Yu, Lifeng

AbstractObjectiveIn X‐raycomputed tomography (CT), many important clinical applications may benefit from a fast acquisition speed. The helical scan is the most widely used acquisition mode in clinical CT, where a fast helical pitch can improve the acquisition speed. However, on a typical single‐source helical CT (SSCT) system, the helical pitch p typically cannot exceed 1.5; otherwise, reconstruction artifacts will result from data insufficiency. The purpose of this work is to develop a deep convolutional neural network (CNN) to correct for artifacts caused by an ultra‐fast pitch, which can enable faster acquisition speed than what is currently achievable.MethodsA customized CNN (denoted as ultra‐fast‐pitch network (UFP‐net)) was developed to restore the underlying anatomical structure from the artifact‐corrupted post‐reconstruction data acquired from SSCT with ultra‐fast pitch (i.e., p ≥ 2). UFP‐net employed residual learning to capture the features of image artifacts. UFP‐net further deployed in‐house‐customized functional blocks with spatial‐domain local operators and frequency‐domain non‐local operators, to explore multi‐scale feature representation. Images of contrast‐enhanced patient exams (n = 83) with routine pitch setting (i.e., p < 1) were retrospectively collected, which were used as training and testing datasets. This patient cohort involved CT exams over different scan ranges of anatomy (chest, abdomen, and pelvis) and CT systems (Siemens Definition, Definition Flash, Definition AS+, Siemens Healthcare, Inc.), and the corresponding base CT scanning protocols used consistent settings of major scan parameters (e.g., collimation and pitch). Forward projection of the original images was calculated to synthesize helical CT scans with one regular pitch setting (p = 1) and two ultra‐fast‐pitch setting (p = 2 and 3). All patient images were reconstructed using the standard filtered‐back‐projection (FBP) algorithm. A customized multi‐stage training scheme was developed to incrementally optimize the parameters of UFP‐net, using ultra‐fast‐pitch images as network inputs and regular pitch images as labels. Visual inspection was conducted to evaluate image quality. Structural similarity index (SSIM) and relative root‐mean‐square error (rRMSE) were used as quantitative quality metrics.ResultsThe UFP‐net dramatically improved image quality over standard FBP at both ultra‐fast‐pitch settings. At p = 2, UFP‐net yielded higher mean SSIM (> 0.98) with lower mean rRMSE (< 2.9%), compared to FBP (mean SSIM < 0.93; mean rRMSE > 9.1%). Quantitative metrics at p = 3: UFP‐net—mean SSIM [0.86, 0.94] and mean rRMSE [5.0%, 8.2%]; FBP—mean SSIM [0.36, 0.61] and mean rRMSE [36.0%, 58.6%].ConclusionThe proposed UFP‐net has the potential to enable ultra‐fast data acquisition in clinical CT without sacrificing image quality. This method has demonstrated reasonable generalizability over different body parts when the corresponding CT exams involved consistent base scan parameters.

