Yu, Lifeng

PurposeThe 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 MethodsImage 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.ResultsDetectability 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.ConclusionsThis 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.

Purpose Using common datasets, to estimate and compare the diagnostic performance of image‐based denoising techniques or iterative reconstruction algorithms for the task of detecting hepatic metastases. Methods Datasets from contrast‐enhanced CT scans of the liver were provided to participants in an NIH ‐, AAPM ‐ and Mayo Clinic‐sponsored Low Dose CT Grand Challenge. Training data included full‐dose and quarter‐dose scans of the ACR CT accreditation phantom and 10 patient examinations; both images and projections were provided in the training data. Projection data were supplied in a vendor‐neutral standardized format ( DICOM ‐ CT ‐ PD ). Twenty quarter‐dose patient datasets were provided to each participant for testing the performance of their technique. Images were provided to sites intending to perform denoising in the image domain. Fully preprocessed projection data and statistical noise maps were provided to sites intending to perform iterative reconstruction. Upon return of the denoised or iteratively reconstructed quarter‐dose images, randomized, blinded evaluation of the cases was performed using a Latin Square study design by 11 senior radiology residents or fellows, who marked the locations of identified hepatic metastases. Markings were scored against reference locations of clinically or pathologically demonstrated metastases to determine a per‐lesion normalized score and a per‐case normalized score (a faculty abdominal radiologist established the reference location using clinical and pathological information). Scores increased for correct detections; scores decreased for missed or incorrect detections. The winner for the competition was the entry that produced the highest total score (mean of the per‐lesion and per‐case normalized score). Reader confidence was used to compute a Jackknife alternative free‐response receiver operating characteristic ( JAFROC ) figure of merit, which was used for breaking ties. Results 103 participants from 90 sites and 26 countries registered to participate. Training data were shared with 77 sites that completed the data sharing agreements. Subsequently, 41 sites downloaded the 20 test cases, which included only the 25% dose data ( CTDI vol = 3.0 ± 1.8 mG y, SSDE = 3.5 ± 1.3 mG y). 22 sites submitted results for evaluation. One site provided binary images and one site provided images with severe artifacts; cases from these sites were excluded from review and the participants removed from the challenge. The mean (range) per‐lesion and per‐case normalized scores were −24.2% (−75.8%, 3%) and 47% (10%, 70%), respectively. Compared to reader results for commercially reconstructed quarter‐dose images with no noise reduction, 11 of the 20 sites showed a numeric improvement in the mean JAFROC figure of merit. Notably two sites performed comparably to the reader results for full‐dose commercial images. The study was not designed for these comparisons, so wide confidence intervals surrounded these figures of merit and the results should be used only to motivate future testing. Conclusion Infrastructure and methodology were developed to rapidly estimate observer performance for liver metastasis detection in low‐dose CT examinations of the liver after either image‐based denoising or iterative reconstruction. The results demonstrated large differences in detection and classification performance between noise reduction methods, although the majority of methods provided some improvement in performance relative to the commercial quarter‐dose images with no noise reduction applied.

Abstract Photon-counting computed tomography (PCCT) uses a photon counting detector to count individual photons and allocate them to specific energy bins by comparing photon energy to preset thresholds. This enables simultaneous multi-energy CT with a single source and detector. Phantom studies were performed to assess the spectral performance of a research PCCT scanner by assessing the accuracy of derived images sets. Specifically, we assessed the accuracy of iodine quantification in iodine map images and of CT number accuracy in virtual monoenergetic images (VMI). Vials containing iodine with five known concentrations were scanned on the PCCT scanner after being placed in phantoms representing the attenuation of different size patients. For comparison, the same vials and phantoms were also scanned on 2nd and 3rd generation dual-source, dual-energy scanners. After material decomposition, iodine maps were generated, from which iodine concentration was measured for each vial and phantom size and compared with the known concentration. Additionally, VMIs were generated and CT number accuracy was compared to the reference standard, which was calculated based on known iodine concentration and attenuation coefficients at each keV obtained from the U.S. National Institute of Standards and Technology (NIST). Results showed accurate iodine quantification (root mean square error of 0.5 mgI/cc) and accurate CT number of VMIs (percentage error of 8.9%) using the PCCT scanner. The overall performance of the PCCT scanner, in terms of iodine quantification and VMI CT number accuracy, was comparable to that of EID-based dual-source, dual-energy scanners.

