Yu, Lifeng

Abstract Objective . Photon-counting detector (PCD) CT enables routine virtual-monoenergetic image (VMI) reconstruction. We evaluated the performance of an automatic VMI energy level (keV) selection tool on a clinical PCD-CT system in comparison to an automatic tube potential (kV) selection tool from an energy-integrating-detector (EID) CT system from the same manufacturer. Approach. Four torso-shaped phantoms (20–50 cm width) containing iodine (2, 5, and 10 mg cc −1 ) and calcium (100 mg cc −1 ) were scanned on PCD-CT and EID-CT. Dose optimization techniques, task-based VMI energy level and tube-potential selection on PCD-CT (CARE keV) and task-based tube potential selection on EID-CT (CARE kV), were enabled. CT numbers, image noise, and dose-normalized contrast-to-noise ratio (CNR d ) were compared. Main results . PCD-CT produced task-specific VMIs at 70, 65, 60, and 55 keV for non-contrast, bone, soft tissue with contrast, and vascular settings, respectively. A 120 kV tube potential was automatically selected on PCD-CT for all scans. In comparison, EID-CT used x-ray tube potentials from 80 to 150 kV based on imaging task and phantom size. PCD-CT achieved consistent dose reduction at 9%, 21% and 39% for bone, soft tissue with contrast, and vascular tasks relative to the non-contrast task, independent of phantom size. On EID-CT, dose reduction factor for contrast tasks relative to the non-contrast task ranged from a 65% decrease (vascular task, 70 kV, 20 cm phantom) to a 21% increase (soft tissue with contrast task, 150 kV, 50 cm phantom) due to size-specific tube potential adaptation. PCD-CT CNR d was equivalent to or higher than those of EID-CT for all tasks and phantom sizes, except for the vascular task with 20 cm phantom, where 70 kV EID-CT CNR d outperformed 55 keV PCD-CT images. Significance . PCD-CT produced more consistent CT numbers compared to EID-CT due to standardized VMI output, which greatly benefits standardization efforts and facilitates radiation dose reduction.

AbstractBackgroundThe detectability performance of a CT scanner is difficult to precisely quantify when nonlinearities are present in reconstruction. An efficient detectability assessment method that is sensitive to small effects of dose and scanner settings is desirable. We previously proposed a method using a search challenge instrument: a phantom is embedded with hundreds of lesions at random locations, and a model observer is used to detect lesions. Preliminary tests in simulation and a prototype showed promising results.PurposeIn this work, we fabricated a full‐size search challenge phantom with design updates, including changes to lesion size, contrast, and number, and studied our implementation by comparing the lesion detectability from a nonprewhitening (NPW) model observer between different reconstructions at different exposure levels, and by estimating the instrument sensitivity to detect changes in dose.MethodsDesigned to fit into QRM anthropomorphic phantoms, our search challenge phantom is a cylindrical insert 10 cm wide and 4 cm thick, embedded with 12 000 lesions (nominal width of 0.6 mm, height of 0.8 mm, and contrast of −350 HU), and was fabricated using PixelPrint, a 3D printing technique. The insert was scanned alone at a high dose to assess printing accuracy. To evaluate lesion detectability, the insert was placed in a QRM thorax phantom and scanned from 50 to 625 mAs with increments of 25 mAs, once per exposure level, and the average of all exposure levels was used as high‐dose reference. Scans were reconstructed with three different settings: filtered‐backprojection (FBP) with Br40 and Br59, and Sinogram Affirmed Iterative Reconstruction (SAFIRE) with strength level 5 and Br59 kernel. An NPW model observer was used to search for lesions, and detection performance of different settings were compared using area under the exponential transform of free response ROC curve (AUC). Using propagation of uncertainty, the sensitivity to changes in dose was estimated by the percent change in exposure due to one standard deviation of AUC, measured from 5 repeat scans at 100, 200, 300, and 400 mAs.ResultsThe printed insert lesions had an average position error of 0.20 mm compared to printing reference. As the exposure level increases from 50 mAs to 625 mAs, the lesion detectability AUCs increase from 0.38 to 0.92, 0.42 to 0.98, and 0.41 to 0.97 for FBP Br40, FBP Br59, and SAFIRE Br59, respectively, with a lower rate of increase at higher exposure level. FBP Br59 performed best with AUC 0.01 higher than SAFIRE Br59 on average and 0.07 higher than FBP Br40 (all P < 0.001). The standard deviation of AUC was less than 0.006, and the sensitivity to detect changes in mAs was within 2% for FBP Br59.ConclusionsOur 3D‐printed search challenge phantom with 12 000 submillimeter lesions, together with an NPW model observer, provide an efficient CT detectability assessment method that is sensitive to subtle effects in reconstruction and is sensitive to small changes in dose.