PurposeMulti‐energy computed tomography (MECT) has a great potential to enable many novel clinical applications such as simultaneous multi‐contrast imaging. The purpose of this study was to implement triple‐beam MECT on a traditional energy‐integrating‐detector (EID) CT platform (EID‐MECT).MethodsThis was accomplished by mounting a z‐axis split‐filter (0.05 mm Au, 0.6 mm Sn) on Tube A of a dual‐source EID CT scanner. With the two split x‐ray beams from Tube A and the third beam from Tube B, three beams with different x‐ray spectra can be simultaneously acquired. With Tube B operated at 70 or 80 kV and Tube A at 120 or 140 kV, four different triple‐beam configurations were calibrated for MECT measurements: 70/Au120/Sn120, 80/Au120/Sn120, 70/Au140/Sn140, and 80/Au140/Sn140 kV. Iodine (I), gadolinium (Gd), bismuth (Bi) samples, and their mixtures were prepared for 2 three‐material‐decomposition tasks and 1 four‐material‐decomposition task. For each task, samples were placed in a water phantom and scanned using each of the four triple‐beam configurations. For comparison, the same phantom was also scanned using three other dual‐energy CT (DECT) or MECT technologies: twin‐beam DECT (TB‐DECT), dual‐source DECT (DS‐DECT), and photon‐counting‐detector CT (PCD‐CT), all with optimal x‐ray spectrum settings and at equal volume CT dose index (CTDIvol). The phantom for four‐material decomposition (I/Gd/Bi/Water imaging) was scanned using the PCD‐CT only (140 kV with 25, 50, 75, and 90 keV). Image‐based material decomposition was performed to acquire material‐specific images, on which the mean basis material concentrations and noise levels were measured and compared across all triple‐beam configurations in EID‐MECT and various DECT/MECT systems.ResultsThe optimal triple‐beam configuration was task‐dependent with 70/Au120/Sn120, 70/Au140/Sn140, and 70/Au120/Sn120 kV for I/Gd/Water, I/Bi/Water, and I/Gd/Bi/Water material decomposition tasks, respectively. At equal radiation dose level, EID‐MECT provided comparable or better quantification accuracy in material‐specific images for all three material decomposition tasks, compared to EID‐based DECT and PCD‐CT systems. In terms of noise level comparison, EID‐MECT‐derived material‐specific images showed lower noise levels than TB‐DECT and DS‐DECT, but slightly higher than that from PCD‐CT in I/Gd/Water imaging. For I/Bi/Water imaging, EID‐MECT showed a comparable noise level to DS‐DECT, and a much lower noise level than TB‐DECT and PCD‐CT in all material‐specific images. For the four‐material decomposition task involving I/Gd/Bi/Water, the bismuth‐specific image derived from EID‐MECT was slightly noisier, but both iodine‐ and gadolinium‐specific images showed much lower noise levels in comparison to PCD‐CT.ConclusionsFor the first time, an EID‐based MECT system that can simultaneously acquire three x‐ray spectra measurements was implemented on a clinical scanner, which demonstrated comparable or better imaging performance than existing DECT and MECT systems.

AbstractPurposeTo demonstrate that variation in chemical composition has a negligible effect on the mapping curve from relative electron density (RED) to proton stopping power ratio (SPR), and to establish the theoretical framework of using Megavoltage (MV) computed tomography (CT), instead of kilovoltage (kV) dual energy CT, to accurately estimate proton SPR.MethodsA simulation study was performed to evaluate the effect of chemical composition variation on kVCT number and proton SPR. The simulation study involved both reference and simulated human tissues. The reference human tissues, together with their physical densities and chemical compositions, came from the ICRP publication 23. The simulated human tissues were created from the reference human tissues assuming that elemental percentage weight followed a Gaussian distribution. For all tissues, kVCT number and proton SPR were obtained through (a) theoretical calculation from tissue’s physical density and chemical composition which served as the ground truth, and (b) estimation from RED using the calibration curves established from the stoichiometric method. Deviations of the estimated values from the calculated values were quantified as errors in using RED to estimate kVCT number and proton SPR.ResultsGiven a chemical composition variation of 5% (1σ) of the nominal percentage weights, the total estimation error of using RED to estimate kVCT number was 0.34%, 0.62%, and 0.77% and the total estimation error of using RED to estimate proton SPR was 0.30%, 0.22%, and 0.16% for fat tissues, non‐fat soft tissues and bone tissues, respectively.ConclusionChemical composition had a negligible effect on the method of using RED to determine proton SPR. RED itself is sufficient to accurately determine proton SPR. MVCT number maintains a superb linear relationship with RED because it is highly dominated by Compton scattering. Therefore, MVCT has great potential in reducing the proton range uncertainty.

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.

AbstractIt is important to employ radiation dose reduction techniques in pediatric computed tomography (CT) to reduce potential risks of radiation‐induced malignancy. Automatic tube potential (kV) selection tools have been developed and become available on many CT scanners, which select the optimum kV based on the patient size and clinical task to improve the radiation dose efficiency. However, its use in pediatric CT has been mostly empirical, following manufacturer’s default recommendation without solid demonstration for quality improvement. This study aimed to implement an automatic tube potential tool (CAREkV, Siemens Healthcare) into routine pediatric CT practice, using the “Plan‐Do‐Study‐Act” quality improvement process, in place of an existing kV/mAs technique chart. The design of this quality improvement project involved Plan‐Do‐Study‐Act stages. Plan and Do stages identified the criteria for optimal automatic kV selection; a range of phantoms representing typical pediatric groups were scanned on a dual‐source 128‐slice scanner using a fast‐pitch scanning mode. The identified CAREkV settings were implemented into the CT protocol and evaluated after a 6‐month period. In the Study stage, an objective evaluation of the image metrics and radiation dose for two similar patient cohorts using CAREkV and the technique‐chart, respectively, were compared. The kV selected, image quality and radiation dose determined by CAREkV were comparable to those obtained while using the technique‐chart. The CAREkV was successfully implemented into our pediatric abdominopelvic CT practice. By utilizing the “PDSA” process optimal image quality and radiation dose reduction were achieved with an automatic kV selection tool to improve CT workflow.

