Exact confidence intervals for channelized Hotelling observer performance in image quality studies

Photon-Counting Detector CT for Liver Lesion Detection-Optimal Virtual Monoenergetic Energy for Different Simulated Patient Sizes and Radiation Doses

OBJECTIVES

The aim of this study was to evaluate the optimal energy level of virtual monoenergetic images (VMIs) from photon-counting detector computed tomography (CT) for the detection of liver lesions as a function of phantom size and radiation dose.

MATERIALS AND METHODS

An anthropomorphic abdominal phantom with liver parenchyma and lesions was imaged on a dual-source photon-counting detector CT at 120 kVp. Five hypoattenuating lesions with a lesion-to-background contrast difference of -30 HU and -45 HU and 3 hyperattenuating lesions with +30 HU and +90 HU were used. The lesion diameter was 5-10 mm. Rings of fat-equivalent material were added to emulate medium- or large-sized patients. The medium size was imaged at a volume CT dose index of 5, 2.5, and 1.25 mGy and the large size at 5 and 2.5 mGy, respectively. Each setup was imaged 10 times. For each setup, VMIs from 40 to 80 keV at 5 keV increments were reconstructed with quantum iterative reconstruction at a strength level of 4 (QIR-4). Lesion detectability was measured as area under the receiver operating curve (AUC) using a channelized Hotelling model observer with 10 dense differences of Gaussian channels.

RESULTS

Overall, highest detectability was found at 65 and 70 keV for both hypoattenuating and hyperattenuating lesions in the medium and large phantom independent of radiation dose (AUC range, 0.91-1.0 for the medium and 0.94-0.99 for the large phantom, respectively). The lowest detectability was found at 40 keV irrespective of the radiation dose and phantom size (AUC range, 0.78-0.99). A more pronounced reduction in detectability was apparent at 40-50 keV as compared with 65-75 keV when radiation dose was decreased. At equal radiation dose, detection as a function of VMI energy differed stronger for the large size as compared with the medium-sized phantom (12% vs 6%).

CONCLUSIONS

Detectability of hypoattenuating and hyperattenuating liver lesions differed between VMI energies for different phantom sizes and radiation doses. Virtual monoenergetic images at 65 and 70 keV yielded highest detectability independent of phantom size and radiation dose.

Dose-efficiency quantification of computed tomography systems using a model-observer

BACKGROUND

Recent advances in computed tomography (CT) technology have considerably improved the quality of CT images and reduced radiation exposure in patients. At present, however, there is no generally accepted figure of merit (FOM) for comparing the dose efficiencies of CT systems.

PURPOSE

(i) To establish an FOM that characterizes the quality of CT images in relation to the radiation dose by means of a mathematical model observer and (ii) to evaluate the new FOM on different CT systems and image reconstruction algorithms.

METHODS

Images of a homogeneous phantom with four low-contrast inserts were acquired using three different CT systems at three dose levels and a representative protocol for CT imaging of low-contrast objects in the abdomen. The images were reconstructed using filtered-back projection and iterative algorithms. A channelized hotelling observer with difference-of-Gaussian channels was applied to compute the detectability (d ' $d^{\prime}$ ). This was done for each insert and each of the considered imaging conditions from square regions of interest (ROIs) that were (semi-)automatically centered on the inserts. The estimated detectabilities (d ' $d^{\prime}$ ) were averaged in the first step over the three dose levels (⟨ d ' ⟩ $\langle {d^{\prime}} \rangle $ ), and subsequently over the four contrast inserts (⟨ d ' ⟩ w ${\langle {d^{\prime}} \rangle _{\rm{w}}}$ ). All calculation steps included a dedicated assessment of the related uncertainties following accepted metrological guidelines.

RESULTS

The determined detectabilities (d ' $d^{\prime}$ ) varied considerably with the contrast and diameter of the four inserts, as well as with the radiation doses and reconstruction algorithms used for image generation (d ' $d^{\prime}\;$ = 1.3-5.5). Thus, the specification of a single detectability as an FOM is not well suited for comprehensively characterizing the dose efficiency of a CT system. A more comprehensive and robust characterization was provided by the averaged detectabilities⟨ d ' ⟩ $\langle {d^{\prime}} \rangle $ and, in particular,⟨ d ' ⟩ w ${\langle {d^{\prime}} \rangle _{\rm{w}}}$ . Our analysis reveals that the model observer analysis is very sensitive to the exact position of the ROIs.

