Tao, Shengzhen

AbstractPurposeThe purpose of this work is to evaluate the scaled computed tomography (CT) number accuracy of an artificial 120 kV reconstruction technique based on phantom experiments in the context of radiation therapy planning.MethodsAn abdomen‐shaped electron density phantom was scanned on a clinical CT scanner capable of artificial 120 kV reconstruction using different tube potentials from 70 to 150 kV. A series of tissue‐equivalent phantom inserts (lung, adipose, breast, solid water, liver, inner bone, 30%/50% CaCO3, cortical bone) were placed inside the phantom. Images were reconstructed using a conventional quantitative reconstruction kernel as well as the artificial 120 kV reconstruction kernel. Scaled CT numbers of inserts were measured from images acquired at different kVs and compared with those acquired at 120 kV, which were deemed as the ground truth. The relative error was quantified as the percentage deviation of scaled CT numbers acquired at different tube potentials from their ground truth values acquired at 120 kV.ResultsScaled CT numbers measured from images reconstructed using the conventional reconstruction demonstrated a strong kV‐dependence. The relative error in scaled CT numbers ranged from 0.6% (liver insert) to 31.1% (cortical bone insert). The artificial 120 kV reconstruction reduced the kV dependence, especially for bone tissues. The relative error in scaled CT number was reduced to 0.4% (liver insert) and 2.6% (30% CaCO3 insert) using this technique. When tube potential selection was limited to the range of 90 to 150 kV, the relative error was further restrained to <1.2% for all tissue types.ConclusionPhantom results demonstrated that using the artificial 120 kV technique, it was feasible to acquire raw projection data at the desired tube potential and then reconstruct images with scaled CT numbers comparable to those obtained directly at 120 kV. In radiotherapy applications, this technique may allow optimization of tube potential without complicating clinical workflow by eliminating the necessity of maintaining multiple sets of CT calibration curves.

Purpose To determine the accuracy of CT number and calcium score of a kV‐independent technique based on an artificial 120 kV reconstruction, and its potential to reduce radiation dose. Methods Anthropomorphic chest phantoms were scanned on a third‐generation dual‐source CT system equipped with the artificial 120 kV reconstruction. First, a phantom module containing a 20‐mm diameter hydroxyapatite (HA) insert was scanned inside the chest phantoms at different tube potentials (70–140 kV) to evaluate calcium CT number accuracy. Next, three small HA inserts (diameter/length = 5 mm) were inserted into a pork steak and scanned inside the phantoms to evaluate calcium score accuracy at different kVs. Finally, the same setup was scanned using automatic exposure control (AEC) at 120 kV, and then with automatic kV selection (auto‐kV). Phantoms were also scanned at 120 kV using a size‐dependent mA chart. CT numbers of soft tissue and calcium were measured from different kV images. Calcium score of each small HA insert was measured using commercial software. Results The CT number difference from 120 kV was small with tube potentials from 90 to 140 kV for both soft tissue and calcium (maximal difference of 4/5 HU, respectively). Consistent calcium scores were obtained from images of different kVs compared to 120 kV, with a relative difference <8%. Auto‐kV provided a 25–34% dose reduction compared to AEC alone. Conclusion A kV‐independent calcium scoring technique can produce artificial 120 kV images with consistent soft tissue and calcium CT numbers compared to standard 120 kV examinations. When coupled with auto‐kV, this technique can reduce radiation by 25–34% compared to that with AEC alone, while providing consistent calcium scores as that of standard 120 kV examinations.

