Patient Shielding in Diagnostic Imaging: Discontinuing a Legacy Practice.

OBJECTIVE: Patient shielding is standard practice in diagnostic imaging, despite growing evidence that it provides negligible or no benefit and carries a substantial risk of increasing patient dose and compromising the diagnostic efficacy of an image. The historical rationale for patient shielding is described, and the folly of its continued use is discussed. CONCLUSION: Although change is difficult, it is incumbent on radiologic technologists, medical physicists, and radiologists to abandon the practice of patient shielding in radiology.

Is lead shielding of patients necessary during fluoroscopic procedures? A study based on kyphoplasty.

OBJECTIVE: To determine the benefits, risks, and limitations associated with wrapping a patient with lead shielding during fluoroscopy-guided kyphoplasty procedures as a way to reduce operator radiation exposure. MATERIALS AND METHODS: An anthropomorphic phantom was used to mimic a patient undergoing a kyphoplasty procedure under fluoroscopic guidance. Radiation measurements of the air kerma rate (AKR) were made at several locations and under various experimental conditions. First, AKR was measured at various angles along the horizontal plane of the phantom and at varying distances from the phantom, both with and without a lead apron wrapped around the lower portion of the phantom (referred to here as phantom shielding). Second, the effect of an operator's apron was simulated by suspending a lead apron between the phantom and the measurement device. AKR was measured for the four shielding conditions-phantom shielding only, operator apron only, both phantom shielding and operator apron, and no shielding. Third, AKR measurements were made at various heights and with varying C-arm angle. RESULTS: At all locations, the phantom shielding provided no substantial protection beyond that provided by an operator's own lead apron. Phantom shielding did not reduce AKR at a height comparable to that of an operator's head. CONCLUSIONS: Previous reports of using patient shielding to reduce operator exposure fail to consider the role of an operator's own lead apron in radiation protection. For an operator wearing appropriate personal lead apparel, patient shielding provides no substantial reduction in operator dose.

Performance characteristics and quality assurance considerations for displays used in interventional radiology and cardiac catheterization facilities.

PURPOSE: While the performance of displays used for the acquisition and primary interpretation of medical images has been well-characterized, notably absent are publications evaluating and discussing the performance of displays used in Interventional Radiology (IR) suites and Cardiac Catheterization (CC) laboratories. The purpose of this work was to evaluate the performance of these displays and to consider the challenges in implementation of display quality assurance practices in this environment. METHODS: Ten large format displays used in IR and CC suites were evaluated. A visual inspection of available test patterns was performed followed by a quantitative evaluation of several performance characteristics including luminance ratio, luminance response function, and luminance uniformity. Additionally, the local ambient lighting conditions were evaluated. RESULTS: Luminance ratios ranged from 243.0 to 1182.1 with a mean value of 500.1 ± 289.2. The maximum deviation between the luminance response function and the DICOM Grayscale Standard Display Function ranged from 11.2% to 38.3% with a mean value of 26.2% ± 10.9%. When evaluating luminance uniformity, the mean maximum luminance deviation was 13.2% ± 3.5%. The mean value of luminance deviation from the median was 7.8% ± 1.0%. Measured values of background illuminance ranged from 29.1 to 310.0 lux with a mean value of 107.6 lux ± 80.4 lux. While no mura or bad pixels were observed during visual inspection, damage including scrapes and scratches as well as smudges was common to most of the displays. CONCLUSION: This work provides much needed data for the characterization of the performance of the large format displays used in IR and CC laboratory suites. These data may be used as a point of comparison when implementing a display QA program.

Imaging acquisition display performance: an evaluation and discussion of performance metrics and procedures.

