Ren, Liqiang

AbstractBackgroundThe first commercially available photon‐counting‐detector CT (PCD‐CT) has been introduced for clinical use. However, its spectral performance on single‐ and dual‐contrast imaging tasks has not been comprehensively assessed.PurposeTo evaluate the spectral imaging performance of a clinical PCD‐CT system for single‐contrast material [iodine (I) or gadolinium (Gd)] and dual‐contrast materials (I and Gd) in comparison with a dual‐source dual‐energy CT (DS‐DECT).MethodsIodine (5, 10, and 15 mg/mL) and gadolinium (3.3, 6.6, and 9.9 mg/mL) samples, and their mixtures (I/Gd: 5/3.3 and 10/6.6 mg/mL) were prepared and placed in two torso‐shaped water phantoms (lateral dimensions: 30 and 40 cm). These phantoms were scanned on a PCD‐CT (NAEOTOM Alpha, Siemens) at 90, 120, and 140 kV. The same phantoms were scanned on a DS‐DECT (SOMATOM Force, Siemens) with 70/Sn150, 80/Sn150, 90/Sn150, and 100/Sn150 kV. The radiation dose levels were matched [volume CT dose index (CTDIvol): 10 mGy for the 30 cm phantom and 20 mGy for the 40 cm phantom] across all tube voltage settings and between scanners. Two‐material decomposition (I/water or Gd/water) was performed on iodine or gadolinium samples, and three‐material decomposition (I/Gd/water) on both individual samples and mixtures. On each decomposed image, mean mass concentration (± standard deviation) was measured in circular region‐of‐interests placed on the contrast samples. Root‐mean‐square‐error (RMSE) values of iodine and gadolinium concentrations were reported based on the measurements across all contrast samples and repeated on 10 consecutive slices.ResultsFor all material decomposition tasks on the DS‐DECT, the kV pairs with greater spectral separation (70/Sn150 kV and 80/Sn150 kV) yielded lower RMSE values than other DS‐DECT and PCD‐CT alternatives. Specifically, for the optimal 70/Sn150 kV, RMSE values were 1.2 ± 0.1 mg/mL (I) for I/water material decomposition, 1.0 ± 0.1 mg/mL (Gd) for Gd/water material decomposition, and 4.5 ± 0.2 mg/mL (I) and 3.7 ± 0.2 mg/mL (Gd), respectively, for I/Gd/water material decomposition. On the PCD‐CT, the optimal tube voltages were 120 or 140 kV for I/water decomposition with RMSE values of 2.0 ± 0.1 mg/mL (I). For Gd/water decomposition on PCD‐CT, the optimal tube voltage was 140 kV with gadolinium RMSE values of 1.5 ± 0.1 mg/mL (Gd), with the 90 kV setting on PCD‐CT generating higher RMSE values for gadolinium concentration compared to all DS‐DECT and PCD‐CT alternatives. For three material decomposition, both imaging modalities demonstrated substantially higher RMSE values for iodine and gadolinium, with 90 kV being the optimal tube potential for Gd/I quantitation on PCD‐CT [5.4 ± 0.3 mg/mL (I) and 3.9 ± 0.2 mg/mL (Gd)], and DS‐DECT at 100/Sn150 kV having larger RMSE values for both materials compared to the alternatives for either modality.ConclusionOptimal tube voltage for material decomposition on the clinical PCD‐CT is task‐dependent but inferior to DS‐DECT using 70/Sn150 kV or 80/Sn150 kV in two‐material decomposition for single‐contrast imaging (iodine/water or gadolinium/water). Three material decomposition (iodine/gadolinium/water) in dual‐contrast imaging yields substantially higher RMSE for both imaging platforms.

The purpose of this study is to investigate the potential of using ultrasound gel to mitigate the risks of skin burn at the insertion site during microwave ablation (MWA) using non-actively cooled applicators. Ex vivo experiments in porcine tissue were conducted using two identical MWA systems. Five MWA scenarios were tested at different applicator insertion depths with an ultrasound gel layer applied at the applicator insertion sites: 8 cm insertion depth with and without 4 cm thick gel, 10 cm insertion depth with and without 2 cm thick gel, and 12 cm insertion depth without gel (reference). In all experiments, temperature elevations at the applicator insertion site on the tissue surface were recorded using thermal sensors in all experiments during 10-min MWA. The application of ultrasound gel and increasing applicator insertion depths resulted in measurable reductions in temperature elevations at the applicator insertion sites. For an insertion depth of 8 cm, the temperature elevations were 39.9 ± 4.7°C and 23.2±6.5°C without and with gel, respectively (P < 0.001). For an insertion depth of 10 cm, the temperature elevations were 20.8 ± 1.5°C and 14.4 ± 1.5°C without and with gel, respectively (P < 0.001). The maximal temperature elevations corresponding to an 8 cm insertion depth with gel were comparable with those corresponding to a 10 cm insertion depth without gel. Similarly, the maximal temperature elevations (12.2 ± 1.8°C) corresponding to 12 cm insertion depth without gel were comparable to those corresponding to 10 cm insertion depth with gel. Applying ultrasound gel at the applicator insertion site can significantly reduce temperature elevations at the tissue surface during MWA procedures.

