A. Kyle Jones

Calculating the peak skin dose resulting from fluoroscopically guided interventions. Part I: Methods

While direct measurement of the peak skin dose resulting from a fluoroscopically‐guided procedure is possible, the decision must be made a priori at additional cost and time. It is most often the case that the need for accurate knowledge of the peak skin dose is realized only after a procedure has been completed, or after a suspected reaction has been discovered. Part I of this review article discusses methods for calculating the peak skin dose across a range of clinical scenarios. In some cases, a wealth of data are available, while in other cases few data are available and additional data must be measured in order to estimate the peak skin dose. Data may be gathered from a dose report, the DICOM headers of images, or from staff and physician interviews. After data are gathered, specific steps must be followed to convert dose metrics, such as the reference point air kerma (Ka,r) or the kerma area product (KAP), into peak skin dose. These steps require knowledge of other related factors, such as the f‐factor and the backscatter factor, tables of which are provided in this manuscript. Sources of error and the impact of these errors on the accuracy of the final estimate of the peak skin dose are discussed. PACS numbers: 87.59.Dj, 87.53.Bn

One Year’s Results from a Server-Based System for Performing Reject Analysis and Exposure Analysis in Computed Radiography

Rejected images represent both unnecessary radiation exposure to patients and inefficiency in the imaging operation. Rejected images are inherent to projection radiography, where patient positioning and alignment are integral components of image quality. Patient motion and artifacts unique to digital image receptor technology can result in rejected images also. We present a centralized, server-based solution for the collection, archival, and distribution of rejected image and exposure indicator data that automates the data collection process. Reject analysis program (RAP) and exposure indicator data were collected and analyzed during a 1-year period. RAP data were sorted both by reason for repetition and body part examined. Data were also stratified by clinical area for further investigation. The monthly composite reject rate for our institution fluctuated between 8% and 10%. Positioning errors were the main cause of repeated images (77.3%). Stratification of data by clinical area revealed that areas where computed radiography (CR) is seldom used suffer from higher reject rates than areas where it is used frequently. S values were log-normally distributed for examinations performed under either manual or automatic exposure control. The distributions were positively skewed and leptokurtic. S value decreases due to radiologic technology student rotations, and CR plate reader calibrations were observed. Our data demonstrate that reject analysis is still necessary and useful in the era of digital imaging. It is vital though that analysis be combined with exposure indicator analysis, as digital radiography is not self-policing in terms of exposure. When combined, the two programs are a powerful tool for quality assurance.

ACR–AAPM–SIIM Practice Guideline for Digital Radiography

This guideline was developed collaboratively by the American College of Radiology (ACR), the American Association of Physicists in Medicine (AAPM), and the Society for Imaging Informatics in Medicine (SIIM). Increasingly, medical imaging and patient information are being managed using digital data during acquisition, transmission, storage, display, interpretation, and consultation. The management of these data during each of these operations may have an impact on the quality of patient care. “CR” and “DR” are the commonly used terms for digital radiography detectors. CR is the acronym for computed radiography, and DR is an acronym for digital radiography. CR uses a photostimulable storage phosphor that stores the latent image, which is subsequently read out using a stimulating laser beam. It can be easily adapted to a cassette-based system analogous to that used in screen-film (SF) radiography. Historically, the acronym DR has been used to describe a flat-panel digital X-ray imaging system that reads the transmitted X-ray signal immediately after exposure with the detector in place. Generically, the term CR is applied to passive detector systems, while the term DR is applied to active detectors. This guideline is applicable to the practice of digital radiography. It defines motivations, qualifications of personnel, equipment guidelines, data manipulation and management, and quality control (QC) and quality improvement procedures for the use of digital radiography that should result in high-quality radiological patient care. In all cases for which an ACR practice guideline or technical standard exists for the modality being used or the specific examination being performed, that guideline or standard will continue to apply when digital image data management systems are used.