When The Joint Commission updated its Requirements for Diagnostic Imaging Services for hospitals and ambulatory care facilities on July 1, 2015, among the new requirements was an annual performance evaluation for acquisition workstation displays. The purpose of this work was to evaluate a large cohort of acquisition displays used in a clinical environment and compare the results with existing performance standards provided by the American College of Radiology (ACR) and the American Association of Physicists in Medicine (AAPM). Measurements of the minimum luminance, maximum luminance, and luminance uniformity, were performed on 42 acquisition displays across multiple imaging modalities. The mean values, standard deviations, and ranges were calculated for these metrics. Additionally, visual evaluations of contrast, spatial resolution, and distortion were performed using either the Society of Motion Pictures and Television Engineers test pattern or the TG-18-QC test pattern. Finally, an evaluation of local nonuniformities was performed using either a uniform white display or the TG-18-UN80 test pattern. Displays tested were flat panel, liquid crystal displays that ranged from less than 1 to up to 10 years of use and had been built by a wide variety of manufacturers. The mean values for Lmin and Lmax for the displays tested were 0.28 ± 0.13 cd/m2 and 135.07 ± 33.35 cd/m2, respectively. The mean maximum luminance deviation for both ultrasound and non-ultrasound displays was 12.61% ± 4.85% and 14.47% ± 5.36%, respectively. Visual evaluation of display performance varied depending on several factors including brightness and contrast settings and the test pattern used for image quality assessment. This work provides a snapshot of the performance of 42 acquisition displays across several imaging modalities in clinical use at a large medical center. Comparison with existing performance standards reveals that changes in display technology and the move from cathode ray tube displays to flat panel displays may have rendered some of the tests inappropriate for modern use.

Characterization of Luminance and Color Properties of 6-MP Wide-Screen Displays.

Luminance and color performance are routinely evaluated as part of acceptance testing of displays used in diagnostic radiology. Previous work has indicated that as some diagnostic liquid crystal displays (LCDs) increase in backlight hours (BLH), the luminance measured with an external luminance meter exceeds the luminance reported by the manufacturer's built-in meter. The purposes of this work were as follows: first, to characterize several luminance and color performance characteristics for 23 Barco Coronis Fusion 6-MP MDCC 6230 color displays and, second, to provide initial data for a longitudinal study evaluating changes in luminance and color performance as BLH increase. Grayscale display conformance and maximum luminance were evaluated using a calibrated luminance meter and AAPM Task Group 18 test patterns, and agreement between target and measured luminance was calculated. Luminance uniformity was evaluated by calculating maximum luminance deviation. Color point and color uniformity were evaluated using a spectrophotometer, and the radial color distances between the corners and center of the display were calculated. Above 3 cd/m(2), there was good agreement between the target and measured luminance. At the maximum luminance, the mean difference was less than 1 %. The mean maximum luminance deviation for these displays was 10.40 ± 2.38 %. Color point was observed to be very consistent between displays with mean values of u' and v' of 0.187 ± 0.002 and 0.474 ± 0.004, respectively. Among all displays, maximum radial color distance had a mean value of 0.003 ± 0.001. These data provide a baseline for the acceptance of future displays as well as for longitudinal studies of luminance and color performance.

Location-Aware Encoding for Lesion Detection in 68Ga-DOTATATE Positron Emission Tomography Images.

OBJECTIVE: Lesion detection with positron emission tomography (PET) imaging is critical for tumor staging, treatment planning, and advancing novel therapies to improve patient outcomes, especially for neuroendocrine tumors (NETs). Current lesion detection methods often require manual cropping of regions/volumes of interest (ROIs/VOIs) a priori, or rely on multi-stage, cascaded models, or use multi-modality imaging to detect lesions in PET images. This leads to significant inefficiency, high variability and/or potential accumulative errors in lesion quantification. To tackle this issue, we propose a novel single-stage lesion detection method using only PET images. METHODS: We design and incorporate a new, plug-and-play codebook learning module into a U-Net-like neural network and promote lesion location-specific feature learning at multiple scales. We explicitly regularize the codebook learning with direct supervision at the network's multi-level hidden layers and enforce the network to learn multi-scale discriminative features with respect to predicting lesion positions. The network automatically combines the predictions from the codebook learning module and other layers via a learnable fusion layer. RESULTS: We evaluate the proposed method on a real-world clinical 68Ga-DOTATATE PET image dataset, and our method produces significantly better lesion detection performance than recent state-of-the-art approaches. CONCLUSION: We present a novel deep learning method for single-stage lesion detection in PET imaging data, with no ROI/VOI cropping in advance, no multi-stage modeling and no multi-modality data. SIGNIFICANCE: This study provides a new perspective for effective and efficient lesion identification in PET, potentially accelerating novel therapeutic regimen development for NETs and ultimately improving patient outcomes including survival.