CONCLUSIONS

The presented automatable software approach yielded with the weighted detectability⟨ d ' ⟩ w ${\langle {d^{\prime}} \rangle _{\rm{w}}}$ an objective FOM to benchmark different CT systems and reconstruction algorithms in a robust and reliable manner. An essential advantage of the proposed model-observer approach is that uncertainties in the FOM can be provided, which is an indispensable prerequisite for type testing.

Reducing the risk of hallucinations with interpretable deep learning models for low-dose CT denoising: comparative performance analysis

Objective.Reducing CT radiation dose is an often proposed measure to enhance patient safety, which, however results in increased image noise, translating into degradation of clinical image quality. Several deep learning methods have been proposed for low-dose CT (LDCT) denoising. The high risks posed by possible hallucinations in clinical images necessitate methods which aid the interpretation of deep learning networks. In this study, we aim to use qualitative reader studies and quantitative radiomics studies to assess the perceived quality, signal preservation and statistical feature preservation of LDCT volumes denoised by deep learning. We aim to compare interpretable deep learning methods with classical deep neural networks in clinical denoising performance.Approach.We conducted an image quality analysis study to assess the image quality of the denoised volumes based on four criteria to assess the perceived image quality. We subsequently conduct a lesion detection/segmentation study to assess the impact of denoising on signal detectability. Finally, a radiomic analysis study was performed to observe the quantitative and statistical similarity of the denoised images to standard dose CT (SDCT) images.Main results.The use of specific deep learning based algorithms generate denoised volumes which are qualitatively inferior to SDCT volumes(p< 0.05). Contrary to previous literature, denoising the volumes did not reduce the accuracy of the segmentation (p> 0.05). The denoised volumes, in most cases, generated radiomics features which were statistically similar to those generated from SDCT volumes (p> 0.05).Significance.Our results show that the denoised volumes have a lower perceived quality than SDCT volumes. Noise and denoising do not significantly affect detectability of the abdominal lesions. Denoised volumes also contain statistically identical features to SDCT volumes.

Phantom and methodology for comparison of small lesion detectability in PET

PURPOSE

The main goal of this work is to describe a phantom design, data acquisition and data analysis methodology enabling comparison of small lesion detectability between PET imaging systems and reconstruction algorithms. Several methods are currently available to characterize intrinsic and image quality performance, but none focus exclusively on small lesion detectability.

METHODS

We previously developed a small-lesion detection phantom and described initial results using a head-size phantom. Unlike most fillable nuclear medicine phantoms, this phantom offers a semi-realistic heterogenous background and wall-less contrast features. In this work, the methodology is extended to include (a) the use of both head- and body-sized phantoms and (b) a multi-scan data collection and analysis method. We present an example use case of the phantom and detection estimation methodology, comparing the small-lesion detection performance across four commercial PET/CT systems.

RESULTS

Repeat acquisitions of the phantom enabled estimation of model observer performance and surrogates of detectability. As anticipated, estimated detectability increased with the square root of system sensitivity and TOF offered marked improvement in detectability, especially for the body sized object. The proposed approach characterizing detectability at different times during the decay of the phantom enabled comparison of small lesion detectability at matched activity concentrations (and scan durations) across different scanners.

CONCLUSION

The proposed approach offers a reproducible tool for evaluating relative tradeoffs of system performance on small lesion detectability.

Photon-Counting Detector CT With Quantum Iterative Reconstruction: Impact on Liver Lesion Detection and Radiation Dose Reduction

OBJECTIVES

To assess image noise, diagnostic performance, and potential for radiation dose reduction of photon-counting detector (PCD) computed tomography (CT) with quantum iterative reconstruction (QIR) in the detection of hypoattenuating and hyperattenuating focal liver lesions compared with energy-integrating detector (EID) CT.

MATERIALS AND METHODS

A medium-sized anthropomorphic abdominal phantom with liver parenchyma and lesions (diameter, 5-10 mm; hypoattenuating and hyperattenuating from -30 HU to +90 HU at 120 kVp) was used. The phantom was imaged on ( a ) a third-generation dual-source EID-CT (SOMATOM Force, Siemens Healthineers) in the dual-energy mode at 100 and 150 kVp with tin filtration and ( b ) a clinical dual-source PCD-CT at 120 kVp (NAEOTOM Alpha, Siemens). Scans were repeated 10 times for each of 3 different radiation doses of 5, 2.5, and 1.25 mGy. Datasets were reconstructed as virtual monoenergetic images (VMIs) at 60 keV for both scanners and as linear-blended images (LBIs) for EID-CT. For PCD-CT, VMIs were reconstructed with different strength levels of QIR (QIR 1-4) and without QIR (QIR-off). For EID-CT, VMIs and LBIs were reconstructed using advanced modeled iterative reconstruction at a strength level of 3. Noise power spectrum was measured to compare image noise magnitude and texture. A channelized Hotelling model observer was used to assess diagnostic accuracy for lesion detection. The potential for radiation dose reduction using PCD-CT was estimated for the QIR strength level with the highest area under the curve compared with EID-CT for each radiation dose.

