A Generalizable Framework for Kidney Stone Composition Characterization Using Dual-Energy CT.

Rationale And Objectives: Classification of non-uric acid (NUA) renal stones in dual-energy CT (DECT) is difficult due to their similar CT number ratios (CTRs) and because the CTRs change with patient size and acquisition protocol. In this work, we developed a generalizable framework to estimate correct CTR threshold for different stone types, protocols, and patient sizes and validated the results on two DECT scanners.

Materials And Methods: Our framework assumes generic x-ray spectra, estimates the added filtration to match half-value-layer (HVL) measurements, and predicts the CTR of each stone type from the chemical composition and patient size.

Synthesizing High-Resolution Dual-Energy Radiographs from Coronary Artery Calcium CT Images.

Generating realistic radiographs from CT is mainly limited by the native spatial resolution of the latter. Here we present a general approach for synthesizing high-resolution digitally reconstructed radiographs (DRRs) from an arbitrary resolution CT volume. Our approach is based on an upsampling framework where tissues of interest are first segmented from the original CT volume and then upsampled individually to the desired voxelization (here ~1 mm → 0.

Imaging performance of a LaBr:Ce scintillation detector for photon counting x-ray computed tomography: Simulation study.

Background: Photon counting detectors (PCDs) for x-ray computed tomography (CT) are the future of CT imaging. At present, semiconductor-based PCDs such as cadmium telluride (CdTe), cadmium zinc telluride, and silicon have been either used or investigated for clinical PCD CT. Unfortunately, all of them have the same major challenges, namely high cost and limited spectral signal-to-noise ratio (SNR).

Existence, uniqueness, and efficiency of numerically unbiased attenuation pathlength estimators for photon counting detectors at low count rates.

Background: The first step in computed tomography (CT) reconstruction is to estimate attenuation pathlength. Usually, this is done with a logarithm transformation, which is the direct solution to the Beer-Lambert Law. At low signals, however, the logarithm estimator is biased.

Low-dose computed tomography perceptual image quality assessment.

In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered the gold standard in medical imaging, these evaluations can be time-consuming and costly. Thus, objective methods, such as the peak signal-to-noise ratio and structural similarity index measure, are often employed as alternatives.

Peripheral liver metastases are more frequently missed than central metastases in contrast-enhanced CT: insights from a 25-reader performance study.

Purpose: Subtle liver metastases may be missed in contrast enhanced CT imaging. We determined the impact of lesion location and conspicuity on metastasis detection using data from a prior reader study.

Methods: In the prior reader study, 25 radiologists examined 40 CT exams each and circumscribed all suspected hepatic metastases.

Automated Web-based Software for CT Quality Control Testing of Low-contrast Detectability using Model Observers.

The Channelized Hotelling observer (CHO) is well correlated with human observer performance in many CT detection/classification tasks but has not been widely adopted in routine CT quality control and performance evaluation, mainly because of the lack of an easily available, efficient, and validated software tool. We developed a highly automated solution - CT image quality evaluation and Protocol Optimization (CTPro), a web-based software platform that includes CHO and other traditional image quality assessment tools such as modulation transfer function and noise power spectrum. This tool can allow easy access to the CHO for both the research and clinical community and enable efficient, accurate image quality evaluation without the need of installing additional software.

Evaluation of data uncertainty for deep-learning-based CT noise reduction using ensemble patient data and a virtual imaging trial framework.

Deep learning-based image reconstruction and noise reduction (DLIR) methods have been increasingly deployed in clinical CT. Accurate assessment of their data uncertainty properties is essential to understand the stability of DLIR in response to noise. In this work, we aim to evaluate the data uncertainty of a DLIR method using real patient data and a virtual imaging trial framework and compare it with filtered-backprojection (FBP) and iterative reconstruction (IR).

3D printed phantom with 12 000 submillimeter lesions to improve efficiency in CT detectability assessment.

Background: The detectability performance of a CT scanner is difficult to precisely quantify when nonlinearities are present in reconstruction. An efficient detectability assessment method that is sensitive to small effects of dose and scanner settings is desirable. We previously proposed a method using a search challenge instrument: a phantom is embedded with hundreds of lesions at random locations, and a model observer is used to detect lesions.

Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution.

Background: Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images.

Purpose: In this work, we aim to develop a patient-data-based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method.

Spectral information content of Compton scattering events in silicon photon counting detectors.

Background: Silicon (Si) is a possible sensor material for photon counting detectors (PCDs). A major drawback of Si is that roughly two-thirds of x-ray interactions in the diagnostic energy range are Compton scattering. Because Compton scattering is an energy-insensitive process, it is commonly assumed that Compton events retain little spectral information.

Possible improvements in effective fill factor using X-ray fluorescent interpixel reflectors.

Background: The spatial resolution of energy-integrating diagnostic CT scanners is limited by interpixel reflectors on the detector, which optically isolate pixels but create dead space. Because the width of the reflector cannot easily be decreased, fill factor diminishes as resolution increases.

Purpose: We propose loading (or mixing) a high-Z element into the reflectors, causing the reflectors to be X-ray fluorescent.