AbstractBackgroundDeep‐learning‐based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images.PurposeIn this work, we aim to develop a patient‐data‐based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method.MethodsThe patient‐data‐based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR‐processed images from projection or image data; (4) creation of ensembles of DLIR‐processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (−500, −100, −50, −20, −10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered‐backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN‐weak, DCNN‐medium, and DCNN‐strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion‐present images from the lesion absent images. Modulation transfer functions (MTFs) both in‐plane and along the z‐axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t‐test was used to compare the z‐axis and in‐plane spatial resolution of five algorithms for five different contrasts and three dose levels.ResultsThe in‐plane and z‐axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in‐plane and z‐axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (−10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in‐plane and z‐axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of −100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in‐plane and cross‐plane spatial resolution: FBP > DCNN‐Weak > IR > DCNN‐Medium > DCNN‐Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels.ConclusionsA patient‐data‐based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non‐linear image reconstruction and post‐processing techniques, the evaluated DCNN method degraded the in‐plane and z‐axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.

AbstractBackgroundObjective and quantitative evaluation for low‐contrast detectability that correlates with human observer performance is lacking for routine CT quality control testing. Channelized Hotelling observer (CHO) is considered a strong candidate to fill the need but has long been deemed impractical to implement due to its requirement of a large number of repeated scans in order to provide accurate and precise estimates of index of detectability (d’). In our previous work, we optimized a CHO model observer on the American College of Radiology (ACR) CT accreditation phantom and achieved accurate measurement of d’ with only 1–3 repeat scans.PurposeIn this work, we aim to validate the repeatability of the proposed CHO‐based low‐contrast evaluation on four scanner models using the ACR CT accreditation phantom.MethodsThe repeatability test was performed on four different scanners from two major CT manufacturers: Siemens Force and Alpha; Canon Prism and Prime SP. An ACR CT phantom was scanned 10 times, each time after repositioning of the phantom. For each repositioning, 3 repeated scans were acquired at 24, 12, and 6 mGy on all four scanner models. CHO was applied at the measured dose levels for different low‐contrast object sizes (4–6 mm). The CHO was also applied to images created using deep learning‐based reconstructions on Canon Prism and to four different scan/reconstruction modes on the Siemens Alpha, a photon‐counting‐detector (PCD)‐CT. The repeatability was evaluated by the probability that a measurement would fall within the ±15% tolerance (P<15%).ResultsWith the CHO setting optimized for the ACR phantom and the use of 3 repeated scans and 9 non‐overlapping slices per scan, the CHO measurement could provide high repeatability with P<15% of 98.8%–99.9% at 12 mGy with IR reconstruction on all four scanners. On scanner A, P<15% were 91.5%–99.9% at the three dose levels and for all three object sizes while the numbers were 93.6%–99.998% on scanner B. P<15% were 96.5%–97.2% for the two deep learning reconstructions and 97.0%–99.97% for the four scan/reconstruction modes on the PCD‐CT.ConclusionThe CHO provided highly repeatable measurements with over 95% probability that a CHO measurement would lie within the ±15% tolerance for most of the dose levels and object sizes on the ACR phantom. The repeatability was maintained when the CHO was applied to images created with a commercial deep learning‐based reconstruction and various scan/reconstruction modes on a PCD‐CT. This study demonstrates that practical implementation of CHO for routine quality control and performance evaluation is feasible.