RESULTS

Image noise decreased with increasing QIR level at all radiation doses. Using QIR-4, noise reduction was 41%, 45%, and 59% compared with EID-CT VMIs and 12%, 18%, and 33% compared with EID-CT LBIs at 5, 2.5, and 1.25 mGy, respectively. The peak spatial frequency shifted slightly to lower frequencies at higher QIR levels. Lesion detection accuracy increased at higher QIR levels and was higher for PCD-CT compared with EID-CT VMIs. The improvement in detection with PCD-CT was strongest at the lowest radiation dose, with an area under the receiver operating curve of 0.917 for QIR-4 versus 0.677 for EID-CT VMIs for hyperattenuating lesions, and 0.900 for QIR-4 versus 0.726 for EID-CT VMIs for hypoattenuating lesions. Compared with EID-CT LBIs, detection was higher for QIR 1-4 at 2.5 mGy and for QIR 2-4 at 1.25 mGy (eg, 0.900 for QIR-4 compared with 0.854 for EID-CT LBIs at 1.25 mGy). Radiation dose reduction potential of PCD-CT with QIR-4 was 54% at 5 mGy compared with VMIs and 39% at 2.5 mGy compared with LBIs.

CONCLUSIONS

Compared with EID-CT, PCD-CT with QIR substantially improved focal liver lesion detection, especially at low radiation dose. This enables substantial radiation dose reduction while maintaining diagnostic accuracy.

Linearized Analysis of Noise and Resolution for DL-Based Image Generation

Deep-learning (DL) based CT image generation methods are often evaluated using RMSE and SSIM. By contrast, conventional model-based image reconstruction (MBIR) methods are often evaluated using image properties such as resolution, noise, bias. Calculating such image properties requires time consuming Monte Carlo (MC) simulations. For MBIR, linearized analysis using first order Taylor expansion has been developed to characterize noise and resolution without MC simulations. This inspired us to investigate if linearization can be applied to DL networks to enable efficient characterization of resolution and noise. We used FBPConvNet as an example DL network and performed extensive numerical evaluations, including both computer simulations and real CT data. Our results showed that network linearization works well under normal exposure settings. For such applications, linearization can characterize image noise and resolutions without running MC simulations. We provide with this work the computational tools to implement network linearization. The efficiency and ease of implementation of network linearization can hopefully popularize the physics-related image quality measures for DL applications. Our methodology is general; it allows flexible compositions of DL nonlinear modules and linear operators such as filtered-backprojection (FBP). For the latter, we develop a generic method for computing the covariance images that is needed for network linearization.

A nonparametric measure of noise in x-ray diagnostic images-mammography

Objective.In x-ray diagnostics, modern image reconstruction or image processing methods may render established methods of image quality assessment inadequate. Task specific quality assessment by using model observers has the disadvantage of being very labour-intensive. Therefore, it appears highly desirable to develop novel image quality parameters that neither rely on the linearity and the shift-invariace of the imaging system nor require the acquisition of hundreds of images as is necessary for the application of model observers, and which can be derived directly from diagnostic images.Approach.A new measure for the noise based on non-maximum-suppression images is defined and its properties are explored using simulated images before it is applied to an exposure series of mammograms of a homogeneous phantom and a 3D-printed breast phantom to demonstrate its usefulness under realistic conditions.Main results.The new noise parameter cannot only be derived from images with a homogeneous background but it can be extracted directly from images containing anatomic structures and is proportional to the standard deviation of the noise. At present, the applicability is restricted to mammography, which satisfies the assumption of short covariance length of the noise.Significance.The new measure of the noise is but a first step of the development of a set of parameters that are required to quantify image quality directly from diagnostic images without relying on the assumption of a linear, shift-invariant system, e.g. by providing measures of sharpness, contrast and structural complexity, in addition to the noise measure. For mammography, a convenient method is now available to quantify noise in processed diagnostic images.