Quality Initiatives: Establishing an Interventional Radiology Patient Radiation Safety Program

The Interventional Radiology Patient Radiation Safety Program was created to better educate patients who are scheduled to undergo high-dose interventional radiologic procedures about the risks of radiation, better monitor the delivered doses, and reduce the risk for deterministic effects. The program combines preprocedure evaluation and counseling, intraprocedure monitoring, and postprocedure documentation and counseling with the guidelines of the National Cancer Institute and the Society of Interventional Radiology. Between July 2009, when the program was implemented, and September 2010, over 3500 interventional radiologic procedures were monitored and documented, and 63 procedures with an adjusted cumulative dose of more than 3 Gy were identified and further analyzed; four procedures were found to be outside the control limits. Additional review of these four procedures resulted in practice modifications. Anecdotal feedback from physician assistants and attending physicians indicated that the program had another positive effect: Patients who required postprocedure counseling about the potential for radiation-induced skin injuries were no longer surprised by this information. Implementation of this program is straightforward, requires little infrastructure and few resources, and may be applied in most interventional radiology practices. Supplemental material available at http://radiographics.rsna.org/lookup/suppl/doi:10.1148/rg.321115002/-/DC1.

Ongoing quality control in digital radiography: Report of AAPM Imaging Physics Committee Task Group 151.

Quality control (QC) in medical imaging is an ongoing process and not just a series of infrequent evaluations of medical imaging equipment. The QC process involves designing and implementing a QC program, collecting and analyzing data, investigating results that are outside the acceptance levels for the QC program, and taking corrective action to bring these results back to an acceptable level. The QC process involves key personnel in the imaging department, including the radiologist, radiologic technologist, and the qualified medical physicist (QMP). The QMP performs detailed equipment evaluations and helps with oversight of the QC program, the radiologic technologist is responsible for the day-to-day operation of the QC program. The continued need for ongoing QC in digital radiography has been highlighted in the scientific literature. The charge of this task group was to recommend consistency tests designed to be performed by a medical physicist or a radiologic technologist under the direction of a medical physicist to identify problems with an imaging system that need further evaluation by a medical physicist, including a fault tree to define actions that need to be taken when certain fault conditions are identified. The focus of this final report is the ongoing QC process, including rejected image analysis, exposure analysis, and artifact identification. These QC tasks are vital for the optimal operation of a department performing digital radiography.

How accurately can the peak skin dose in fluoroscopy be determined using indirect dose metrics

PURPOSE Skin dosimetry is important for fluoroscopically-guided interventions, as peak skin doses (PSD) that result in skin reactions can be reached during these procedures. There is no consensus as to whether or not indirect skin dosimetry is sufficiently accurate for fluoroscopically-guided interventions. However, measuring PSD with film is difficult and the decision to do so must be madea priori. The purpose of this study was to assess the accuracy of different types of indirect dose estimates and to determine if PSD can be calculated within ± 50% using indirect dose metrics for embolization procedures. METHODS PSD were measured directly using radiochromic film for 41 consecutive embolization procedures at two sites. Indirect dose metrics from the procedures were collected, including reference air kerma. Four different estimates of PSD were calculated from the indirect dose metrics and compared along with reference air kerma to the measured PSD for each case. The four indirect estimates included a standard calculation method, the use of detailed information from the radiation dose structured report, and two simplified calculation methods based on the standard method. Indirect dosimetry results were compared with direct measurements, including an analysis of uncertainty associated with film dosimetry. Factors affecting the accuracy of the different indirect estimates were examined. RESULTS When using the standard calculation method, calculated PSD were within ± 35% for all 41 procedures studied. Calculated PSD were within ± 50% for a simplified method using a single source-to-patient distance for all calculations. Reference air kerma was within ± 50% for all but one procedure. Cases for which reference air kerma or calculated PSD exhibited large (± 35%) differences from the measured PSD were analyzed, and two main causative factors were identified: unusually small or large source-to-patient distances and large contributions to reference air kerma from cone beam computed tomography or acquisition runs acquired at large primary gantry angles. When calculated uncertainty limits [-12.8%, 10%] were applied to directly measured PSD, most indirect PSD estimates remained within ± 50% of the measured PSD. CONCLUSIONS Using indirect dose metrics, PSD can be determined within ± 35% for embolization procedures. Reference air kerma can be used without modification to set notification limits and substantial radiation dose levels, provided the displayed reference air kerma is accurate. These results can reasonably be extended to similar procedures, including vascular and interventional oncology. Considering these results, film dosimetry is likely an unnecessary effort for these types of procedures when indirect dose metrics are available.