PurposeTo develop a convolutional neural network (CNN) that can directly estimate material density distribution from multi‐energy computed tomography (CT) images without performing conventional material decomposition.MethodsThe proposed CNN (denoted as Incept‐net) followed the general framework of encoder–decoder network, with an assumption that local image information was sufficient for modeling the nonlinear physical process of multi‐energy CT. Incept‐net was implemented with a customized loss function, including an in‐house‐designed image‐gradient‐correlation (IGC) regularizer to improve edge preservation. The network consisted of two types of customized multibranch modules exploiting multiscale feature representation to improve the robustness over local image noise and artifacts. Inserts with various densities of different materials [hydroxyapatite (HA), iodine, a blood–iodine mixture, and fat] were scanned using a research photon‐counting detector (PCD) CT with two energy thresholds and multiple radiation dose levels. The network was trained using phantom image patches only, and tested with different‐configurations of full field‐of‐view phantom and in vivo porcine images. Furthermore, the nominal mass densities of insert materials were used as the labels in CNN training, which potentially provided an implicit mass conservation constraint. The Incept‐net performance was evaluated in terms of image noise, detail preservation, and quantitative accuracy. Its performance was also compared to common material decomposition algorithms including least‐square‐based material decomposition (LS‐MD), total‐variation regularized material decomposition (TV‐MD), and U‐net‐based method.ResultsIncept‐net improved accuracy of the predicted mass density of basis materials compared with the U‐net, TV‐MD, and LS‐MD: the mean absolute error (MAE) of iodine was 0.66, 1.0, 1.33, and 1.57 mgI/cc for Incept‐net, U‐net, TV‐MD, and LS‐MD, respectively, across all iodine‐present inserts (2.0–24.0 mgI/cc). With the LS‐MD as the baseline, Incept‐net and U‐net achieved comparable noise reduction (both around 95%), both higher than TV‐MD (85%). The proposed IGC regularizer effectively helped both Incept‐net and U‐net to reduce image artifact. Incept‐net closely conserved the total mass densities (i.e., mass conservation constraint) in porcine images, which heuristically validated the quantitative accuracy of its outputs in anatomical background. In general, Incept‐net performance was less dependent on radiation dose levels than the two conventional methods; with approximately 40% less parameters, the Incept‐net achieved relatively improved performance than the comparator U‐net, indicating that performance gain by Incept‐net was not achieved by simply increasing network learning capacity.ConclusionIncept‐net demonstrated superior qualitative image appearance, quantitative accuracy, and lower noise than the conventional methods and less sensitive to dose change. Incept‐net generalized and performed well with unseen image structures and different material mass densities. This study provided preliminary evidence that the proposed CNN may be used to improve the material decomposition quality in multi‐energy CT.

Objective The aim of this study was to grade cartilage damage in a swine model of osteoarthritis using a whole-body photon-counting detector (PCD) CT. Materials and Methods A multienergy phantom containing gadolinium (Gd) (2, 4, 8, and 16 mg/mL) and hydroxyapatite (200 and 400 mg/cc) was scanned using a PCD-CT system (48 × 0.25 mm collimation, 80 kV, 800 mAs, D50 reconstruction kernel) to serve as calibration for material decomposition and to assess quantification accuracy. Osteoarthritis was induced in Yucatan miniswine (n = 8) using 1.2 mg monoiodoacetate (MIA) injected into a randomized knee, whereas the contralateral control knee received saline. Twenty-one days later, a contrast bolus (gadoterate meglumine, 4 mL/knee) was intra-articularly administered into both knees. The knees were simultaneously scanned on the PCD-CT system (48 × 0.25 mm collimation, 80 kV, 800 mAs). Multienergy images were reconstructed with a sharp “V71” kernel and a quantitative “D50” kernel. Image denoising was applied to the V71 images before grading cartilage damage, and an iterative material decomposition technique was applied to D50 images to generate the Gd maps. Two radiologists blinded to the knee injection status graded the cartilage integrity based on a modified International Cartilage Repair Society scoring system. Histology was performed on excised cartilage using methylene blue/basic fuchsin. Statistical analysis of grade distribution was performed using an exact test of omnibus symmetry with P < 0.05 considered significant. Results Material decomposed images from the multienergy phantom scan showed delineation and quantification of Gd and hydroxyapatite with a root-mean-squared error of 0.3 mg/mL and 18.4 mg/cc, respectively. In the animal cohort, the radiologists reported chondromalacia in the MIA knees with International Cartilage Repair Society scores ranging from grade 1 (cartilage heterogeneity, n = 4 knees) to grade 3 (up to 100% cartilage loss, n = 4 knees). Grade 1 was characterized by cartilage heterogeneity and increased joint space in the patellofemoral compartment, whereas grade 3 was characterized by cartilage erosion and bone-on-bone articulation in the patellofemoral compartment. All control knees were scored as grade 0 (normal cartilage). Significant difference (P = 0.004) was observed in the grade distribution between the MIA and control knees. Gross examination of the excised knees showed cartilage lesions in the grade 3 MIA knees. The Gd maps from material decomposition showed lower contrast levels in the joint space of the MIA knee compared with the contralateral control knee due to joint effusion. Histology revealed chondrocyte loss in the MIA knee cartilage confirming the chondrotoxic effects of MIA on cartilage matrix. Conclusions We demonstrated a high-resolution and quantitative PCD-CT arthrography technique for grading cartilage damage in a large animal model of osteoarthritis. Photon-counting detector CT offers simultaneous high-resolution and multienergy imaging capabilities that allowed morphological assessment of cartilage loss and quantification of contrast levels in the joint as a marker of joint disease. Cartilage damage in the MIA knees was graded using PCD-CT images, and the image-based findings were further confirmed using histology and gross examination of the excised knees.