Effect of optical blurring of X-ray source on breast tomosynthesis image quality: Modulation transfer function, anatomical noise power spectrum, and signal detectability perspectives

We investigated the effect of the optical blurring of X-ray source on digital breast tomosynthesis (DBT) image quality using well-designed DBT simulator and table-top experimental systems. To measure the in-plane modulation transfer function (MTF), we used simulated sphere phantom and Teflon sphere phantom and generated their projection data using two acquisition modes (i.e., step-and-shoot mode and continuous mode). After reconstruction, we measured the in-plane MTF using reconstructed sphere phantom images. In addition, we measured the anatomical noise power spectrum (aNPS) and signal detectability. We constructed simulated breast phantoms with a 50% volume glandular fraction (VGF) of breast anatomy using the power law spectrum and inserted spherical objects with 1 mm, 2 mm, and 5 mm diameters as breast masses. Projection data were acquired using two acquisition modes, and in-plane breast images were reconstructed using the Feldkamp-Davis-Kress (FDK) algorithm. For the experimental study, we used BR3D breast phantom with 50% VGF and obtained projection data using a table-top experimental system. To compare the detection performance of the two acquisition modes, we calculated the signal detectability using the channelized Hotelling observer (CHO) with Laguerre-Gauss (LG) channels. Our results show that spatial resolution of in-plane image in continuous mode was degraded due to the optical blurring of X-ray source. This blurring effect was reflected in aNPS, resulting in large β values. From a signal detectability perspective, the signal detectability in step-and-shoot mode is higher than that in continuous mode for small spherical signals but not large spherical signals. Although the step-and-shoot mode has disadvantage in terms of scan time compared to the continuous mode, scanning in step-and-shoot mode is better for detecting small signals, indicating that there is a tradeoff between scan time and image quality.

The regression detectability index RDI for mammography images of breast phantoms with calcification-like objects and anatomical background

Currently, quality assurance measurements in mammography are performed on unprocessed images. For diagnosis, however, radiologists are provided with processed images. This image processing is optimised for images of human anatomy and therefore does not always perform satisfactorily with technical phantoms. To overcome this problem, it may be possible to use anthropomorphic phantoms reflecting the anatomic structure of the human breast in place of technical phantoms when carrying out task-specific quality assessment using model observers. However, the use of model observers is hampered by the fact that a large number of images needs to be acquired. A recently published novel observer called the regression detectability index (RDI) needs significantly fewer images, but requires the background of the images to be flat. Therefore, to be able to apply the RDI to images of anthropomorphic phantoms, the anatomic background needs to be removed. For this, a procedure in which the anatomical structures are fitted by thin plate spline (TPS) interpolation has been developed. When the object to be detected is small, such as a calcification-like lesion, it is shown that the anatomic background can be removed successfully by subtracting the TPS interpolation, which makes the background-free image accessible to the RDI. We have compared the detectability obtained by the RDI with TPS background subtraction to results of the channelized Hotelling observer (CHO) and human observers. With the RDI, results for the detectabilityd'can be obtained using 75% fewer images compared to the CHO, while the same uncertainty ofd'is achieved. Furthermore, the correlation ofd'(RDI) with the results of human observers is at least as good as that ofd'(CHO) with human observers.

Assessment of task-based image quality for abdominal CT protocols linked with national diagnostic reference levels

Objectives

To assess task-based image quality for two abdominal protocols on various CT scanners. To establish a relationship between diagnostic reference levels (DRLs) and task-based image quality.

Methods

A protocol for the detection of focal liver lesions was used to scan an anthropomorphic abdominal phantom containing 8- and 5-mm low-contrast (20 HU) spheres at five CTDI_vol levels (4, 8, 12, 16, and 20 mGy) on 12 CTs. Another phantom with high-contrast calcium targets (200 HU) was scanned at 2, 4, 6, 10, and 15 mGy using a renal stones protocol on the same CTs. To assess the detectability, a channelized Hotelling observer was used for low-contrast targets and a non-prewhitening observer with an eye filter was used for high contrast targets. The area under the ROC curve and signal to noise ratio were used as figures of merit.

Results

For the detection of 8-mm spheres, the image quality reached a high level (mean AUC over all CTs higher than 0.95) at 11 mGy. For the detection of 5-mm spheres, the AUC never reached a high level of image quality. Variability between CTs was found, especially at low dose levels. For the search of renal stones, the AUC was nearly maximal even for the lowest dose level.

Conclusions

Comparable task-based image quality cannot be reached at the same dose level on all CT scanners. This variability implies the need for scanner-specific dose optimization.

Key Points

• There is an image quality variability for subtle low-contrast lesion detection in the clinically used dose range.

• Diagnostic reference levels were linked with task-based image quality metrics.