AbstractBackgroundComputed tomography (CT) is routinely used to guide cryoablation procedures. Notably, CT‐guidance provides 3D localization of cryoprobes and can be used to delineate frozen tissue during ablation. However, metal‐induced artifacts from ablation probes can make accurate probe placement challenging and degrade the ice ball conspicuity, which in combination could lead to undertreatment of potentially curable lesions.PurposeIn this work, we propose an image‐based neural network (CNN) model for metal artifact reduction for CT‐guided interventional procedures.MethodsAn image domain metal artifact simulation framework was developed and validated for deep‐learning‐based metal artifact reduction for interventional oncology (MARIO). CT scans were acquired for 19 different cryoablation probe configurations. The probe configurations varied in the number of probes and the relative orientations. A combination of intensity thresholding and masking based on maximum intensity projections (MIPs) was used to segment both the probes only and probes + artifact in each phantom image. Each of the probe and probe + artifact images were then inserted into 19 unique patient exams, in the image domain, to simulate metal artifact appearance for CT‐guided interventional oncology procedures. The resulting 361 pairs of simulated image volumes were partitioned into disjoint training and test datasets of 304 and 57 volumes, respectively. From the training partition, 116 600 image patches with a shape of 128 × 128 × 5 pixels were randomly extracted to be used for training data. The input images consisted of a superposition of the patient and probe + artifact images. The target images consisted of a superposition of the patient and probe only images. This dataset was used to optimize a U‐Net type model. The trained model was then applied to 50 independent, previously unseen CT images obtained during renal cryoablations. Three board‐certified radiologists with experience in CT‐guided ablations performed a blinded review of the MARIO images. A total of 100 images (50 original, 50 MARIO processed) were assessed across different aspects of image quality on a 4‐point likert‐type item. Statistical analyses were performed using Wilcoxon signed‐rank test for paired samples.ResultsReader scores were significantly higher for MARIO processed images compared to the original images across all metrics (all p < 0.001). The average scores of the overall image quality, iceball conspicuity, overall metal artifact, needle tip visualization, target region confidence, and worst metal artifact, needle tip visualization, iceball conspicuity, and target region confidence improved by 34.91%, 36.29%, 39.94%, 34.17%, 35.13%, and 45.70%, respectively.ConclusionsThe proposed method of image‐based metal artifact simulation can be used to train a MARIO algorithm to effectively reduce probe‐related metal artifacts in CT‐guided cryoablation procedures.

