Schueler, Beth A.

This slide presentation uses real-world examples from diverse modern systems to categorize digital radiographic artifacts by likely cause and outlines investigation and mitigation strategies to improve troubleshooting, artifact prevention, and patient care.

Abstract The establishment of guidelines and curriculum standards for medical physics residency training is a critical component of setting expectations and competencies for the profession. Since the last publication of these standards, residency training has become integrated into the eligibility criteria for most medical physics certification bodies. The rapid growth of medical physics requires periodic review of the curriculum to remove outdated approaches, update established knowledge, and add emerging technologies. The goal of this document is to provide a reference standard for residency training program directors, training mentors, and residents to guide creation and/or optimization of their program's curriculum. The document is intentionally forward‐looking to capture emerging technological advances including hybrid diagnostic and therapeutic delivery systems and applications of image processing. This document is an update of the current residency training curriculum. Version history of residency training guidelines Publication Title Date 2013 Essentials and Guidelines for Clinical Medical Physics Residency Training Programs—AAPM Report No. 249 2006 Essentials and Guidelines for Hospital‐Based Medical Physics Residency Training Programs—Report No. 90 1992 Essentials and Guidelines for Hospital‐Based Medical Physics Residency Training Programs—Report No. 36 The Work Group on Periodic Review of Medical Physics Residency Training (WGMPRT) would like to acknowledge the authors of past versions of this document as they form the basis for this update. It is the sincere hope of the WGMPRT that this report will continue to be used by the medical physics community to guide training in the profession.

AbstractBackgroundPrior implementations of the channelized Hotelling observer (CHO) model have succeeded in assessing the performance of X‐ray angiography systems under a variety of imaging conditions. However, often times these conditions do not resemble those present in routine clinical imaging scenarios, such as having complex anthropomorphic backgrounds in conjunction with moving test objects.PurposeThis work builds up on prior established CHO methods and introduces a new approach to switch from the already established “multiple‐sample” CHO implementation to a “single‐sample” technique. The proposed implementation enables the inclusion of moving test objects upon nonuniform backgrounds by allowing only a single sample to represent the test object present condition that is to be used within the statistical test to estimate the detectability index.MethodsTo assess the proposed method, two image data sets were acquired with a clinical X‐ray angiography system. The first set consisted of a uniform background in combination with static test objects while the second consisted of an anthropomorphic chest phantom in conjunction with moving test objects. The first set was used to validate the proposed approach against the multiple‐sample method while the second was used to assess the feasibility of the proposed method under a variety of imaging conditions, including seven object sizes and seven detector target dose (DTD) levels.ResultsFor the uniform background data set, considering all detectability indices greater or equal than 1, the ratio between the detectability indices of the proposed single‐sample approach versus the multiple‐sample method was 0.997 ± 0.056 (range 0.884–1.159). The average single‐direction width of the 95% confidence intervals (CIs) of the detectability index estimates for the multiple‐sample method was 0.38 ± 0.43 (range 0.03–2.20). For the single‐sample approach, the average width was 2.52 ± 0.63 (range 1.11–5.44). For the anthropomorphic background image set, the results were consistent with classical quantum‐limited signal‐to‐noise ratio (SNR) theory. The magnitude of the detectability indices varied predictably with changes in both object size and DTD, with the highest value associated with the highest dose and the largest object size. Additionally, the proposed method was able to capture differences in the imaging performance for a given test object across the field of view, which was associated with the attenuation levels provided by different features of the anthropomorphic background.ConclusionsA new single‐sample variant of the CHO model to assess the performance of X‐ray angiography imaging systems is proposed. The new approach is consistent with quantum‐limited image quality theory and with a standard implementation of the CHO model. The proposed method enables the assessment of moving test objects in combination with complex, nonuniform image backgrounds, thereby opening the possibility to assess imaging conditions which more closely resemble those used in clinical care.

AbstractThe well‐referenced structural shielding design NCRP Report No. 147 uses workload information based on self‐reported film‐screen data from the AAPM Task Group 9 survey. The aim of this study was to assess the clinical workload distributions of modern digital radiography (DR) systems in general hospital and pediatric‐only practices. A retrospective analysis of DR imaging data on four radiographic systems in a hospital practice and two radiographic systems in a pediatric practice, through a custom clinical DICOM header analytics program. A total of 203, 294 exposures from the general hospital practice and 25,415 from the pediatric practice from 2019 and 2021 were included. Values for kVp, mAs, and detector type (wall bucky, table bucky, or free detector) were extracted. For each exam, mAs was accumulated in a kVp histogram with bins 5 kVp wide and further parsed by detector type. Total workload was calculated by summing all exposures, then normalized by the number of patients. The median (25th and 75th percentile) workload in the hospital practice was 0.43 (0.22, 1.13) mA‐min per patient, while the average was 1.36 ± 3.08. Pediatric data yielded a median (25th and 75th percentile) of 0.10 (0.05, 0.23) and an average of 0.29 ± 0.69 mA‐min per patient. Mean number of patients per week was 230 adult and 57 pediatric. Hospital workload data is approximately 44% less than the NCRP Report No. 147 value.