• There is a need for specific dose optimization for each CT scanner and clinical protocol.

Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction

OBJECTIVES

To compare deep learning (True Fidelity, TF) and partial model based Iterative Reconstruction (ASiR-V) algorithm for image texture, low contrast lesion detectability and potential dose reduction.

METHODS

Anthropomorphic phantoms (mimicking non-overweight and overweight patient), containing lesions of 6 mm in diameter with 20HU contrast, were scanned at five different dose levels (2,6,10,15,20 mGy) on a CT system, using clinical routine protocols for liver lesion detection. Images were reconstructed using ASiR-V 0% (surrogate for FBP), 60 % and TF at low, medium and high strength. Noise texture was characterized by computing a normalized Noise Power Spectrum filtered by an eye filter. The similarity against FBP texture was evaluated using peak frequency difference (PFD) and root mean square deviation (RMSD). Low contrast detectability was assessed using a channelized Hotelling observer and the area under the ROC curve (AUC) was used as figure of merit. Potential dose reduction was calculated to obtain the same AUC for TF and ASiR-V.

RESULTS

FBP-like noise texture was more preserved with TF (PFD from -0.043mm-1 to -0.09mm-1, RMSD from 0.12mm-1 to 0.21mm-1) than with ASiR-V (PFD equal to 0.12 mm-1, RMSD equal to 0.53mm-1), resulting in a sharper image. AUC was always higher with TF than ASIR-V. In average, TF compared to ASiR-V, enabled a radiation dose reduction potential of 7%, 25 % and 33 % for low, medium and high strength respectively.

CONCLUSION

Compared to ASIR-V, TF at high strength does not impact noise texture and maintains low contrast liver lesions detectability at significant lower dose.

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

CT metal artifact reduction algorithms: Toward a framework for objective performance assessment

Purpose

Although several metal artifact reduction (MAR) algorithms for computed tomography (CT) scanning are commercially available, no quantitative, rigorous, and reproducible method exists for assessing their performance. The lack of assessment methods poses a challenge to regulators, consumers, and industry. We explored a phantom‐based framework for assessing an important aspect of MAR performance: how applying MAR in the presence of metal affects model observer performance at a low‐contrast detectability (LCD) task This work is, to our knowledge, the first model observer–based framework for the evaluation of MAR algorithms in the published literature.

Methods

We designed a numerical head phantom with metal implants. In order to incorporate an element of randomness, the phantom included a rotatable inset with an inhomogeneous background. We generated simulated projection data for the phantom. We applied two variants of a simple MAR algorithm, sinogram inpainting, to the projection data, that we reconstructed using filtered backprojection. To assess how MAR affected observer performance, we examined the detectability of a signal at the center of a region of interest (ROI) by a channelized Hotelling observer (CHO). As a figure of merit, we used the area under the ROC curve (AUC).

Results

We used simulation to test our framework on two variants of the MAR technique of sinogram inpainting. We found that our method was able to resolve the difference in two different MAR algorithms’ effect on LCD task performance, as well as the difference in task performances when MAR was applied, vs not.

Conclusion

We laid out a phantom‐based framework for objective assessment of how MAR impacts low‐contrast detectability, that we tested on two MAR algorithms. Our results demonstrate the importance of testing MAR performance over a range of object and imaging parameters, since applying MAR does not always improve the quality of an image for a given diagnostic task. Our framework is an initial step toward developing a more comprehensive objective assessment method for MAR, which would require developing additional phantoms and methods specific to various clinical applications of MAR, and increasing study efficiency.

RDI[Formula: see text]a regression detectability index for quality assurance in: x-ray imaging

Novel iterative image reconstruction methods can help reduce the required radiation dose in x-ray diagnostics such as computed tomography (CT), while maintaining sufficient image quality. Since some of the established image quality measures are not appropriate for reliably judging the quality of images derived by iterative methods, alternative approaches such as task-specific quality assessment would be highly desirable for acceptance or constancy testing. Task-based image quality methods are also closer to tasks performed by the radiologists, such as lesion detection. However, this approach is usually hampered by a huge workload, since hundreds of images are usually required for its application. It is demonstrated that the proposed approach works reliably on the basis of significantly fewer images, and that it correlates well with results obtained from human observers.

