Long, Zaiyang

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.

AbstractPurposeAn increasing number of implantable or external devices can impact whether patients can receive radiological imaging examinations. This study examines and tests the Neulasta (pegfilgrastim) Onpro on‐body injector in multiple imaging environments.MethodsThe injector was analyzed for four imaging modalities with testing protocols and strategies developed for each modality. In x‐ray and computed tomography (CT), scans with much higher exposure than clinical protocols were performed with the device attached to an anthropomorphic phantom. The device was monitored until the completion of drug delivery. For magnetic resonance imaging (MRI), the device was assessed using a hand‐held magnet and underwent the magnetically induced displacement testing in a 1.5T clinical MRI scanner room. For ultrasound, magnetic field changes were measured around an ultrasound scanner system with three transducers.ResultsFor x‐ray and CT no sign of device error was identified during or after the high radiation exposure scans. Drug delivery was completed at expected timing with expected volume. For MRI the device showed significant attractive force towards the hand‐held magnet and a 50‐degree deflection angle at 50 cm from the opening of the scanner bore. No further assessment from the gradient or radiofrequency field was deemed necessary. For ultrasound the maximum magnetic field change from baseline was measured to be +11.7 μT in comparison to +74.2 μT at 4 inches from a working microwave.ConclusionsNo device performance issue was identified under the extreme test conditions in x‐ray or CT. The device was found to be MR Unsafe. Magnetic field changes around an ultrasound system met the limitation set by manufacture. Patient ultrasound scanning is considered safe as long as the transducers do not inadvertently loosen the device.

Objective The diagnosis of autoimmune pancreatitis (AIP) is challenging. Sonographic and cross-sectional imaging findings of AIP closely mimic pancreatic ductal adenocarcinoma (PDAC) and techniques for tissue sampling of AIP are suboptimal. These limitations often result in delayed or failed diagnosis, which negatively impact patient management and outcomes. This study aimed to create an endoscopic ultrasound (EUS)-based convolutional neural network (CNN) model trained to differentiate AIP from PDAC, chronic pancreatitis (CP) and normal pancreas (NP), with sufficient performance to analyse EUS video in real time. Design A database of still image and video data obtained from EUS examinations of cases of AIP, PDAC, CP and NP was used to develop a CNN. Occlusion heatmap analysis was used to identify sonographic features the CNN valued when differentiating AIP from PDAC. Results From 583 patients (146 AIP, 292 PDAC, 72 CP and 73 NP), a total of 1 174 461 unique EUS images were extracted. For video data, the CNN processed 955 EUS frames per second and was: 99% sensitive, 98% specific for distinguishing AIP from NP; 94% sensitive, 71% specific for distinguishing AIP from CP; 90% sensitive, 93% specific for distinguishing AIP from PDAC; and 90% sensitive, 85% specific for distinguishing AIP from all studied conditions (ie, PDAC, CP and NP). Conclusion The developed EUS-CNN model accurately differentiated AIP from PDAC and benign pancreatic conditions, thereby offering the capability of earlier and more accurate diagnosis. Use of this model offers the potential for more timely and appropriate patient care and improved outcome.

AbstractPurposeUltrasound grayscale imaging preset optimization has often been qualitative and dependent upon vendor application specialists. This study aimed to propose a systematic approach for grayscale imaging preset optimization and apply the approach in a clinical abdominal scan setting.MethodsA six‐step approach was detailed including identification of clinical task, adjustment of basic parameters, fine‐tuning of advanced parameters, image performance metrics confirmation, clinical evaluation and data analysis, and implementation of new presets and monitoring of clinical usage. Its application in an abdominal scanning task was described for each step with phantoms, volunteers, and software tools.ResultsClinical image data analytics facilitated the understanding of the imaging task, relevant transducers, and target characteristics, in addition to specific requests from radiologists. Quantitative measurements were made on global image contrast and gray map function. In addition, clinically relevant phantoms and volunteer scans without and with acoustic distortion layers were involved to determine the new presets. Furthermore, phantom signal to noise ratio study and clinical evaluation using volunteers with different body habitus were utilized to confirm the superiority of the new presets. Quantitative clinical usage monitoring demonstrated successful implementation of the new presets.ConclusionsA systematic approach for grayscale imaging preset optimization has been proposed and successfully applied for a specific clinical task. This approach was designed to be generalizable and relatively flexible, which would facilitate movement away from previous qualitative and subjective approaches.

AbstractPurposeIt is unclear if a 3D transducer with the special design of mechanical swing or 2D array could provide acceptable 2D grayscale image quality for the general diagnosis purpose. The aim of this study is to compare the 2D image quality of a 3D intracavitary transducer with a conventional 2D intracavitary transducer using clinically relevant phantom experiments.MethodsAll measurements were performed on a GE Logiq E9 scanner with both a 2D (IC5‐9‐D) and a 3D (RIC5‐9‐D) transducer used in 2D mode. Selection of phantom targets and acquisition parameters were determined from analysis of 33 clinical pelvic exams. Depth of penetration (DOP), contrast response, contrast of anechoic cylinders (diameter: 6.7 mm) at 1.5 and 4.5 cm depths in transverse planes, and in‐plane resolution represented by full‐width half‐maximum of pin targets at multiple depths were measured with transmit frequencies of 7 and 8 MHz. Spherical signal‐noise‐ratio (SNR) (diameter: 4 and 2 mm) at multiple depths were measured at 8 MHz.ResultsRIC5‐9‐D demonstrated <8% decrease in DOP for both transmit frequencies (7 MHz: 69.7 ± 8.2 mm; 8 MHz: 64.3 ± 7.8 mm) compared with those from IC5‐9‐D (7 MHz: 73.9 ± 4.4 mm; 8 MHz: 69.4 ± 7.8 mm). A decreased anechoic contrast was observed with a 4.5 cm depth for RIC5‐9‐D (7 MHz: 23.2 ± 1.8 dB, P > 0.05; 8 MHz: 17.7 ± 0.9 dB, P < 0.01) compared with IC5‐9‐D (7 MHz: 25.9 ± 1.2 dB; 8 MHz: 21.5 ± 0.8 dB). The contrast response and spatial resolution performance were comparable between the two transducers. RIC5‐9‐D showed comparable SNR of anechoic spheres compared to IC5‐9‐D.Conclusions2D images from a 3D probe exhibited comparable overall image quality for routine clinical pelvic imaging.