Abstract Photon counting detector (PCD) CT represents the newest advance in CT technology, with improved radiation dose efficiency, increased spatial resolution, inherent spectral imaging capabilities, and the ability to eliminate electronic noise. Its design fundamentally differs from conventional energy integrating detector CT because photons are directly converted to electrical signal in a single step. Rather than converting X-rays to visible light and having an output signal that is a summation of energies, PCD directly counts each photon and records its individual energy information. The current commercially available PCD-CT utilizes a dual-source CT geometry, which allows 66 ms cardiac temporal resolution and high-pitch (up to 3.2) scanning. This can greatly benefit pediatric patients by facilitating high quality fast scanning to allow sedation-free imaging. The energy-resolving nature of the utilized PCDs allows “always-on” dual-energy imaging capabilities, such as the creation of virtual monoenergetic, virtual non-contrast, virtual non-calcium, and other material-specific images. These features may be combined with high-resolution imaging, made possible by the decreased size of individual detector elements and the absence of interelement septa. This work reviews the foundational concepts associated with PCD-CT and presents examples to highlight the benefits of PCD-CT in the pediatric population.

We present an updated ultrafast dynamic computed tomography myelography technique that can be used for the localization of spontaneous spinal cerebrospinal fluid leaks in the setting of spontaneous intracranial hypotension. This has over twice the temporal resolution of previously described techniques at the same radiation dose output.

Background and Purpose Recent introduction of photon counting detector (PCD) computed tomography (CT) scanners into clinical practice further improve CT angiography (CTA) depiction of orbital arterial vasculature compared to conventional energy integrating detector (EID) CT scanners. PCD-CTA of the orbit can provide a detailed arterial roadmap of the orbit which can de diagnostic on its own or serve as a helpful planning adjunct for both diagnostic and therapeutic catheter-based angiography of the orbit. Methods For this review, EID and PCD-CT imaging was obtained in 28 volunteers. The volume CT dose index was closely matched. A dual-energy scanning protocol was used on EID-CT. An ultra-high-resolution (UHR) scan mode was used on PCD-CT. Images were reconstructed at 0.6 mm slice thickness using a closely matched medium-sharp standard resolution (SR) kernel. High-resolution (HR) images with the sharpest quantitative kernel were also reconstructed on PCD-CT at the thinnest slice thickness of 0.2 mm. A denoising algorithm was applied to the HR image series. Results The imaging description of the orbital vascular anatomy presented in this work was derived from these patients’ PCD-CTA images in combination with review of the literature. We found that orbital arterial anatomy is much better depicted with PCD-CTA, and this work can serve primarily as an imaging atlas of the normal orbital vascular anatomy. Conclusion With recent advances in technology, arterial anatomy of the orbit is much better depicted with PCD-CTA as opposed to EID-CTA. Current orbital PCD-CTA technology approaches the necessary resolution threshold for reliable evaluation of central retinal artery occlusion.

Objective Noise quantification is fundamental to computed tomography (CT) image quality assessment and protocol optimization. This study proposes a deep learning–based framework, Single-scan Image Local Variance EstimatoR (SILVER), for estimating the local noise level within each region of a CT image. The local noise level will be referred to as a pixel-wise noise map. Methods The SILVER architecture resembled a U-Net convolutional neural network with mean-square-error loss. To generate training data, 100 replicate scans were acquired of 3 anthropomorphic phantoms (chest, head, and pelvis) using a sequential scan mode; 120,000 phantom images were allocated into training, validation, and testing data sets. Pixel-wise noise maps were calculated for the phantom data by taking the per-pixel SD from the 100 replicate scans. For training, the convolutional neural network inputs consisted of phantom CT image patches, and the training targets consisted of the corresponding calculated pixel-wise noise maps. Following training, SILVER noise maps were evaluated using phantom and patient images. For evaluation on patient images, SILVER noise maps were compared with manual noise measurements at the heart, aorta, liver, spleen, and fat. Results When tested on phantom images, the SILVER noise map prediction closely matched the calculated noise map target (root mean square error <8 Hounsfield units). Within 10 patient examinations, SILVER noise map had an average percent error of 5% relative to manual region-of-interest measurements. Conclusion The SILVER framework enabled accurate pixel-wise noise level estimation directly from patient images. This method is widely accessible because it operates in the image domain and requires only phantom data for training.