Human observer performance on in-plane digital breast tomosynthesis images: Effects of reconstruction filters and data acquisition angles on signal detection

For digital breast tomosynthesis (DBT) systems, we investigate the effects of the reconstruction filters for different data acquisition angles on signal detection. We simulated a breast phantom with a 30% volume glandular fraction (VGF) of breast anatomy using the power law spectrum and modeled the breast mass as a spherical object with a 1 mm diameter. Projection data were acquired using two different data acquisition angles and numbers of projection view pairs, and in-plane breast images were reconstructed using the Feldkamp-Davis-Kress (FDK) algorithm with three different reconstruction filter schemes. To measure the ability to detect a signal, we conducted the human observer study with a binary detection task and compared the signal detectability of human to that of channelized Hotelling observer (CHO) with Laguerre-Gauss (LG) channels and dense difference-of-Gaussian (D-DOG) channels. We also measured the contrast-to-noise ratio (CNR), signal power spectrum (SPS), and β values of the anatomical noise power spectrum (NPS) to show the association between human observer performance and these traditional metrics. Our results show that using a slice thickness (ST) filter degraded the signal detection performance of human observers at the same data acquisition angle. This could be predicted by D-DOG CHO with internal noise, but the correlation between the traditional metrics and signal detectability was not observed in this work.

Radiation dose reduction and static image quality assessment using a channelized hotelling observer on an angiography system upgraded with clarity IQ

The goal of this paper was the comparison of radiation dose and imaging quality before and after the Clarity IQ technology installation in a Philips AlluraXper FD20/20 angiography system using a Channelized Hotelling Observer model (CHO). The core characteristics of the Allura Clarity IQ technology are its real-time noise reduction algorithms (NRT) combined with state-of-the-art hardware; this technology allows to implement acquisition protocols able to significantly reduce patient entrance dose. To measure the system performances in terms of image quality we used a contrast detail phantom in a clinical scatter condition. A Leeds TO10 phantom has been imaged between two 10 cm thick homogeneous solid water slabs. Fluoroscopy images were acquired using a cerebral protocol at 3 dose levels (low, medium and high) with a field- of view (FOV) of 31 cm. Cineangiography images were acquired using a cerebral protocol at 2 fps. Thus, 4 acquisitions were obtained for the conventional technology and 4 acquisitions were taken after the Clarity IQ upgrade, for a total of 8 different image sets. A validated 40 Gabor channels CHO with an internal noise model compared the image sets. Human observers' studies were carried out to tune the internal noise parameter. We showed that the CHO did not detect any significant difference between any of the image sets acquired using the two technologies. Consequently, this x-ray imaging technology provides a non-inferior image quality with an average patient dose reduction of 57% and 28% respectively in cineangiography and fluoroscopy. The Clarity IQ installation has certainly allowed a considerable improvement in patient and staff safety, while maintaining the same image quality.

Assessment of CT Image Quality Using a Bayesian Framework

In computed tomography, there is a tradeoff between the quality of the reconstructed image and the radiation dose received by the patient. In order to find an appropriate compromise between the image quality of the reconstructed images and the radiation dose, it is important to have reliable methods for evaluating the quality of the reconstructed images. A successful family of methods for the assessment of image quality is task-based image quality assessment, which often involves the use of model observers, and which assesses the quality of the image reconstruction by deriving a figure of merit. Here, we present a Bayesian framework that can be used in task-based image quality assessment. Our framework is applicable to binary classification problems with normally distributed observations, and we make the additional assumption that the covariance matrix is the same in both image classes. We choose a particular non-informative prior for the parameters of our model, which allows us to derive an expression for the Bayes factor for the binary classification problem which to the best of our knowledge is novel. We introduce a novel model observer based on this Bayes factor. Further, we have developed a methodology for estimating the posterior distribution of the figure of merit for this type of classification problem. Compared with classical statistical approaches, our Bayesian approach has the advantage that it provides a full characterization of the uncertainty of the figure of merit. Our choice of prior allows us to design a simple Monte Carlo algorithm to efficiently sample the posterior of the figure of merit of the ideal observer, in contrast to common Bayesian procedures which rely on computationally expensive Markov chain Monte Carlo sampling. We have shown that for training samples of sufficient size, our estimated credible intervals for the figure of merit have coverage probabilities close to their credibility, so that our approach can reasonably be used within a classical statistical framework as well.

Assessment of collimators in radium-223 imaging with channelized Hotelling observer: a simulation study

OBJECTIVE

Radium-223 (²²³Ra) is used in unsealed radionuclide therapy for metastatic bone tumors. The aim of this study is to apply a computational model observer to ²²³Ra planar images, and to assess the performance of collimators in ²²³Ra imaging.