Learning Without Real Data Annotations to Detect Hepatic Lesions in PET Images.

OBJECTIVE: Deep neural networks have been recently applied to lesion identification in fluorodeoxyglucose (FDG) positron emission tomography (PET) images, but they typically rely on a large amount of well-annotated data for model training. This is extremely difficult to achieve for neuroendocrine tumors (NETs), because of low incidence of NETs and expensive lesion annotation in PET images. The objective of this study is to design a novel, adaptable deep learning method, which uses no real lesion annotations but instead low-cost, list mode-simulated data, for hepatic lesion detection in real-world clinical NET PET images. METHODS: We first propose a region-guided generative adversarial network (RG-GAN) for lesion-preserved image-to-image translation. Then, we design a specific data augmentation module for our list-mode simulated data and incorporate this module into the RG-GAN to improve model training. Finally, we combine the RG-GAN, the data augmentation module and a lesion detection neural network into a unified framework for joint-task learning to adaptatively identify lesions in real-world PET data. RESULTS: The proposed method outperforms recent state-of-the-art lesion detection methods in real clinical 68Ga-DOTATATE PET images, and produces very competitive performance with the target model that is trained with real lesion annotations. CONCLUSION: With RG-GAN modeling and specific data augmentation, we can obtain good lesion detection performance without using any real data annotations. SIGNIFICANCE: This study introduces an adaptable deep learning method for hepatic lesion identification in NETs, which can significantly reduce human effort for data annotation and improve model generalizability for lesion detection with PET imaging.

Improving Generalizability of PET DL Algorithms: List-Mode Reconstructions Improve DOTATATE PET Hepatic Lesion Detection Performance.

Deep learning (DL) algorithms used for DOTATATE PET lesion detection typically require large, well-annotated training datasets. These are difficult to obtain due to low incidence of gastroenteropancreatic neuroendocrine tumors (GEP-NETs) and the high cost of manual annotation. Furthermore, networks trained and tested with data acquired from site specific PET/CT instrumentation, acquisition and processing protocols have reduced performance when tested with offsite data. This lack of generalizability requires even larger, more diverse training datasets. The objective of this study is to investigate the feasibility of improving DL algorithm performance by better matching the background noise in training datasets to higher noise, out-of-domain testing datasets. 68Ga-DOTATATE PET/CT datasets were obtained from two scanners: Scanner1, a state-of-the-art digital PET/CT (GE DMI PET/CT; n = 83 subjects), and Scanner2, an older-generation analog PET/CT (GE STE; n = 123 subjects). Set1, the data set from Scanner1, was reconstructed with standard clinical parameters (5 min; Q.Clear) and list-mode reconstructions (VPFXS 2, 3, 4, and 5-min). Set2, data from Scanner2 representing out-of-domain clinical scans, used standard iterative reconstruction (5 min; OSEM). A deep neural network was trained with each dataset: Network1 for Scanner1 and Network2 for Scanner2. DL performance (Network1) was tested with out-of-domain test data (Set2). To evaluate the effect of training sample size, we tested DL model performance using a fraction (25%, 50% and 75%) of Set1 for training. Scanner1, list-mode 2-min reconstructed data demonstrated the most similar noise level compared that of Set2, resulting in the best performance (F1 = 0.713). This was not significantly different compared to the highest performance, upper-bound limit using in-domain training for Network2 (F1 = 0.755; p-value = 0.103). Regarding sample size, the F1 score significantly increased from 25% training data (F1 = 0.478) to 100% training data (F1 = 0.713; p < 0.001). List-mode data from modern PET scanners can be reconstructed to better match the noise properties of older scanners. Using existing data and their associated annotations dramatically reduces the cost and effort in generating these datasets and significantly improves the performance of existing DL algorithms. List-mode reconstructions can provide an efficient, low-cost method to improve DL algorithm generalizability.