AbstractObjectivesBoth projection and dual‐energy (DE)‐based methods have been used for metal artifact reduction (MAR) in CT. The two methods can also be combined. The purpose of this work was to evaluate these three MAR methods using phantom experiments for five types of metal implants.Materials and MethodsFive phantoms representing spine, dental, hip, shoulder, and knee were constructed with metal implants. These phantoms were scanned using both single‐energy (SE) and DE protocols with matched radiation output. The SE data were processed using a projection‐based MAR (iMAR, Siemens) algorithm, while the DE data were processed to generate virtual monochromatic images at high keV (Mono+, Siemens). In addition, the DE images after iMAR were used to generate Mono+ images (DE iMAR Mono+). Artifacts were quantitatively evaluated using CT numbers at different regions of interest. Iodine contrast‐to‐noise ratio (CNR) was evaluated in the spine phantom. Three musculoskeletal radiologists and two neuro‐radiologists independently ranked the artifact reduction.ResultsThe DE Mono+ at high keV resulted in reduced artifacts but also lower iodine CNR. The iMAR method alone caused missing tissue artifacts in dental phantom. DE iMAR Mono+ caused wrong CT numbers in close proximity to the metal prostheses in knee and hip phantoms. All musculoskeletal radiologists ranked SE iMAR > DE iMAR Mono+ > DE Mono+ for knee and hip, while DE iMAR Mono+ > SE iMAR > DE Mono+ for shoulder. Both neuro‐radiologists ranked DE iMAR Mono+ > DE Mono+ > SE iMAR for spine and DE Mono+ > DE iMAR Mono+ > SE iMAR for dental.ConclusionsThe SE iMAR was the best choice for the hip and knee prostheses, while DE Mono+ at high keV was best for dental implants and DE iMAR Mono+ was best for spine and shoulder prostheses. Artifacts were also introduced by MAR algorithms.

AbstractBecause of the rapidly growing use of ultrasound shear wave elastography (SWE) in clinical practices, there is a significant need for development of clinical physics performance assessment methods for this technology. This study aims to report two clinical medical physicists’ tasks: (a) acceptance testing (AT) of SWE function on ten commercial ultrasound systems for clinical liver application and (b) comparison of SWE measurements of targets across vendors for clinical musculoskeletal application. For AT, ten GE LOGIQ E9 XDclear 2.0 scanners with ten C1‐6‐D and ten 9L‐D transducers were studied using two commercial homogenous phantoms. Five measurements were acquired at two depths for each scanner/transducer pair by two operators. Additional tests were performed to access effects of different coupling media, phantom locations and operators. System deviations were less than 5% of group mean or three times standard deviation; therefore, all systems passed AT. A test protocol was provided based on results that no statistically significant difference was observed between using ultrasound gel and salt water for coupling, among different phantom locations, and that interoperator and intraoperator coefficient of variation was less than 3%. For SWE target measurements, two systems were compared — a Supersonic Aixplorer scanner with a SL10‐2 and a SL15‐4 transducer, and an abovementioned GE scanner with 9L‐D transducer. Two stepped cylinders with diameters of 4.05–10.40 mm were measured both longitudinally and transaxially. Target shear wave speed quantification was performed using an in‐house MATLAB program. Using the target shear wave speed deduced from phantom specs as a reference, SL15‐4 performed the best at the measured depth. However, it was challenging to reliably measure a 4.05 mm target for either system. The reported test methods and results could provide important information when dealing with SWE‐related tasks in the clinical environment.

AbstractWe evaluated a commercially available software package that uses B‐mode images to semi‐automatically measure quantitative metrics of ultrasound image quality, such as contrast response, depth of penetration (DOP), and spatial resolution (lateral, axial, and elevational). Since measurement of elevational resolution is not a part of the software package, we achieved it by acquiring phantom images with transducers tilted at 45 degrees relative to the phantom. Each measurement was assessed in terms of measurement stability, sensitivity, repeatability, and semi‐automated measurement success rate. All assessments were performed on a GE Logiq E9 ultrasound system with linear (9L or 11L), curved (C1‐5), and sector (S1‐5) transducers, using a CIRS model 040GSE phantom. In stability tests, the measurements of contrast, DOP, and spatial resolution remained within a ±10% variation threshold in 90%, 100%, and 69% of cases, respectively. In sensitivity tests, contrast, DOP, and spatial resolution measurements followed the expected behavior in 100%, 100%, and 72% of cases, respectively. In repeatability testing, intra‐ and inter‐individual coefficients of variations were equal to or less than 3.2%, 1.3%, and 4.4% for contrast, DOP, and spatial resolution (lateral and axial), respectively. The coefficients of variation corresponding to the elevational resolution test were all within 9.5%. Overall, in our assessment, the evaluated package performed well for objective and quantitative assessment of the above‐mentioned image qualities under well‐controlled acquisition conditions. We are finding it to be useful for various clinical ultrasound applications including performance comparison between scanners from different vendors.