METHODS

The ²²³Ra planar images were created via an in-house Monte Carlo simulation code using HEXAGON and NAI modules. The phantom was a National Electrical Manufacturers Association body phantom with a hot sphere. The concentration of the background was 55 Bq/mL, and the sphere was approximately 1.5-20 times that of the background concentration. The acquisition time was 10 min. The photopeaks (and the energy window) were 84 (full width of energy window: 20%), 154 (15%), and 270 keV (10%). Each 40 images, with and without hot concentration, were applied to a three-channel difference-of-Gaussian channelized Hotelling observer (CHO), and the signal-to-noise ratio (SNR) of the hot region was calculated. The images were examined using five different collimators: two low-energy general-purpose (LEGP), two medium-energy general-purpose (MEGP), and one high-energy general-purpose (HEGP) collimators.

RESULTS

The SNR value was linearly proportional to the contrast of the hot region for all collimators and energy windows. The images of the 84-keV energy window with the MEGP collimator that have thicker septa and larger holes produced the highest SNR value. The SNR values of two LEGP collimators were approximately half of the MEGP collimators. The HEGP collimator was halfway between the MEGP and LEGP. Similar characteristics were observed for other energy windows (154, 270 keV). The SNR value of images captured via the 270-keV energy window was larger than 154-keV, although the sensitivity of the 270-keV energy window is lower than 154-keV. The results suggested a positive correlation between the SNR value and the fraction of unscattered photons.

CONCLUSIONS

The SNR value of CHO reflected the performance of collimators and was available to assess and quantitatively evaluate the collimator performance in ²²³Ra imaging. The SNR value depends on the magnitudes of unscattered photon count and the fraction of unscattered photon count. Consequently, in this study, MEGP collimators performed better than LEGP and HEGP collimators for ²²³Ra imaging.

Inter-laboratory comparison of channelized hotelling observer computation

PURPOSE

The task-based assessment of image quality using model observers is increasingly used for the assessment of different imaging modalities. However, the performance computation of model observers needs standardization as well as a well-established trust in its implementation methodology and uncertainty estimation. The purpose of this work was to determine the degree of equivalence of the channelized Hotelling observer performance and uncertainty estimation using an intercomparison exercise.

MATERIALS AND METHODS

Image samples to estimate model observer performance for detection tasks were generated from two-dimensional CT image slices of a uniform water phantom. A common set of images was sent to participating laboratories to perform and document the following tasks: (a) estimate the detectability index of a well-defined CHO and its uncertainty in three conditions involving different sized targets all at the same dose, and (b) apply this CHO to an image set where ground truth was unknown to participants (lower image dose). In addition, and on an optional basis, we asked the participating laboratories to (c) estimate the performance of real human observers from a psychophysical experiment of their choice. Each of the 13 participating laboratories was confidentially assigned a participant number and image sets could be downloaded through a secure server. Results were distributed with each participant recognizable by its number and then each laboratory was able to modify their results with justification as model observer calculation are not yet a routine and potentially error prone.

RESULTS

Detectability index increased with signal size for all participants and was very consistent for 6 mm sized target while showing higher variability for 8 and 10 mm sized target. There was one order of magnitude between the lowest and the largest uncertainty estimation.

CONCLUSIONS

This intercomparison helped define the state of the art of model observer performance computation and with thirteen participants, reflects openness and trust within the medical imaging community. The performance of a CHO with explicitly defined channels and a relatively large number of test images was consistently estimated by all participants. In contrast, the paper demonstrates that there is no agreement on estimating the variance of detectability in the training and testing setting.

Method for Adapting the Grayscale Standard Display Function to the Aging Eye

Perceptual linearity of grayscale images based on a contrast sensitivity model is a widely recognized and used standard for medical imaging visualization. This approach ensures consistency across devices and provides perception of luminance variations in direct relationship to changes in image values. We analyze the effect of aging of the human eye on the precept of linearity and demonstrate that not only the number of just-noticeable differences diminishes for older subjects but also linearity across the range of luminance values is significantly affected. While loss of JNDs is inevitable for a fixed luminance range, our findings suggest possible corrective approaches for maintaining linearity.

