Browsing by Subject "Noise"

Purpose: The fleet of x-ray computed tomography systems used by large medical institutions is often comprised of scanners from various manufacturers. An inhomogeneous fleet of scanners could lead to inconsistent image quality due to the different features and technologies implemented by each manufacturer. Specifically, image noise could be highly variable across scanners from different manufacturers. To partly address this problem, we have performed two studies to characterize noise magnitude and texture on two scanners: one from GE Healthcare and one from Siemens Healthcare. The purpose of the first study was to evaluate how noise magnitude changes as a function of image quality indicators (e.g., "noise index" and "quality reference mAs") when automatic tube current modulation is used. The purpose of the second study was to compare and match reconstruction kernels from each vendor with respect to noise texture.

Methods: The first study was performed by imaging anthropomorphic phantoms on each scanner using a clinical range of scan settings and image quality indicator values. Noise magnitude was measured at various anatomical locations using an image subtraction technique. Noise was then modeled as a function of image quality indicators and other scan parameters that were found to significantly affect the noise-image quality indicator relationship.

The second study was performed by imaging the American College of Radiology CT accreditation phantom with a comparable acquisition protocol on each scanner. Images were reconstructed using filtered backprojection and a wide selection of reconstruction kernels. We then estimated the noise power spectrum (NPS) of each image set and performed a systematic kernel-by-kernel comparison of spectra using the peak frequency difference (PFD) and the root mean square error (RMSE) as metrics of similarity. Kernels that minimized the PFD and RMSE were paired.

Results: From the fist study, on the GE scanner, noise magnitude increased linearly with noise index. The slope of that line was affected by changing the anatomy of interest, kVp, reconstruction algorithm, and convolution kernel. The noise-noise index relationship was independent of phantom size, slice thickness, pitch, field of view, and beam width. On the Siemens scanner, noise magnitude decreased non-linearly with increasing quality reference effective mAs, slice thickness, and peak tube voltage. The noise-quality reference effective mAs relationship also depended on anatomy of interest, phantom size, age selection, and reconstruction algorithm but was independent of pitch, field of view, and detector configuration. From the second study, the RMSE between the NPS of GE and Siemens kernels varied from 0.02 to 0.74 mm. The GE kernels "Soft", "Standard", "Chest", and "Lung" closely matched the Siemens kernels "B35f", "B43f", "B46f", and "B80f" (RMSE<0.07 mm, PFD<0.02 mm-1). The GE "Bone", "Bone+", and "Edge" kernels all matched most closely to Siemens "B75f" kernel but with sizeable RMSE and PFD values up to 0.62 mm and 0.5 mm-1 respectively. These sizeable RMSE and PFD values corresponded to visually perceivable differences in the noise texture of the images.

Conclusions: From the first study, we established how noise changes with changing image quality indicators across a clinically relevant range of imaging parameters. This will allow us target equal noise levels across manufacturers. From the second study, we concluded that it is possible to use the NPS to quantitatively compare noise texture across CT systems. We found that many commonly used GE and Siemens kernels have similar texture. The degree to which similar texture across scanners could be achieved varies and is limited by the kernels available on each scanner. This result will aid in choosing appropriate corresponding kernels for each scanner when writing protocols. Taken together, the results from these two studies will allow us to write protocols that result in images with more consistent noise properties.

The purpose of this dissertation was to develop methods of measuring patient-specific image quality in computed tomography. The methods developed in this dissertation enable noise power spectrum, low contrast resolution, and ultimately a detectability index to be measured in a patient-specific manner. The project is divided into three part: 1) demonstrating the utility of currently developed patient-specific measures of image quality, 2) developing a method to estimate noise power spectrum and low contrast task transfer function from patient images, 3) and applying the extended metrology to the calculation of a patient-specific and task-specific detectability index of the future.In part 1, (chapters 2 and 3) the value of patient-specific image quality is demonstrated in two ways. First, patient-specific measures of noise magnitude and high-contrast resolution were deployed on a broad clinical dataset of chest and abdomen-pelvis exams. Image quality and dose were measured for 87,629 cases across 97 medical facilities, and variability in each outcome are reported. Such measurements of variability would be impossible in a phantom-derived image quality paradigm. Secondly, patient-specific measures of noise magnitude and high-contrast resolution were combined with a phantom-derived noise power spectrum to yield a detectability index. The hybrid (patient, and phantom-derived) detectability index was measured and retrospectively compared to the results of a detection observer study. The results show that the measured hybrid detectability index is shown to be correlated with human observer detection performance, further demonstrating the value of measuring patient-specific image quality. In part 2, (chapters 4 and 5) two image quality aspects are extended from a phantom-derived to a patient-specific paradigm. In chapter 4, a method to measure noise power spectrum from patient images is developed and validated using virtual imaging trial and physical phantom data. The method is applied to unseen clinical cases to demonstrate its feasibility, and the method’s sensitivity to expected trends across image reconstructions. Since the method relies on a sufficient area within the patient’s liver to make a measurement, the sensitivity of measurement accuracy of the method region size is assessed. Results show that the measurements can be accurate with as few as 106 included pixels, and that measurements are sensitive to ground truth differences in reconstruction algorithm. In chapter 5, a method to measure low contrast resolution from patient images is developed and validated using low contrast insert phantom scans. The method uses a support vector machine to learn the connection between the patient-specific noise power spectrum measured in chapter 4 and the low contrast task transfer function. The estimation method is compared to clinical alternative and results show that it is more accurate on the basis of RMSE for iterative reconstructions (especially high strength reconstructions). In part 3, (chapter 6 and appendix section 8.1) the developed patient-specific image quality metrology are applied to calculated fully patient-specific detectability index. Here, patient-specific image quality measures are re-applied to the detectability index calculations from chapter 3, converting the calculations from a hybrid method to a fully patient-specific method. To do so, the patient-specific noise power spectrum estimates from chapter 4 were combined with the patient-specific low contrast task transfer functions from chapter 5 to inform the detectability index calculations. The purpose of this chapter was to show the positive impact of measuring a task-based measure of image quality in a fully patient-specific paradigm. The results show that the fully patient-specific detectability index show a statistically significant improvement in its relation with human detection accuracy over the hybrid measurements. This section also served as an indirect validation methodologies in chapters 4 and 5. Finally, all patient-specific measures are deployed over a variety of clinical cases to demonstrate feasibility of using the methods to monitor image quality. In conclusion, this dissertation developed methods to assess task based and task generic image quality directly from patient images, and demonstrated the utility and value of patient-specific image quality assessment.