Harmonizing the pixel size in retrospective computed tomography radiomics studies

Consistent pixel sizes are of fundamental importance for assessing texture features that relate intensity and spatial information in radiomics studies. To correct for the effects of variable pixel sizes, we combined image resampling with Butterworth filtering in the frequency domain and tested the correction on computed tomography (CT) scans of lung cancer patients reconstructed 5 times with pixel sizes varying from 0.59 to 0.98 mm. One hundred fifty radiomics features were calculated for each preprocessing and field-of-view combination. Intra-patient agreement and inter-patient agreement were compared using the overall concordance correlation coefficient (OCCC). To further evaluate the corrections, hierarchical clustering was used to identify patient scans before and after correction. To assess the general applicability of the corrections, they were applied to 17 CT scans of a radiomics phantom. The reduction in the inter-scanner variability relative to non–small cell lung cancer patient scans was quantified. The variation in pixel sizes caused the intra-patient variability to be large (OCCC <95%) relative to the inter-patient variability in 79% of the features. However, with the resampling and filtering corrections, the intra-patient variability was relatively large in only 10% of the features. With the filtering correction, 8 of 8 patients were correctly clustered, in contrast to only 2 of 8 without the correction. In the phantom study, resampling and filtering the images of a rubber particle cartridge substantially reduced variability in 61% of the radiomics features and substantially increased variability in only 6% of the features. Surprisingly, resampling without filtering tended to increase the variability. In conclusion, applying a correction based on resampling and Butterworth low-pass filtering in the frequency domain effectively reduced variability in CT radiomics features caused by variations in pixel size. This correction may also reduce the variability introduced by other CT scan acquisition parameters.

Medical imaging using ionizing radiation: Optimization of dose and image quality in fluoroscopy

The 2012 Summer School of the American Association of Physicists in Medicine (AAPM) focused on optimization of the use of ionizing radiation in medical imaging. Day 2 of the Summer School was devoted to fluoroscopy and interventional radiology and featured seven lectures. These lectures have been distilled into a single review paper covering equipment specification and siting, equipment acceptance testing and quality control, fluoroscope configuration, radiation effects, dose estimation and measurement, and principles of flat panel computed tomography. This review focuses on modern fluoroscopic equipment and is comprised in large part of information not found in textbooks on the subject. While this review does discuss technical aspects of modern fluoroscopic equipment, it focuses mainly on the clinical use and support of such equipment, from initial installation through estimation of patient dose and management of radiation effects. This review will be of interest to those learning about fluoroscopy, to those wishing to update their knowledge of modern fluoroscopic equipment, to those wishing to deepen their knowledge of particular topics, such as flat panel computed tomography, and to those who support fluoroscopic equipment in the clinic.

In vivo CT dosimetry during CT colonography.

OBJECTIVE The purpose of this study was to develop a method of measuring rectal radiation dose in vivo during CT colonography (CTC) and assess the accuracy of size-specific dose estimates (SSDEs) relative to that of in vivo dose measurements. MATERIALS AND METHODS Thermoluminescent dosimeter capsules were attached to a CTC rectal catheter to obtain four measurements of the CT radiation dose in 10 volunteers (five men and five women; age range, 23-87 years; mean age, 70.4 years). A fixed CT technique (supine and prone, 50 mAs and 120 kVp each) was used for CTC. SSDEs and percentile body habitus measurements were based on CT images and directly compared with in vivo dose measurements. RESULTS The mean absorbed doses delivered to the rectum ranged from 8.8 to 23.6 mGy in the 10 patients, whose mean body habitus was in the 27th percentile among American adults 18-64 years old (range, 0.5-67th percentile). The mean SSDE error was 7.2% (range, 0.6-31.4%). CONCLUSION This in vivo radiation dose measurement technique can be applied to patients undergoing CTC. Our measurements indicate that SSDEs are reasonable estimates of the rectal absorbed dose. The data obtained in this pilot study can be used as benchmarks for assessing dose estimates using other indirect methods (e.g., Monte Carlo simulations).