Modeling contrast-to-noise ratio from list mode reconstructions of 68Ga DOTATATE PET/CT: predicting detectability of hepatic metastases in shorter acquisition PET reconstructions.

BACKGROUND: Deep learning (DL) algorithms have shown promise in identifying and quantifying lesions in PET/CT. However, the accuracy and generalizability of these algorithms relies on large, diverse datasets which are time and labor intensive to curate. Modern PET/CT scanners may acquire data in list mode, allowing for multiple reconstructions of the same datasets with different parameters and imaging times. These reconstructions may provide a wide range of image characteristics to increase the size and diversity of datasets. Training algorithms with shorter imaging times and higher noise properties requires that lesions remain detectable. The purpose of this study is to model and predict the contrast-to-noise ratio (CNR) for shorter imaging times based on CNR from longer duration, lower noise images for 68Ga DOTATATE PET hepatic lesions and identify a threshold above which lesions remain detectable. METHODS: 68Ga DOTATATE subjects (n=20) with hepatic lesions were divided into two subgroups. The "Model" group (n=4 subjects; n=9 lesions; n=36 datapoints) was used to identify the relationship between CNR and imaging time. The "Test" group (n=16 subjects; n=44 lesions; n=176 datapoints) was used to evaluate the prediction provided by the model. RESULTS: CNR plotted as a function of imaging time for a subset of identified subjects was very well fit with a quadratic model. For the remaining subjects, the measured CNR showed a very high linear correlation with the predicted CNR for these lesions (R2 > 0.97) for all imaging durations. From the model, a threshold CNR=6.9 at 5-minutes predicted CNR > 5 at 2-minutes. Visual inspection of lesions in 2-minute images with CNR above the threshold in 5-minute images were assessed and rated as a 4 or 5 (probably positive or definitely positive) confirming 100% lesion detectability on the shorter 2-minute PET images. CONCLUSIONS: CNR for shorter DOTATATE PET imaging times may be accurately predicted using list mode reconstructions of longer acquisitions. A threshold CNR may be applied to longer duration images to ensure lesion detectability of shorter duration reconstructions. This method can aid in the selection of lesions to include in novel data augmentation techniques for deep learning.

Current status and future of targeted peptide receptor radionuclide positron emission tomography imaging and therapy of gastroenteropancreatic-neuroendocrine tumors.

Theranostics is the highly targeted molecular imaging and therapy of tumors. Targeted peptide receptor radionuclide therapy has taken the lead in demonstrating the safety and effectiveness of this molecular approach to treating cancers. Metastatic, well-differentiated gastroenteropancreatic neuroendocrine tumors may be most effectively imaged and treated with DOTATATE ligands. We review the current practice, safety, advantages, and limitations of DOTATATE based theranostics. Finally, we briefly describe the exciting new areas of development and future directions of gastroenteropancreatic neuroendocrine tumor theranostics.

Small target repeatability of 68Ga and 18F: effects of target concentration and imaging time on SUV measurements in clinically relevant phantoms.