A simple parametric model observer for quality assurance in computer tomography

Model observers are mathematical classifiers that are used for the quality assessment of imaging systems such as computer tomography. The quality of the imaging system is quantified by means of the performance of a selected model observer. For binary classification tasks, the performance of the model observer is defined by the area under its ROC curve (AUC). Typically, the AUC is estimated by applying the model observer to a large set of training and test data. However, the recording of these large data sets is not always practical for routine quality assurance. In this paper we propose as an alternative a parametric model observer that is based on a simple phantom, and we provide a Bayesian estimation of its AUC. It is shown that a limited number of repeatedly recorded images (10-15) is already sufficient to obtain results suitable for the quality assessment of an imaging system. A MATLAB® function is provided for the calculation of the results. The performance of the proposed model observer is compared to that of the established channelized Hotelling observer and the nonprewhitening matched filter for simulated images as well as for images obtained from a low-contrast phantom on an x-ray tomography scanner. The results suggest that the proposed parametric model observer, along with its Bayesian treatment, can provide an efficient, practical alternative for the quality assessment of CT imaging systems.

Evaluation of lesion detectability in positron emission tomography when using a convergent penalized likelihood image reconstruction method

We have previously developed a convergent penalized likelihood (PL) image reconstruction algorithm using the relative difference prior (RDP) and showed that it achieves more accurate lesion quantitation compared to ordered subsets expectation maximization (OSEM). We evaluated the detectability of low-contrast liver and lung lesions using the PL-RDP algorithm compared to OSEM. We performed a two-alternative forced choice study using a channelized Hotelling observer model that was previously validated against human observers. Lesion detectability showed a stronger dependence on lesion size for PL-RDP than OSEM. Lesion detectability was improved using time-of-flight (TOF) reconstruction, with greater benefit for the liver compared to the lung and with increasing benefit for decreasing lesion size and contrast. PL detectability was statistically significantly higher than OSEM for 20 mm liver lesions when contrast was [Formula: see text] ([Formula: see text]), and TOF PL detectability was statistically significantly higher than TOF OSEM for 15 and 20 mm liver lesions with contrast [Formula: see text] and [Formula: see text], respectively. For all other cases, there was no statistically significant difference between PL and OSEM ([Formula: see text]). For the range of studied lesion properties, lesion detectability using PL-RDP was equivalent or improved compared to using OSEM.

Correlation between human detection accuracy and observer model-based image quality metrics in computed tomography

The purpose of this study was to compare computed tomography (CT) low-contrast detectability from human readers with observer model-based surrogates of image quality. A phantom with a range of low-contrast signals (five contrasts, three sizes) was imaged on a state-of-the-art CT scanner (Siemens' force). Images were reconstructed using filtered back projection and advanced modeled iterative reconstruction and were assessed by 11 readers using a two alternative forced choice method. Concurrently, contrast-to-noise ratio (CNR), area-weighted CNR (CNRA), and observer model-based metrics were estimated, including nonprewhitening (NPW) matched filter, NPW with eye filter (NPWE), NPW with internal noise, NPW with an eye filter and internal noise (NPWEi), channelized Hotelling observer (CHO), and CHO with internal noise (CHOi). The correlation coefficients (Pearson and Spearman), linear discriminator error, [Formula: see text], and magnitude of confidence intervals, [Formula: see text], were used to determine correlation, proper characterization of the reconstruction algorithms, and model precision, respectively. Pearson (Spearman) correlation was 0.36 (0.33), 0.83 (0.84), 0.84 (0.86), 0.86 (0.88), 0.86 (0.91), 0.88 (0.90), 0.85 (0.89), and 0.87 (0.84), [Formula: see text] was 0.25, 0.15, 0.2, 0.25, 0.3, 0.25, 0.4, and 0.45, and [Formula: see text] was [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] for CNR, CNRA, NPW, NPWE, NPWi, NPWEi, CHO, and CHOi, respectively.

Factors affecting the normality of channel outputs of channelized model observers: an investigation using realistic myocardial perfusion SPECT images

The channelized Hotelling observer (CHO) uses the first- and second-order statistics of channel outputs under both hypotheses to compute test statistics used in binary classification tasks. If these input data deviate from a multivariate normal (MVN) distribution, the classification performance will be suboptimal compared to an ideal observer operating on the same channel outputs. We conducted a comprehensive investigation to rigorously study the validity of the MVN assumption under various kinds of background and signal variability in a realistic population of phantoms. The study was performed in the context of myocardial perfusion SPECT imaging; anatomical, uptake (intensity), and signal variability were simulated. Quantitative measures and graphical approaches applied to the outputs of each channel were used to investigate the amount and type of deviation from normality. For some types of background and signal variations, the channel outputs, under both hypotheses, were non-normal (i.e., skewed or multimodal). This indicates that, for realistic medical images in cases where there is signal or background variability, the normality of the channel outputs should be evaluated before applying a CHO. Finally, the different degrees of departure from normality of the various channels are explained in terms of violations of the central limit theorem.