Objective The aim of this study was to quantitatively demonstrate radiation dose reduction for sinus and temporal bone examinations using high-resolution photon-counting detector (PCD) computed tomography (CT) with an additional tin (Sn) filter. Materials and Methods A multienergy CT phantom, an anthropomorphic head phantom, and a cadaver head were scanned on a research PCD-CT scanner using ultra-high-resolution mode at 100-kV tube potential with an additional tin filter (Sn-100 kV) and volume CT dose index of 10 mGy. They were also scanned on a commercial CT scanner with an energy-integrating detector (EID) following standard clinical protocols. Thirty patients referred to clinically indicated sinus examinations, and two patients referred to temporal bone examinations were scanned on the PCD-CT system after their clinical scans on an EID-CT. For the sinus cohort, PCD-CT scans were performed using Sn-100 kV at 4 dose levels at 10 mGy (n = 9), 8 mGy (n = 7), 7 mGy (n = 7), and 6 mGy (n = 7), and the clinical EID-CT was performed at 120 kV and 13.7 mGy (mean CT volume dose index). For the temporal bone scans, PCD-CT was performed using Sn-100 kV (10.1 mGy), and EID-CT was performed at 120 kV and routine clinical dose (52.6 and 66 mGy). For both PCD-CT and EID-CT, sinus images were reconstructed using H70 kernel at 0.75-mm slice thickness, and temporal bone images were reconstructed using a U70 kernel at 0.6-mm slice thickness. In addition, iterative reconstruction with a dedicated sharp kernel (V80) was used to obtain high-resolution PCD-CT images from a sinus patient scan to demonstrate improved anatomic delineation. Improvements in spatial resolution from the dedicated sharp kernel was quantified using modulation transfer function measured with a wire phantom. A neuroradiologist assessed the H70 sinus images for visualization of critical anatomical structures in low-dose PCD-CT images and routine-dose EID-CT images using a 5-point Likert scale (structural detection obscured and poor diagnostic confidence, score = 1; improved anatomic delineation and diagnostic confidence, score = 5). Image contrast and noise were measured in representative regions of interest and compared between PCD-CT and EID-CT, and the noise difference between the 2 acquisitions was used to estimate the dose reduction in the sinus and temporal bone patient cohorts. Results The multienergy phantom experiment showed a noise reduction of 26% in the Sn-100 kV PCD-CT image, corresponding to a total dose reduction of 56% compared with EID-CT (clinical dose) without compromising image contrast. The PCD-CT images from the head phantom and the cadaver scans demonstrated a dose reduction of 67% and 83%, for sinus and temporal bone examinations, respectively, compared with EID-CT. In the sinus cohort, PCD-CT demonstrated a mean dose reduction of 67%. The 10- and 8-mGy sinus patient images from PCD-CT were significantly superior to clinical EID-CT for visualization of critical sinus structures (median score = 5 ± 0.82 and P = 0.01 for lesser palatine foramina, median score = 4 ± 0.68 and P = 0.039 for nasomaxillary sutures, and median score = 4 ± 0.96 and P = 0.01 for anterior ethmoid artery canal). The 6- and 7-mGy sinus patient images did not show any significant difference between PCD-CT and EID-CT. In addition, V80 (sharp kernel, 10% modulation transfer function = 18.6 cm −1 ) PCD-CT images from a sinus patient scan increased the conspicuity of nasomaxillary sutures compared with the clinical EID-CT images. The temporal bone patient images demonstrated a dose reduction of up to 85% compared with clinical EID-CT images, whereas visualization of inner ear structures such as the incudomalleolar joint were similar between EID-CT and PCD-CT. Conclusions Phantom and cadaver studies demonstrated dose reduction using Sn-100 kV PCD-CT compared with current clinical EID-CT while maintaining the desired image contrast. Dose reduction was further validated in sinus and temporal bone patient studies. The ultra-high resolution capability from PCD-CT allowed improved anatomical delineation for sinus imaging compared with current clinical standard.