Quantification of tumor uptake using PET imaging is important for the evaluation of therapy response. For 18F FDG PET scans, a change in uptake of 25% is commonly considered significant. For scans using novel radiopharmaceuticals, the threshold of significance is unclear. Factors including imaging time, tumor size, activity concentration, and radiopharmaceutical may affect the repeatablity of uptake metrics. This work evaluates the effect of these parameters on the repeatablity of maximum SUV (SUVmax) and mean SUV (SUVmean) in phantoms using 18F and 68Ga. An Esser PET phantom (Data Spectrum, Durham NC) was scanned on a Biograph Horizon PET/CT scanner (Siemens Medical Solutions, Malvern PA) using 18F and 68Ga. Data were acquired for 5 minutes with reconstructions between 0.5-5 minutes. The background activity mimicked clinical scans with target-to-background (T/B) ratios from 1.7-19.8. The SUVmax and SUVmean were measured for 5 slices. The mean, standard deviation, and coefficient of variation (COV) were calculated. The effects of radionuclide, imaging time, activity concentration, and target size on COV were evaluated using multivariate gamma regressions. COV for 68Ga was 40% higher and 54% higher on average than for 18F for SUVmax and SUVmean, respectively. Decreased lesion size, imaging time, and activity concentration were significantly associated with increased COV for both metrics (P < 0.001). COV was substantially reduced at high T/B for 68Ga. At the highest T/B the COV for SUVmax and SUVmean was within the typical range seen for 18F. COV is relatively high for small targets (8 mm) but is dramatically reduced with high radiotracer uptake.

Automated liver lesion detection in 68Ga DOTATATE PET/CT using a deep fully convolutional neural network.

BACKGROUND: Gastroenteropancreatic neuroendocrine tumors most commonly metastasize to the liver; however, high normal background 68Ga-DOTATATE activity and high image noise make metastatic lesions difficult to detect. The purpose of this study is to develop a rapid, automated and highly specific method to identify 68Ga-DOTATATE PET/CT hepatic lesions using a 2D U-Net convolutional neural network. METHODS: A retrospective study of 68Ga-DOTATATE PET/CT patient studies (n = 125; 57 with 68Ga-DOTATATE hepatic lesions and 68 without) was evaluated. The dataset was randomly divided into 75 studies for the training set (36 abnormal, 39 normal), 25 for the validation set (11 abnormal, 14 normal) and 25 for the testing set (11 abnormal, 14 normal). Hepatic lesions were physician annotated using a modified PERCIST threshold, and boundary definition by gradient edge detection. The 2D U-Net was trained independently five times for 100,000 iterations using a linear combination of binary cross-entropy and dice losses with a stochastic gradient descent algorithm. Performance metrics included: positive predictive value (PPV), sensitivity, F1 score and area under the precision-recall curve (PR-AUC). Five different pixel area thresholds were used to filter noisy predictions. RESULTS: A total of 233 lesions were annotated with each abnormal study containing a mean of 4 ± 2.75 lesions. A pixel filter of 20 produced the highest mean PPV 0.94 ± 0.01. A pixel filter of 5 produced the highest mean sensitivity 0.74 ± 0.02. The highest mean F1 score 0.79 ± 0.01 was produced with a 20 pixel filter. The highest mean PR-AUC 0.73 ± 0.03 was produced with a 15 pixel filter. CONCLUSION: Deep neural networks can automatically detect hepatic lesions in 68Ga-DOTATATE PET. Ongoing improvements in data annotation methods, increasing sample sizes and training methods are anticipated to further improve detection performance.

Physical characteristics of 68Ga DOTATATE PET/CT affecting small lesion detectability.