As society becomes increasingly data-driven, the appetite of individuals, corporations, and algorithms for data sources swells, strengthening the demand for sensors. Chemical sensors are of particular interest as they provide highly human-relevant information, such as DNA sequences, cancer biomarker concentrations, blood glucose levels, antibody detection, and viral testing, to name a few. Among the most promising transduction elements for chemical sensors are nanomaterial field-effect transistors (FETs). The nanoscale size of these devices allows them to operate using very small sample sizes (an extremely small volume of patient blood, for instance), be strongly influenced by low concentrations of the target chemical, and be produced at low-cost, potentially using the same methods developed for consumer electronics (which have achieved a cost of less than 0.000001 cents per device). Nanomaterial FET-based chemical sensors also have the advantage of directly transducing a chemical presence or change to an electrical output signal. This avoids components such as lasers, optics, fluorophores, and more, that are frequently used as a part of the transduction chain in other types of chemical sensors, adding size, complexity, and cost. Much work has focused on demonstrating one-off nanomaterial FET-based sensors, but less work has been done to determine the underlying mechanisms that lead to sensitivity by mapping sensitivity against other variables in experimental devices. With challenges of consistency and reproducible operation stifling progress in this field, there is a significant need to improve understanding of nanomaterial-based FET sensitivity and operation mechanisms.

The work contained in this dissertation maps the sensitivity of nanomaterial FETs across a range of parameters, including space, time, device operating point, and analyte charge. This mapping is performed in an effort to yield insight into the underlying mechanisms that govern the sensitivity of these devices to nearby charges. In order to both draw comparisons between different device types and to make the results of this work broadly applicable to the field as a whole, four types of devices were studied that span a broad range of characteristics. The device types spanned from channels of one-dimensional nanotubes to three-dimensional nanostructures, and from partially printed fabrication to cleanroom-based nanofabrication. Specifically, the devices explored herein are carbon nanotube (CNT) FETs, molybdenum disulfide (MoS2) FETs, silicon nanowire FETs, and carbon nanotube thin-film transistors (CNT-TFTs). Fabrication processes were developed to build devices of each of these types that are capable of undergoing long-term electronic testing with reliable contact strategies. Passivation schemes were also developed for each device type to enable testing in solution and formation of solution-based sensors so that results could be extended to the case of biosensors. An automated experimentation platform was developed to enable tight synchronization between characterization instruments so that each variable impacting device sensitivity could be controlled and measured in tandem, in some cases for months on end.

Many of the obtained results showed similar trends in sensitivity between device types, while some findings were unique to a given channel material. All tested devices showed stability after a period of drain current settling caused by the occupation equilibration of charge trap states – an effect that was found to severely reduce sensitivity and dynamic range. For CNTs specifically, two new decay modes were discovered (intermediate between device stability and breakdown) along with respective onset voltages that can be used to avoid them. For CNT-TFTs, it was found that the relationship between signal-to-noise ratio (SNR) and device operating point remained consistent between ambient air and solution environments, indicating that this relationship is governed primarily by properties of the device. A simple chemical sensor made from the same devices showed a clear peak in the SNR near the device threshold voltage – a result that became increasingly meaningful when combined with similar observations in other device types obtained via separate experimental methods.

For both silicon nanowire and MoS2 FETs, sensitivity was mapped in space with sub-nanometer precise control over analyte position. Both device types manifested distinct sensitivity hotspots spread across the geometry of the channel. These hotspots were found to be stable in time, but their prominence depended heavily on the device operating point. When SNR was mapped across a range of operating points for these devices, a clear peak was discovered, with the hotspot intensity culminating at the peak. Ideal operating points were identified to be near the threshold voltage for both device types, with findings (and a developed numerical model) in MoS2 indicating that the operating point where SNR is maximized may depend upon the extent of the channel that is influenced by the analyte. Observations from multiple devices and approaches revealed that SNR peaks below the point of maximal transconductance, offering increased resolution to a matter that has previously been of some debate in the literature. In MoS2 FETs, a significant asymmetry was discovered in the response of devices to analytes of opposing polarity, with analytes that modulate devices toward their off-state eliciting a much larger response (and, correspondingly, SNR). This asymmetry was confirmed by a numerical model that suggested it to be a general result applicable to all FET-based charge detection sensors, leading to the recommendation that sensor designers select FETs that will be turned off by the target analyte.

Each finding contributed by this dissertation provides insight into future sensor designs and increases clarity of the underlying mechanisms leading to sensitivity in nanomaterial FET-based sensors. The discovery of decay modes, hotspots, ideal operating points, asymmetries, and other trends comprise substantial scientific advancements and propel the field closer to the goal of providing ubiquitous access to critical information, diagnoses, and measurements that promptly and correctly inform decisions.