Direct energy binning for photon counting detectors: Simulation study.

Background: Photon counting detectors (PCDs) for x-ray computed tomography (CT) face spectral distortion from pulse pileup and charge sharing. The photon counting scheme used by many PCDs is threshold-subtract (TS) with pulse height analysis (PHA), where each counter counts up-crossing events when pulses exceed an energy threshold. PCD data are not Poisson-distributed due to charge sharing and pulse pileup, but the counting statistics have never been studied yet.

Supervised Learning for CT Denoising and Deconvolution Without High-Resolution Reference Images.

Purpose: Convolutional neural networks (CNNs) have been proposed for super-resolution in CT, but training of CNNs requires high-resolution reference data. Higher spatial resolution can also be achieved using deconvolution, but conventional deconvolution approaches amplify noise. We develop a CNN that mitigates increasing noise and that does not require higher-resolution reference images.

Technical note: Exploring the detectability of coronary calcification using ultra-high-resolution photon-counting-detector CT.

Background: Coronary calcification is a strong indicator of coronary artery disease, and patients with a "zero" coronary calcification score have a much lower risk of future cardiac events than those with even small amounts of calcium. However, false-negative (incorrect zero scores) may occur if small calcifications are missed at CT due to limited spatial resolution.

Purpose: To demonstrate lower limits of detection for coronary calcification using an ultra-high-resolution (UHR) mode on a clinical photon-counting-detector CT (PCD-CT), compared to a conventional energy-integrating-detector CT (EID-CT).

Targeted Training Reduces Search Errors but Not Classification Errors for Hepatic Metastasis Detection at Contrast-Enhanced CT.

Rationale And Objectives: Methods are needed to improve the detection of hepatic metastases. Errors occur in both lesion detection (search) and decisions of benign versus malignant (classification). Our purpose was to evaluate a training program to reduce search errors and classification errors in the detection of hepatic metastases in contrast-enhanced abdominal computed tomography (CT).

Contrast- and noise-dependent spatial resolution measurement for deep convolutional neural network-based noise reduction in CT using patient data.

Deep convolutional neural network (DCNN)-based noise reduction methods have been increasingly deployed in clinical CT. Accurate assessment of their spatial resolution properties is required. Spatial resolution is typically measured on physical phantoms, which may not represent the true performance of DCNN in patients as it is typically trained and tested with patient images and the generalizability of DNN to physical phantoms is questionable.

A training program to reduce reader search errors for liver metastasis detection in CT.

Detection of low contrast liver metastases varies between radiologists. Training may improve performance for lower-performing readers and reduce inter-radiologist variability. We recruited 31 radiologists (15 trainees, 8 non-abdominal staff, and 8 abdominal staff) to participate in four separate reading sessions: pre-test, search training, classification training, and post-test.

Patient-specific uncertainty and bias quantification of non-transparent convolutional neural network model through knowledge distillation and Bayesian deep learning.

Assessing the reliability of convolutional neural network (CNN)-based CT imaging techniques is critical for reliable deployment in practice. Some evaluation methods exist but require full access to target CNN architecture and training data, something not available for proprietary or commercial algorithms. Moreover, there is a lack of systematic evaluation methods.

Aberration correction in diagnostic ultrasound: A review of the prior field and current directions.

Medical ultrasound images are reconstructed with simplifying assumptions on wave propagation, with one of the most prominent assumptions being that the imaging medium is composed of a constant sound speed. When the assumption of a constant sound speed are violated, which is true in most in vivoor clinical imaging scenarios, distortion of the transmitted and received ultrasound wavefronts appear and degrade the image quality. This distortion is known as aberration, and the techniques used to correct for the distortion are known as aberration correction techniques.

A dense search challenge phantom fabricated with pixel-based 3D printing for precise detectability assessment.

The performance of a CT scanner for detectability tasks is difficult to precisely measure. Metrics such as contrast-to-noise ratio, modulation transfer function, and noise power spectrum do not predict detectability in the context of nonlinear reconstruction. We propose to measure detectability using a dense search challenge: a phantom is embedded with hundreds of target objects at random locations, and a human or numerical observer analyzes the reconstruction and reports on suspected locations of all target objects.

Estimating the minimum SNR necessary for object detection in the projection domain.

The Rose criterion, stating that an object is detectable if it is five standard deviations above background, has been used as a rule of thumb for decades but its applicability is limited in computed tomography. Recent denoising algorithms, powered by convolutional neural networks, promise to reveal objects that were previously obscured by noise, but any denoising algorithm is fundamentally limited by the statistics of the sinogram. In this work, we estimate the minimum SNR necessary for detecting one of a set of objects in the projection domain.

Prediction of Kidney Stone Fragility Using Radiomics-Based Regression Models.

The surgical technique for urinary stone removal is partly influenced by its fragility, as prognosticated by the clinician. This feasibility study aims to develop a linear regression model from CT-based radiomic markers to predict kidney stone comminution time with two ultrasonic lithotrites. Patients identified by urologists at our institution as eligible candidates for percutaneous nephrolithotomy were prospectively enrolled.