PurposePhoton‐counting‐detector‐computed tomography (PCD‐CT) allows separation of multiple, simultaneously imaged contrast agents, such as iodine (I), gadolinium (Gd), and bismuth (Bi). However, PCDs suffer from several technical limitations such as charge sharing, K‐edge escape, and pulse pile‐up, which compromise spectral separation of multi‐energy data and degrade multi‐contrast imaging performance. The purpose of this work was to determine the performance of a dual‐source (DS) PCD‐CT relative to a single‐source (SS) PCD‐CT for the separation of simultaneously imaged I, Gd, and Bi contrast agents.MethodsPhantom experiments were performed using a research whole‐body PCD‐CT and head/abdomen‐sized phantoms containing vials of different I, Gd, Bi concentrations. To emulate a DS‐PCD‐CT, the phantoms were scanned twice on the SS‐PCD‐CT using different tube potentials for each scan. A tube potential of 80 kV (energy thresholds = 25/50 keV) was used for low‐energy tube, while the high‐energy tube used Sn140 kV (Sn indicates tin filter) and thresholds of 25/90 keV. The same phantoms were scanned also on the SS‐PCD‐CT using the chess acquisition mode. In chess mode, the 4 × 4 subpixels within a macro detector pixel are split into two sets based on a chess‐board pattern. With each subpixel set having two energy thresholds, chess mode allows four energy‐bin data sets, which permits simultaneous multi‐contrast imaging. Because of this design, only 50% area of each detector pixel is configured to receive photons of a pre‐defined threshold, leading to 50% dose utilization efficiency. To compensate for this dose inefficiency, the radiation dose for this scan was doubled compared to DS‐PCD‐CT. A 140 kV tube potential and thresholds = 25/50/75/90 keV were used. These settings were determined based on the K‐edges of Gd, and Bi, and were found to yield good differentiation of I/Gd/Bi based on phantom experiments and other literature. The energy‐bin images obtained from each scan (scan pair) were used to generate I‐, Gd‐, Bi‐specific image via material decomposition. Root‐mean‐square‐error (RMSE) between the known and measured concentrations was calculated for each scenario. A 20‐cm water cylinder phantom was scanned on both systems, which was used for evaluating the magnitude of noise, and noise power spectra (NPS) of I/Gd/Bi‐specific images.ResultsPhantom results showed that DS‐PCD‐CT reduced noise in material‐specific images for both head and body phantoms compared to SS‐PCD‐CT. The noise level of SS‐PCD was reduced from 2.55 to 0.90 mg/mL (I), 1.97 to 0.78 mg/mL (Gd), and 0.85 to 0.74 mg/mL (Bi) using DS‐PCD. NPS analysis showed that the noise texture of images acquired on both systems is similar. For the body phantom, the RMSE for SS‐PCD‐CT was reduced relative to DS‐PCD‐CT from 10.52 to 2.76 mg/mL (I), 7.90 to 2.01 mg/mL (Gd), and 1.91 to 1.16 mg/mL (Bi). A similar trend was observed for the head phantom: RMSE reduced from 2.59 (SS‐PCD) to 0.72 (DS‐PCD) mg/mL (I), 2.02 to 0.58 mg/mL (Gd), and 0.85 to 0.57 mg/mL (Bi).ConclusionWe demonstrate the feasibility of performing simultaneous imaging of I, Gd, and Bi materials on DS‐PCD‐CT. Under the condition without cross scattering, DS‐PCD reduced the RMSE for quantification of material concentration in relative to a SS‐PCD‐CT system using chess mode.