PurposeModel observers have been successfully developed and used to assess the quality of static 2D CT images. However, radiologists typically read images by paging through multiple 2D slices (i.e., multislice reading). The purpose of this study was to correlate human and model observer performance in a low‐contrast detection task performed using both 2D and multislice reading, and to determine if the 2D model observer still correlate well with human observer performance in multislice reading.MethodsA phantom containing 18 low‐contrast spheres (6 sizes × 3 contrast levels) was scanned on a 192‐slice CT scanner at five dose levels (CTDIvol = 27, 13.5, 6.8, 3.4, and 1.7 mGy), each repeated 100 times. Images were reconstructed using both filtered‐backprojection (FBP) and an iterative reconstruction (IR) method (ADMIRE, Siemens). A 3D volume of interest (VOI) around each sphere was extracted and placed side‐by‐side with a signal‐absent VOI to create a 2‐alternative forced choice (2AFC) trial. Sixteen 2AFC studies were generated, each with 100 trials, to evaluate the impact of radiation dose, lesion size and contrast, and reconstruction methods on object detection. In total, 1600 trials were presented to both model and human observers. Three medical physicists acted as human observers and were allowed to page through the 3D volumes to make a decision for each 2AFC trial. The human observer performance was compared with the performance of a multislice channelized Hotelling observer (CHO_MS), which integrates multislice image data, and with the performance of previously validated CHO, which operates on static 2D images (CHO_2D). For comparison, the same 16 2AFC studies were also performed in a 2D viewing mode by the human observers and compared with the multislice viewing performance and the two CHO models.ResultsHuman observer performance was well correlated with the CHO_2D performance in the 2D viewing mode [Pearson product‐moment correlation coefficient R = 0.972, 95% confidence interval (CI): 0.919 to 0.990] and with the CHO_MS performance in the multislice viewing mode (R = 0.952, 95% CI: 0.865 to 0.984). The CHO_2D performance, calculated from the 2D viewing mode, also had a strong correlation with human observer performance in the multislice viewing mode (R = 0.957, 95% CI: 879 to 0.985). Human observer performance varied between the multislice and 2D modes. One reader performed better in the multislice mode (P = 0.013); whereas the other two readers showed no significant difference between the two viewing modes (P = 0.057 and P = 0.38).ConclusionsA 2D CHO model is highly correlated with human observer performance in detecting spherical low contrast objects in multislice viewing of CT images. This finding provides some evidence for the use of a simpler, 2D CHO to assess image quality in clinically relevant CT tasks where multislice viewing is used.

PurposeThe vast majority of body CT exams are performed with automatic exposure control (AEC), which adapts the mean tube current to the patient size and modulates the tube current either angularly, longitudinally or both. However, most radiation dose estimation tools are based on fixed tube current scans. Accurate estimates of patient dose from AEC scans require knowledge of the tube current values, which is usually unavailable. The purpose of this work was to develop and validate methods to accurately estimate the tube current values prescribed by one manufacturer's AEC system to enable accurate estimates of patient dose.MethodsMethods were developed that took into account available patient attenuation information, user selected image quality reference parameters and x‐ray system limits to estimate tube current values for patient scans. Methods consistent with AAPM Report 220 were developed that used patient attenuation data that were: (a) supplied by the manufacturer in the CT localizer radiograph and (b) based on a simulated CT localizer radiograph derived from image data. For comparison, actual tube current values were extracted from the projection data of each patient. Validation of each approach was based on data collected from 40 pediatric and adult patients who received clinically indicated chest (n = 20) and abdomen/pelvis (n = 20) scans on a 64 slice multidetector row CT (Sensation 64, Siemens Healthcare, Forchheim, Germany). For each patient dataset, the following were collected with Institutional Review Board (IRB) approval: (a) projection data containing actual tube current values at each projection view, (b) CT localizer radiograph (topogram) and (c) reconstructed image data. Tube current values were estimated based on the actual topogram (actual‐topo) as well as the simulated topogram based on image data (sim‐topo). Each of these was compared to the actual tube current values from the patient scan. In addition, to assess the accuracy of each method in estimating patient organ doses, Monte Carlo simulations were performed by creating voxelized models of each patient, identifying key organs and incorporating tube current values into the simulations to estimate dose to the lungs and breasts (females only) for chest scans and the liver, kidney, and spleen for abdomen/pelvis scans. Organ doses from simulations using the actual tube current values were compared to those using each of the estimated tube current values (actual‐topo and sim‐topo).ResultsWhen compared to the actual tube current values, the average error for tube current values estimated from the actual topogram (actual‐topo) and simulated topogram (sim‐topo) was 3.9% and 5.8% respectively. For Monte Carlo simulations of chest CT exams using the actual tube current values and estimated tube current values (based on the actual‐topo and sim‐topo methods), the average differences for lung and breast doses ranged from 3.4% to 6.6%. For abdomen/pelvis exams, the average differences for liver, kidney, and spleen doses ranged from 4.2% to 5.3%.ConclusionsStrong agreement between organ doses estimated using actual and estimated tube current values provides validation of both methods for estimating tube current values based on data provided in the topogram or simulated from image data.