AbstractBackgroundThe feasibility of oral dark contrast media is under exploration in abdominal computed tomography (CT) applications. One of the experimental contrast media in this class is dark borosilicate contrast media (DBCM), which has a CT attenuation lower than that of intra‐abdominal fat.PurposeTo evaluate the performances of DBCM using single‐ and multi‐energy CT imaging on a clinical photon‐counting‐detector CT (PCD‐CT).MethodsFive vials, three with iodinated contrast agent (5, 10, and 20 mg/mL; Omnipaque 350) and two with DBCM (6% and 12%; Nextrast, Inc.), and one solid‐water rod (neutral contrast agent) were inserted into two multi‐energy CT phantoms, and scanned on a clinical PCD‐CT system (NAEOTOM Alpha) at 90, 120, 140, Sn100, and Sn140 kV (Sn: tin filter) in multi‐energy mode. CARE keV IQ level was 180 (CTDIvol: 3.0 and 12.0 mGy for the small and large phantoms, respectively). Low‐energy threshold images were reconstructed with a quantitative kernel (Qr40, iterative reconstruction strength 2) and slice thickness/increment of 2.0/2.0 mm. Virtual monoenergetic images (VMIs) were reconstructed from 40 to 140 keV at 10 keV increments. On all images, average CT numbers for each vial/rod were measured using circular region‐of‐interests and averaged over eight slices. The contrast‐to‐noise ratio (CNR) of iodine (5 mg/mL) against DBCM was calculated and plotted against tube potential and VMI energy level, and compared to the CNR of iodine against water. Similar analyses were performed on iodine maps and VNC images derived from the multi‐energy scan at 120 kV.ResultsWith increasing kV or VMI keV, the negative HU of DBCM decreased only slightly, whereas the positive HU of iodine decreased across all contrast concentrations and phantom sizes. CT numbers for DBCM decreased from −178.5 ± 9.6 to −194.4 ± 6.3 HU (small phantom) and from −181.7 ± 15.7 to −192.1 ± 11.9 HU (large phantom) for DBCM‐12% from 90 to Sn140 kV; on VMIs, the CT numbers for DBCM decreased minimally from −147.1 ± 15.7 to −185.1 ± 9.2 HU (small phantom) and −158.8 ± 28.6 to −188.9 ± 14.7 HU (large phantom) from 40 to 70 keV, but remained stable from 80 to 140 keV. The highest iodine CNR against DBCM in low‐energy threshold images was seen at 90 or Sn140 kV for the small phantom, whereas all CNR values from low‐energy threshold images for the large phantom were comparable. The CNR values of iodine against DBCM computed on VMIs were highest at 40 or 70 keV depending on iodine and DBCM concentrations. The CNR values of iodine against DBCM were consistently higher than iodine to water (up to 460% higher dependent on energy level). Further, the CNR of iodine compared to DBCM is less affected by VMI energy level than the identical comparison between iodine and water: CNR values at 140 keV were reduced by 46.6% (small phantom) or 42.6% (large phantom) compared to 40 keV; CNR values for iodine compared to water were reduced by 86.3% and 83.8% for similar phantom sizes, respectively. Compared to 70 keV VMI, the iodine CNR against DBCM was 13%–79% lower on iodine maps and VNC.ConclusionsWhen evaluated at different tube potentials and VMI energy levels using a clinical PCD‐CT system, DBCM showed consistently higher CNR compared to iodine versus water (a neutral contrast).

AbstractBackgroundCoronary calcification is a strong indicator of coronary artery disease, and patients with a “zero” coronary calcification score have a much lower risk of future cardiac events than those with even small amounts of calcium. However, false‐negative (incorrect zero scores) may occur if small calcifications are missed at CT due to limited spatial resolution.PurposeTo demonstrate lower limits of detection for coronary calcification using an ultra‐high‐resolution (UHR) mode on a clinical photon‐counting‐detector CT (PCD‐CT), compared to a conventional energy‐integrating‐detector CT (EID‐CT).MethodsChicken eggshell fragments (0.4–0.8 mm) mimicking coronary calcifications were scanned on a clinical PCD‐CT (NAEOTOM Alpha) in UHR mode and a conventional EID‐CT (SOMATOM Force) with matched tube potential and radiation dose levels to the PCD‐CT. PCD‐CT images were reconstructed with a sharp kernel (Qr68) and a quantum iterative algorithm (QIR‐3). Two sets of EID‐CT images were reconstructed: routine clinical kernel (Qr36, ADMIRE‐3) and a sharper kernel (Qr54) with similar noise to PCD‐CT images. With institutional review board approval, in vivo exams performed with the PCD‐CT in UHR mode were compared against patients’ clinical EID‐CT exams. The visibility of calcifications on PCD‐CT and EID‐CT images was assessed and compared qualitatively.ResultsPCD‐CT images visualized all calcified fragments, while EID‐CT failed to detect those below 0.6 mm using a routine protocol. EID‐CT with Qr54 improved visibility but distorted boundaries. Calcifications were less visible on EID‐CT than PCD‐CT as phantom sizes increased. 0.6‐ and 0.7‐mm calcified fragments were barely visible on 35‐ and 40‐cm phantom EID‐CT images. Patient cases showed small calcifications missed on EID‐CT but detected on PCD‐CT.ConclusionAt matched radiation dose, PCD‐CT in UHR mode provided higher spatial resolution and improved the detectability of small calcified fragments for different phantom/patient sizes in comparison to EID‐CT.

