm/21.60 mm for dCTV1, 0.70/2.42 mm/22.44 mm for dCTV2, and 0.86/1.15 mm/20.78 mm for pCTV1. Only minor modifications were needed for 63.5% of auto‐segmentations to meet the clinical requirements. The contouring accuracy of the DL‐based tool was comparable to that of senior radiation oncologists and was superior to that of junior/intermediate radiation oncologists. Additionally, DL assistance improved the performance of junior radiation oncologists for dCTV2 and pCTV1 contouring (mean DSC increases: 0.20 for dCTV2, 0.03 for pCTV1; mean contouring time decrease: 9.8 min for dCTV2, 28.9 min for pCTV1). Conclusions: DL‐based auto‐segmentation improves CTV contouring accuracy, reduces contouring time, and improves clinical efficiency for treating cervical cancer. [ABSTRACT FROM AUTHOR]

The clinical application of dynamic ECT reconstruction algorithms for inconsistent projection (IP) data has been beset with difficulties. These include poor scalability, numerical instability of algorithms, problems of nonuniqueness of solutions, the need to oversimplify tracer kinetics, and impractical computational burden. We present a stable reconstruction algorithm with low computational cost that is able to recover the tracer kinetics of several hundred image regions at realistic noise levels. Through optimal selection of a small set of nonnegative basis functions to describe regional time--activity curves (TACs), we are able to solve for the first-order compartmental model kinetics of each region. A nonuniform resolution pixelization of image space is employed to obtain highest resolution in regions of interest. These spatial and temporal simplifications improve numerical conditioning, provide robustness against noise, and greatly decrease the computational burden of dynamic reconstruction. We apply this algorithm to IP phantom data whose source distribution, kinetics, and count statistics are modeled after a clinical myocardial SPECT dataset. TACs of phantom regions are recovered to within a mean square error of 6%, an accuracy that proves sufficient to allow for quantitative detection of a myocardial perfusion defect within healthy myocardial tissue. Index Terms--Dynamic emission computed tomography (ECT), kinetic modeling, image reconstruction, myocardial single photon emission tomography (PET), spectral analysis, tracer kinetics.