Ren, Liqiang

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.

PurposeTo evaluate the quantitative accuracy and dose efficiency of simultaneous imaging of two contrast agents using dual‐energy computed tomography (DECT), two imaging tasks each representing one potential clinical application were investigated in a phantom study: biphasic liver imaging with iodine and gadolinium, and small bowel imaging with iodine and bismuth.MethodsTo separate and quantify mixtures of two contrast agents using a single DECT scan, mixed iodine and gadolinium samples were prepared with the contrast enhancement values corresponding to the late arterial (iodine) and the portal‐venous (gadolinium) phase for biphasic liver imaging. Mixed iodine and bismuth samples were prepared mimicking the arterial (iodine) and the enteric (bismuth) enhancement for small bowel imaging. For comparison to the reference condition of performing two single‐energy CT (SECT) scans, contrast samples were prepared separately to mimic separate scans in the arterial/venous phase and arterial/enteric enhancement. Samples were placed in a 35 cm wide water tank and scanned using a third‐generation dual‐source DECT scanner with three tube potential pairs: 80/Sn150, 90/Sn150, and 100/Sn150 kV, all with default dose partitioning between two x‐ray beams to acquire DECT data. The same scanner operated in a single‐energy mode acquired SECT data (120 kV). Total radiation dose (CTDIvol) was matched for the single‐scan DECT and the two‐scan SECT protocols. The DECT protocol was followed by a generic image‐based three‐material decomposition method to determine the material‐specific images, based on which concentrations of each basis material were quantified and noise levels were measured. To compare with the SECT images directly acquired with the SECT protocol, the concentration values in each contrast‐specific image were converted to CT numbers at 120 kV (i.e., virtual SECT (vSECT) images). The noise level and noise power spectra differences between the SECT and vSECT images were compared to evaluate the dose efficiency of the single‐scan DECT protocol. The impact of dose partitioning in the DECT protocol on quantitative dual‐contrast imaging performance was also studied.ResultsFor each imaging task, contrast materials were accurately quantified against the nominal concentrations using the DECT data with strong correlation (R2 ≥ 0.98 for both imaging tasks). Compared to the SECT protocol, the DECT protocol was not dose efficient. With the optimal x‐ray tube potential pair 80/Sn150 kV, the noise level in vSECT images increased by 401%/488% (arterial/portal‐venous) for the biphasic liver imaging task and by 10%/41% (arterial/enteric) for the small bowel imaging task compared to that in SECT images. The corresponding radiation dose increase is 2410%/3357% for the biphasic liver imaging task and 21%/99% for the small bowel imaging task, respectively, to achieve the same noise as that in SECT images. This could be improved by adjusting the dose partitioning in DECT.ConclusionsDECT can be used to simultaneously separate and quantify two contrast materials. However, compared to a two‐scan SECT protocol, much higher radiation dose is needed in a single‐scan DECT protocol to achieve the same image noise, especially for tasks involving the dual contrast of iodine and gadolinium.

PurposeThis work aims to develop a new framework of image quality assessment using deep learning‐based model observer (DL‐MO) and to validate it in a low‐contrast lesion detection task that involves CT images with patient anatomical background.MethodsThe DL‐MO was developed using the transfer learning strategy to incorporate a pretrained deep convolutional neural network (CNN), a partial least square regression discriminant analysis (PLS‐DA) model and an internal noise component. The CNN was previously trained to achieve the state‐of‐the‐art classification accuracy over a natural image database. The earlier layers of the CNN were used as a deep feature extractor, with the assumption that similarity exists between the CNN and the human visual system. The PLSR model was used to further engineer the deep feature for the lesion detection task in CT images. The internal noise component was incorporated to model the inefficiency and variability of human observer (HO) performance, and to generate the ultimate DL‐MO test statistics. Seven abdominal CT exams were retrospectively collected from the same type of CT scanners. To compare DL‐MO with HO, 12 experimental conditions with varying lesion size, lesion contrast, radiation dose, and reconstruction types were generated, each condition with 154 trials. CT images of a real liver metastatic lesion were numerically modified to generate lesion models with four lesion sizes (5, 7, 9, and 11 mm) and three contrast levels (15, 20, and 25 HU). The lesions were inserted into patient liver images using a projection‐based method. A validated noise insertion tool was used to synthesize CT exams with 50% and 25% of routine radiation dose level. CT images were reconstructed using the weighted filtered back projection algorithm and an iterative reconstruction algorithm. Four medical physicists performed a two‐alternative forced choice (2AFC) detection task (with multislice scrolling viewing mode) on patient images across the 12 experimental conditions. DL‐MO was operated on the same datasets. Statistical analyses were performed to evaluate the correlation and agreement between DL‐MO and HO.ResultsA statistically significant positive correlation was observed between DL‐MO and HO for the 2AFC low‐contrast detection task that involves patient liver background. The corresponding Pearson product moment correlation coefficient was 0.986 [95% confidence interval (0.950, 0.996)]. Bland–Altman agreement analysis did not indicate statistically significant differences.ConclusionsThe proposed DL‐MO is highly correlated with HO in a low‐contrast detection task that involves realistic patient liver background. This study demonstrated the potential of the proposed DL‐MO to assess image quality directly based on patient images in realistic, clinically relevant CT tasks.