Evaluation of the potential utility of flat panel CT for quantifying relative contrast enhancement

PURPOSE Certain directed oncologic therapies seek to take advantage of the fact that tumors are typically more susceptible to directed therapeutic agents than normal tissue owing to their extensive networks of poorly formed, leaky vasculature. If differences between the vascularity of normal and tumor tissues could be quantified, patients could be selected for or excluded from directed treatments on the basis of this difference. However, angiographic imaging techniques such as digital subtraction angiography (DSA) yield two-dimensional data that may be inadequate for this task. As a first step, the authors evaluated the feasibility of using a commercial implementation of flat panel computed tomography (FPCT) to quantify differences in enhancement of a simulated tumor compared with normal tissue based on differences in CT number measured in precontrast and postcontrast scans. METHODS To evaluate the FPCT scanner studied, the authors scanned several phantoms containing simulated normal and tumor tissues. In the first experiment, the authors used an anthropomorphic phantom containing inclusions representing normal, tumor, and bone tissue to evaluate the constancy of CT numbers in scans repeated at clinically relevant intervals of 1 and 3 min. The authors then scanned gelatin phantoms containing dilutions of iodinated contrast to evaluate the accuracy of relative contrast enhancement measurements for a clinical FPCT system. Data were analyzed using widely available software. RESULTS CT numbers measured in identical locations were constant over both scan intervals evaluated. Measured relative contrast enhancement values were accurate compared with known relative contrast enhancement values. Care must be taken to avoid artifacts in reconstructed images when placing regions of interest. CONCLUSIONS Despite its limitations, FPCT in the interventional laboratory can be used to quantify relative contrast enhancement in phantoms. This is accomplished by measuring CT number in simulated tumor and normal tissue on precontrast and postcontrast scans. This information opens the door for refinement of technique in an effort to use such a technique to plan directed therapies.PURPOSE Certain directed oncologic therapies seek to take advantage of the fact that tumors are typically more susceptible to directed therapeutic agents than normal tissue owing to their extensive networks of poorly formed, leaky vasculature. If differences between the vascularity of normal and tumor tissues could be quantified, patients could be selected for or excluded from directed treatments on the basis of this difference. However, angiographic imaging techniques such as digital subtraction angiography (DSA) yield two-dimensional data that may be inadequate for this task. As a first step, the authors evaluated the feasibility of using a commercial implementation of flat panel computed tomography (FPCT) to quantify differences in enhancement of a simulated tumor compared with normal tissue based on differences in CT number measured in precontrast and postcontrast scans. METHODS To evaluate the FPCT scanner studied, the authors scanned several phantoms containing simulated normal and tumor tissues. In the first experiment, the authors used an anthropomorphic phantom containing inclusions representing normal, tumor, and bone tissue to evaluate the constancy of CT numbers in scans repeated at clinically relevant intervals of 1 and 3 min. The authors then scanned gelatin phantoms containing dilutions of iodinated contrast to evaluate the accuracy of relative contrast enhancement measurements for a clinical FPCT system. Data were analyzed using widely available software. RESULTS CT numbers measured in identical locations were constant over both scan intervals evaluated. Measured relative contrast enhancement values were accurate compared with known relative contrast enhancement values. Care must be taken to avoid artifacts in reconstructed images when placing regions of interest. CONCLUSIONS Despite its limitations, FPCT in the interventional laboratory can be used to quantify relative contrast enhancement in phantoms. This is accomplished by measuring CT number in simulated tumor and normal tissue on precontrast and postcontrast scans. This information opens the door for refinement of technique in an effort to use such a technique to plan directed therapies.