BACKGROUND AND PURPOSE: 68Ga DOTATATE PET/CT protocols are similar to 18F FDG protocols despite differences in physical properties, biodistribution, and tumor uptake. The purpose of this study is to evaluate the impact of scan time (counts), and target activity on signal-to-noise ratio (SNR) in various sized targets, or lesions. To evaluate this, phantom experiments and analysis of clinical 68Ga DOTATATE PET/CT studies were performed. MATERIALS AND METHODS: 68Ga was first compared to 18F in phantom studies to evaluate recovery coefficients and SNR. 68Ga phantom studies were also acquired in list mode, and at varying target activities to evaluate the effects of acquisition time and high target concentrations on SNR in clinically relevant small (8 mm) and larger targets (≥ 12 mm). Clinical studies (n = 50) were analyzed to determine if phantom target concentrations and SNR are present in clinical 68Ga DOTATATE studies at similarly very high tumor activity concentrations (n = 159). RESULTS: In phantoms, recovery coefficient and SUVmax for 68Ga were ~87% of 18F. SNR for 68Ga was ~65% of 18F. For the 68Ga small target (8 mm) at standard T/B = 2.4, increasing scan time from 5 to 15 minutes increased SNR from < 1 to 1.6, and did not result in target identification. Increasing T/B from 2.4 to 10.9, however, dramatically increased SNR from < 1 to 22.3. Increased T/B resulted in clear visibility of the 8 mm target, even for 1-minute scans. In patients, high hepatic tumor SUVmax (27.3±29.6), resulted in high SNR (12.5±9.8). For extrahepatic tumors, high SUVmax (41.6±42.8), resulted in high SNR (43.8±49.9). CONCLUSION: Very high target or T/B, even in small targets, can offset the physical limitations of 68Ga. High target uptake and high T/B are primary factors influencing small lesion detectability.

Practical application of AAPM Report 270 in display quality assurance: A report of Task Group 270.

Published in January 2019, AAPM Report 270 provides an update to the recommendations of the AAPM's "TG18" report. Report 270 provides new definitions of display types, updated testing patterns, and revised performance standards for the modern, flat-panel displays used as part of medical image acquisition and review. The focus of the AAPM report is on consistent image quality and appearance, and how to establish a quality assurance program to achieve those two goals. This work highlights some of the key takeaways of AAPM Report 270 and makes comparisons with existing recommendations from other references. It also provides guidance for establishing a display quality assurance program for different-sized institutions. Finally, it describes future challenges for display quality assurance and what work remains.

The effects of patient positioning when interpreting CT dose metrics: A phantom study.

PURPOSE: Review of dose metrics as part of the routine evaluation of CT protocols has become commonplace and is required by the Joint Commission and the American College of Radiology for accreditation. Most CT quality assurance programs include a review of CTDIvol and/or SSDE, both of which are affected by changes in mAs and kV. mAs, and sometimes kV, are largely determined by the Tube Current Modulation (TCM) functions of the scanner. TCM, in turn, relies on localizer scans to provide an accurate estimate of patient size. When patient size estimates are inaccurate, TCM and SSDE calculations are affected, leading to errors in both. It is important that those who are involved in reviewing CT dose indices recognize these effects to properly direct quality improvement initiatives. METHODS: An anthropomophic phantom was scanned on four clinical CT scanners using AP and PA localizers and the institution's routine abdomen protocol. Scans were repeated with the phantom at various heights relative to scanner isocenter. For each height, the projected phantom width, as shown by the localizer scans, was measured and normalized by the width of the helical scan. After each localizer scan, the TCM algorithm determined the mAs to be used for the helical scan. The scanner-reported average CTDIvol was recorded for each helical scan, and the SSDE was calculated from the projected phantom size and the scanner-reported CTDIvol at each phantom height. Last, the phantom was augmented with a lipid-gel bolus material to simulate different body mass distributions and investigate the effect of differing body habitus on projected phantom size. The results were considered in the context of optimizing dose in CT imaging, with particular attention paid to the effect on dose to breast tissue. RESULTS: Vertical mis-positioning of the phantom within the scanner led to errors in estimated phantom size of up to a factor of 1.5. These effects were more severe when localizers were acquired in the PA orientation compared with the AP orientation. Minification effects were more pronounced for AP localizers. As a consequence of inaccuracies in estimated phantom size, TCM resulted in changes in CTDIvol and SSDE of as much as a factor of 4.4 and 2.7, respectively. The effect was more pronounced when the TCM function used data from the PA, rather than the AP, localizer. CONCLUSIONS: Proper patient positioning plays a large role in the function of TCM, and hence CTDIvol and SSDE. In addition, body mass distribution may affect how patients ought to be positioned within the scanner. Understanding these effects is critical in optimizing CT scanning practices.