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 on image domain. Fully pre-processed 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 gastro-intestinal 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 (CTDIvol = 3.0 ± 1.8 mGy, SSDE = 3.5 ± 1.3 mGy). 22 sites submitted results for evaluation. One site provided binary images and 1 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 2 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. Conclus...
The use of Monte Carlo simulations in diagnostic medical imaging research is widespread due to its flexibility and ability to estimate quantities that are challenging to measure empirically. However, any new Monte Carlo simulation code needs to be validated before it can be used reliably. The type and degree of validation required depends on the goals of the research project, but, typically, such validation involves either comparison of simulation results to physical measurements or to previously published results obtained with established Monte Carlo codes. The former is complicated due to nuances of experimental conditions and uncertainty, while the latter is challenging due to typical graphical presentation and lack of simulation details in previous publications. In addition, entering the field of Monte Carlo simulations in general involves a steep learning curve. It is not a simple task to learn how to program and interpret a Monte Carlo simulation, even when using one of the publicly available code packages. This Task Group report provides a common reference for benchmarking Monte Carlo simulations across a range of Monte Carlo codes and simulation scenarios. In the report, all simulation conditions are provided for six different Monte Carlo simulation cases that involve common x-ray based imaging research areas. The results obtained for the six cases using four publicly available Monte Carlo software packages are included in tabular form. In addition to a full description of all simulation conditions and results, a discussion and comparison of results among the Monte Carlo packages and the lessons learned during the compilation of these results are included. This abridged version of the report includes only an introductory description of the six cases and a brief example of the results of one of the cases. This work provides an investigator the necessary information to benchmark his/her Monte Carlo simulation software against the reference cases included here before performing his/her own novel research. In addition, an investigator entering the field of Monte Carlo simulations can use these descriptions and results as a self-teaching tool to ensure that he/she is able to perform a specific simulation correctly. Finally, educators can assign these cases as learning projects as part of course objectives or training programs.
Purpose: In AAPM Task Group 204, the size-specific dose estimate (SSDE) was developed by providing size adjustment factors which are applied to the Computed Tomography (CT) standardized dose metric, CTDI vol . However, that work focused on fixed tube current scans and did not specifically address tube current modulation (TCM) scans, which are currently the majority of clinical scans performed. The purpose of this study was to extend the SSDE concept to account for TCM by investigating the feasibility of using anatomic and organ specific regions of scanner output to improve accuracy of dose estimates. Methods: Thirty-nine adult abdomen/pelvis and 32 chest scans from clinically indicated CT exams acquired on a multidetector CT using TCM were obtained with Institutional Review Board approval for generating voxelized models. Along with image data, raw projection data were obtained to extract TCM functions for use in Monte Carlo simulations. Patient size was calculated using the effective diameter described in TG 204. In addition, the scanner-reported CTDI vol (CTDI vol,global ) was obtained for each patient, which is based on the average tube current across the entire scan. For the abdomen/pelvis scans, liver, spleen, and kidneys were manually segmented from the patient datasets; for the chest scans, lungs and for female models only, glandular breast tissue were segmented. For each patient organ doses were estimated using Monte Carlo Methods. To investigate the utility of regional measures of scanner output, regional and organ anatomic boundaries were identified from image data and used to calculate regional and organ-specific average tube current values. From these regional and organ-specific averages, CTDI vol values, referred to as regional and organ-specific CTDI vol , were calculated for each patient. Using an approach similar to TG 204, all CTDI vol values were used to normalize simulated organ doses; and the ability of each normalized dose to correlate with patient size was investigated. Results: For all five organs, the correlations with patient size increased when organ doses were normalized by regional and organ-specific CTDI vol values. For example, when estimating dose to the liver, CTDI vol,global yielded a R 2 value of 0.26, which improved to 0.77 and 0.86, when using the regional and organ-specific CTDI vol for abdomen and liver, respectively. For breast dose, the global CTDI vol yielded a R 2 value of 0.08, which improved to 0.58 and 0.83, when using the regional and organ-specific CTDI vol for chest and breasts, respectively. The R 2 values also increased once the thoracic models were separated for the analysis into females and males, indicating differences between genders in this region not explained by a simple measure of effective diameter. Conclusions: This work demonstrated the utility of regional and organ-specific CTDI vol as normalization factors when using TCM. It was demonstrated that CTDI vol,global is not an effective normalization factor in TCM exams where attenuation (and therefore tu...
Purpose The 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. Methods Methods 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 was: (1) supplied by the manufacturer in the CT localizer radiograph and (2) 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: (1) projection data containing actual tube current values at each projection view, (2) CT localizer radiograph (topogram) and (3) 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). Results When 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%....
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