BackgroundDistortion‐free, high‐resolution diffusion imaging using DIADEM (Distortion‐free Imaging: A Double Encoding Method), proposed recently, has great potential for clinical applications. However, it can suffer from prolonged scan times and its reliability for quantitative diffusion imaging has not been evaluated.PurposeTo investigate the clinical feasibility of DIADEM‐based high‐resolution diffusion imaging on a novel compact 3T (C3T) by evaluating the reliability of quantitative diffusion measurements and utilizing both the high‐performance gradients (80 mT/m, 700 T/m/s) and the sequence optimization with the navigator acquisition window reduction and simultaneous multislice (multiband) imaging.Study TypeProspective feasibility study.Phantom/SubjectsDiffusion quality control phantom scans to evaluate the reliability of quantitative diffusion measurements; 36 normal control scans for B0‐field mapping; six healthy and two patient subject scans with a brain tumor for comparisons of diffusion and anatomical imaging.Field Strength/Sequence3T; the standard single‐shot echo‐planar‐imaging (EPI), multishot DIADEM diffusion, and anatomical (2D‐FSE [fast‐spin‐echo], 2D‐FLAIR [fluid‐attenuated‐inversion‐recovery], and 3D‐MPRAGE [magnetization prepared rapid acquisition gradient echo]) imaging.AssessmentThe scan time reduction, the reliability of quantitative diffusion measurements, and the clinical efficacy for high‐resolution diffusion imaging in healthy control and brain tumor volunteers.Statistical TestBland–Altman analysis.ResultsThe scan time for high in‐plane (0.86 mm2) resolution, distortion‐free, and whole brain diffusion imaging were reduced from 10 to 5 minutes with the sequence optimizations. All of the mean apparent diffusion coefficient (ADC) values in phantom were within the 95% confidence interval in the Bland–Altman plot. The proposed acquisition with a total off‐resonance coverage of 597.2 Hz wider than the expected bandwidth of 500 Hz in human brain could yield a distortion‐free image without foldover artifacts. Compared with EPI, therefore, this approach allowed direct image matching with the anatomical images and enabled improved delineation of the tumor boundaries.Data ConclusionThe proposed high‐resolution diffusion imaging approach is clinically feasible on C3T due to a combination of hardware and sequence improvements.Level of Evidence3Technical EfficacyStage 1 J. Magn. Reson. Imaging 2020;51:296–310.

BackgroundGradient nonlinearity (GNL) leads to biased apparent diffusion coefficients (ADCs) in diffusion‐weighted imaging. A gradient nonlinearity correction (GNLC) method has been developed for whole body systems, but is yet to be tested for the new compact 3T (C3T) scanner, which exhibits more complex GNL due to its asymmetrical design.PurposeTo assess the improvement of ADC quantification with GNLC for the C3T scanner.Study TypePhantom measurements and retrospective analysis of patient data.Phantom/SubjectsA diffusion quality control phantom with vials containing 0–30% polyvinylpyrrolidone in water was used. For in vivo data, 12 patient exams were analyzed (median age, 33).Field Strength/SequenceImaging was performed on the C3T and two commercial 3T scanners. A clinical DWI (repetition time [TR] = 10,000 msec, echo time [TE] = minimum, b = 1000 s/mm2) sequence was used for phantom imaging and 10 patient cases and a clinical DTI (TR = 6000–10,000 msec, TE = minimum, b = 1000 s/mm2) sequence was used for two patient cases.AssessmentThe 0% vial was measured along three orthogonal axes, and at two different temperatures. The ADC for each concentration was compared between the C3T and two whole‐body scanners. Cerebrospinal fluid and white matter ADCs were quantified for each patient and compared to values in literature.Statistical TestsPaired t‐test and two‐way analysis of variance (ANOVA).ResultsFor all PVP concentrations, the corrected ADC was within 2.5% of the reference ADC. On average, the ADC of cerebrospinal fluid and white matter post‐GNLC were within 1% and 6%, respectively, of values reported in the literature and were significantly different from the uncorrected data (P < 0.05).Data ConclusionThis study demonstrated that GNL effects were more severe for the C3T due to the asymmetric gradient design, but our implementation of a GNLC compensated for these effects, resulting in ADC values that are in good agreement with values from the literature.Level of Evidence: 4Technical Efficacy: Stage 2J. Magn. Reson. Imaging 2018;48:1498–1507