Purpose To describe a large, publicly available dataset comprising computed tomography (CT) projection data from patient exams, both at routine clinical doses and simulated lower doses. Acquisition and Validation Methods The library was developed under local ethics committee approval. Projection and image data from 299 clinically performed patient CT exams were archived for three types of clinical exams: noncontrast head CT scans acquired for acute cognitive or motor deficit, low‐dose noncontrast chest scans acquired to screen high‐risk patients for pulmonary nodules, and contrast‐enhanced CT scans of the abdomen acquired to look for metastatic liver lesions. Scans were performed on CT systems from two different CT manufacturers using routine clinical protocols. Projection data were validated by reconstructing the data using several different reconstruction algorithms and through use of the data in the 2016 Low Dose CT Grand Challenge. Reduced dose projection data were simulated for each scan using a validated noise‐insertion method. Radiologists marked location and diagnosis for detected pathologies. Reference truth was obtained from the patient medical record, either from histology or subsequent imaging. Data Format and Usage Notes Projection datasets were converted into the previously developed DICOM‐CT‐PD format, which is an extended DICOM format created to store CT projections and acquisition geometry in a nonproprietary format. Image data are stored in the standard DICOM image format and clinical data in a spreadsheet. Materials are provided to help investigators use the DICOM‐CT‐PD files, including a dictionary file, data reader, and user manual. The library is publicly available from The Cancer Imaging Archive ( https://doi.org/10.7937/9npb‐2637 ). Potential Applications This CT data library will facilitate the development and validation of new CT reconstruction and/or denoising algorithms, including those associated with machine learning or artificial intelligence. The provided clinical information allows evaluation of task‐based diagnostic performance.

Background Low tube potential-high tube current computed tomography (CT) imaging allows reduction in iodine-based contrast dose and may extend the benefit of routine contrast-enhanced CT exams to patients at risk of nephrotoxicity. Purpose To determine the ability of an iodine contrast reduction algorithm to maintain diagnostic image quality for contrast-enhanced abdominal CT. Material and Methods CT exams with iodine contrast reduction were prescribed for patients at risk for renal dysfunction. The iodine contrast reduction algorithm combines weight-based contrast volume reduction with patient width-based low tube potential selection and bolus-tracking. Control exams with routine iodine dose were selected based on weight, width, and scan protocol. Three radiologists evaluated image quality and diagnostic confidence using a 4-point scale (<2 acceptable). Another radiologist assessed contrast reduction indications and measured portal vein and liver contrast-to-noise ratios. Results Forty-six contrast reduction algorithm and control exams were compared (mean creatinine 1.6 vs. 1.2 mg/dL, P ≤ 0.0001). Thirty-nine contrast reduction patients had an eGFR <60 mL/min/1.73m 2 and 15 had single or transplanted kidney. Mean iodine contrast dose was lower in the contrast reduction group (20.9 vs. 39.4 g/mL, P < 0.0001). Diagnostic confidence was rated as acceptable in 95% (131/138) of contrast reduction and 100% of control exams (1.18–1.28 vs. 1.02–1.13, respectively; P > 0.06). Liver attenuation and contrast-to-noise ratio (CNR) were similar ( P = 0.08), but portal vein attenuation and CNR were lower with contrast-reduction (mean 176 vs. 198 HU, P = 0.02; 13 vs. 16, P = 0.0002). Conclusion This size-based contrast reduction algorithm using low kV and bolus tracking reduced iodine contrast dose by 50%, while achieving acceptable image quality in 95% of exams.