AbstractThis report reviews the image acquisition and reconstruction characteristics of C‐arm Cone Beam Computed Tomography (C‐arm CBCT) systems and provides guidance on quality control of C‐arm systems with this volumetric imaging capability. The concepts of 3D image reconstruction, geometric calibration, image quality, and dosimetry covered in this report are also pertinent to CBCT for Image‐Guided Radiation Therapy (IGRT). However, IGRT systems introduce a number of additional considerations, such as geometric alignment of the imaging at treatment isocenter, which are beyond the scope of the charge to the task group and the report.Section 1 provides an introduction to C‐arm CBCT systems and reviews a variety of clinical applications. Section 2 briefly presents nomenclature specific or unique to these systems. A short review of C‐arm fluoroscopy quality control (QC) in relation to 3D C‐arm imaging is given in Section 3. Section 4 discusses system calibration, including geometric calibration and uniformity calibration. A review of the unique approaches and challenges to 3D reconstruction of data sets acquired by C‐arm CBCT systems is give in Section 5. Sections 6 and 7 go in greater depth to address the performance assessment of C‐arm CBCT units. First, Section 6 describes testing approaches and phantoms that may be used to evaluate image quality (spatial resolution and image noise and artifacts) and identifies several factors that affect image quality. Section 7 describes both free‐in‐air and in‐phantom approaches to evaluating radiation dose indices.The methodologies described for assessing image quality and radiation dose may be used for annual constancy assessment and comparisons among different systems to help medical physicists determine when a system is not operating as expected. Baseline measurements taken either at installation or after a full preventative maintenance service call can also provide valuable data to help determine whether the performance of the system is acceptable. Collecting image quality and radiation dose data on existing phantoms used for CT image quality and radiation dose assessment, or on newly developed phantoms, will inform the development of performance criteria and standards. Phantom images are also useful for identifying and evaluating artifacts. In particular, comparing baseline data with those from current phantom images can reveal the need for system calibration before image artifacts are detected in clinical practice. Examples of artifacts are provided in Sections 4, 5, and 6.

AbstractBackgroundDigital radiography (DR) still presents many challenges and could have complex imaging acquisition and processing patterns in a clinical practice hindering quality standardization.PurposeThis technical note aims to report the 4‐year experience with utilizing a custom DICOM metadata analytics program in clinical DR at a large institution.MethodsThirty‐eight DR systems of three vendors at multiple locations were configured to automatically send clinical DICOM images to a DICOM receiver. A suite of custom MATLAB programs was established to extract and store public and private header data weekly. Specific use cases are provided for systematic image acquisition investigation, image processing harmonization, exposure index (EI) longitudinal monitoring and EI target optimization.ResultsFor systematic acquisition investigation, an example of adult lumbar spine exam analysis was provided with statistics on manual acquisition versus the use of automatic exposure control (AEC, including AEC dose level, active cell, and backup timer), grid usage, and collimation for various projections. For processing harmonization, up to 12.6% of protocols were revealed to have processing parameter differences in an example of a mobile radiography fleet. In addition, inconsistent use of a post‐acquisition image size function was also demonstrated, which resulted in anatomy size display variations. Bimonthly monitoring of median EI values showed expected trends, including changes after an AEC dose level adjustment for adult posterior‐anterior chest imaging on a scanner system. An example of adult axillary shoulder EI target refinement was shared using the median value, eμ, based on the lognormal EI data distribution after parsing down to acquisitions with appropriate techniques.ConclusionsThis analytics program enables systematic analysis of image acquisition and processing details. The information provides invaluable insights into real practice patterns, which can support data‐driven quality standardization and optimization.

AbstractThere is a lack of understanding in the performance of flexible noise control (FNC) processing, which is used in digital radiography on a scanner vendor and has four parameters each involving multiple options. The aim of this study was to investigate the impact of FNC on portable chest imaging. An anthropomorphic chest phantom was imaged using a clinical chest program with 85 kV and five radiation dose levels at 40″ source‐to‐image distance with software‐based scatter reduction method. All images were processed without and with FNC. Noise analysis was performed in two regions of interest (ROI) on subtracted noise‐only images, and line profiles were generated through a lung‐rib interface. In addition, noise power spectra (NPS) analysis was performed in solid water phantoms of 10 and 20 cm thicknesses, using the same acquisition program and a range of dose levels. Last, feedback on retrospectively deidentified, reprocessed, and randomized clinical images from 20 portable chest exams was gathered from two thoracic radiologists. Noise reduction performances of FNC were demonstrated, with the level depending on specific FNC parameters, dose levels, ROI placement, and phantom sizes. Higher frequency textural patterns were revealed through the NPS analysis, which varied based on FNC parameters, dose levels, and phantom sizes. Overall, the vendor default parameter FGA0.5 yielded the highest noise reduction and textural artifacts. Radiologist feedback showed consistent preference of no FNC due to the presence of textural artifacts in the FNC‐processed images. An algorithm improvement to avoid introducing artifacts would be desired.

AbstractModern fluoroscopes used for image guidance have become quite complex. Adding to this complexity are the many regulatory and accreditation requirements that must be fulfilled during acceptance testing of a new unit. Further, some of these acceptance tests have pass/fail criteria, whereas others do not, making acceptance testing a subjective and time‐consuming task. The AAPM Task Group 272 Report spells out the details of tests that are required and gives visibility to some of the tests that while not yet required are recommended as good practice. The organization of the report begins with the most complicated fluoroscopes used in interventional radiology or cardiology and continues with general fluoroscopy and mobile C‐arms. Finally, the appendices of the report provide useful information, an example report form and topics that needed their own section due to the level of detail.

Data suggest that radiation-induced cataracts may form without a threshold and at low-radiation doses. Staff involved in interventional radiology and cardiology fluoroscopy-guided procedures have the potential to be exposed to radiation levels that may lead to eye lens injury and the occurrence of opacifications have been reported. Estimates of lens dose for various fluoroscopy procedures and predicted annual dosages have been provided in numerous publications. Available tools for eye lens radiation protection include accessory shields, drapes and glasses. While some tools are valuable, others provide limited protection to the eye. Reducing patient radiation dose will also reduce occupational exposure. Significant variability in reported dose measurements indicate dose levels are highly dependent on individual actions and exposure reduction is possible. Further follow-up studies of staff lens opacification are recommended along with eye lens dose measurements under current clinical practice conditions.