AbstractPurposeTo determine the suitability of a quality assurance (QA) program based on the American College of Radiology's (ACR) CT quality control (QC) manual to fully evaluate the unique capabilities of a clinical photon‐counting‐detector (PCD) CT system.MethodsA daily QA program was established to evaluate CT number accuracy and artifacts for both standard and ultra‐high‐resolution (UHR) scan modes. A complete system performance evaluation was conducted in accordance with the ACR CT QC manual by scanning the CT Accreditation Phantom with routine clinical protocols and reconstructing low‐energy‐threshold (T3D) and virtual monoenergetic images (VMIs) between 40 and 120 keV. Spatial resolution was evaluated by computing the modulation transfer function (MTF) for the UHR mode, and multi‐energy performance was evaluated by scanning a body phantom containing four iodine inserts with concentrations between 2 and 15 mg I/cc.ResultsThe daily QA program identified instances when the detector needed recalibration or replacement. CT number accuracy was impacted by image type: CT numbers at 70 keV VMI were within the acceptable range (defined for 120 kV). Other keV VMIs and the T3D reconstruction had at least one insert with CT number outside the acceptable range. The limiting resolution was nearly 40 lp/cm based on MTF measurements, which far exceeds the 12 lp/cm maximum capability of the ACR phantom. The CT numbers in the iodine inserts were accurate on all VMIs (3.8% average percentage error), while the iodine concentrations had an average root mean squared error of 0.3 mg I/cc.ConclusionProtocols and parameters must be properly selected on PCD‐CT to meet current accreditation requirements with the ACR CT phantom. Use of the 70 keV VMI allowed passing all tests prescribed in the ACR CT manual. Additional evaluations such an MTF measurement and multi‐energy phantom scans are also recommended to comprehensively evaluate PCD‐CT scanner performance.

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.

AbstractPurposeTo demonstrate the feasibility of simultaneous dual‐contrast imaging in a large animal using a newly developed dual‐source energy‐integrating detector (EID)‐based multi‐energy computed tomography (MECT) system.MethodsTwo imaging tasks that may have potential clinical applications were investigated: head/neck (HN) CT angiography (CTA)/CT venography (CTV) with iodine and gadolinium, and small bowel imaging with iodine and bismuth in domestic swine. Dual‐source X‐ray beam configurations of 70 kV + Au120/Sn120 kV and 70 kV + Au140/Sn140 kV were used for the HN‐CTA/CTV and small bowel imaging studies, respectively. A test bolus scan was performed for each study. The regions of interest (ROIs) in the carotid artery and jugular vein for HN‐CTA/CTV imaging and abdominal aorta for small bowel imaging were used to determine the time‐attenuation curves, based on which the timing for contrast injection and the CT scan was determined. In the HN‐CTA/CTV study, an MECT scan was performed at the time point corresponding to the optimal arterial enhancement by iodine and the optimal venous enhancement by gadolinium. In the small bowel imaging study, an MECT scan was performed at the optimal time point to simultaneously capture the mesenteric arterial enhancement of iodine and the enteric enhancement of bismuth. Image‐based material decomposition was performed to decompose different materials for each study. To quantitatively characterize contrast material separation and misclassification, two ROIs on left common carotid artery and left internal jugular vein in HN‐CTA/CTV imaging and three ROIs on superior mesenteric artery, ileal lumen, and collapsed ileum (ileal wall) in small bowel imaging were placed to measure the mean concentration values and the standard deviations.ResultsIn the HN‐CTA/CTV study, common carotid arteries containing iodine and internal/external jugular veins containing gadolinium were clearly delineated from each other. Fine vessels such as cephalic veins and branches of external jugular veins were noticeable but clear visualization was hindered by image noise in gadolinium‐specific (CTV) images, as reviewed by a neuroradiologist. In the small bowel imaging study, the mesenteric arteries and collapsed bowel wall containing iodine and the small bowel loops containing bismuth were clearly distinctive from each other in the iodine‐ and bismuth‐specific images after material decomposition, as reviewed by an abdominal radiologist. Quantitative analyses showed that the misclassifications between the two contrast materials were less than 1.7 and 0.1 mg/ml for CTA/CTV and small bowel imaging studies, respectively.ConclusionsFeasibility of simultaneous CTA/CTV imaging in head and neck with iodine and gadolinium and simultaneous imaging of arterial and enteric phases of small bowel with iodine and bismuth, using a dual‐source EID‐MECT system, was demonstrated in a swine study. Compared to iodine and gadolinium in CTA/CTV, better delineation and classification of iodine and bismuth in small bowel imaging were achieved mainly due to wider separation between the corresponding two K‐edge energies.

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.