Spectral Conventional Computed Tomography Research Articles

Thermometry mapping during CT-guided thermal ablations: proof of feasibility and internal validation using spectral CT

Objective. Conventional computed tomography (CT) imaging does not provide quantitative information on local thermal changes during percutaneous ablative therapy of cancerous and benign tumors, aside from few qualitative, visual cues. In this study, we have investigated changes in CT signal across a wide range of temperatures and two physical phases for two different tissue mimicking materials, each. Approach. A series of experiments were conducted using an anthropomorphic phantom filled with water-based gel and olive oil, respectively. Multiple, clinically used ablation devices were applied to locally cool or heat the phantom material and were arranged in a configuration that produced thermal changes in regions with inconsequential amounts of metal artifact. Eight fiber optic thermal sensors were positioned in the region absent of metal artifact and were used to record local temperatures throughout the experiments. A spectral CT scanner was used to periodically acquire and generate electron density weighted images. Average electron density weighted values in 1 mm3 volumes of interest near the temperature sensors were computed and these data were then used to calculate thermal volumetric expansion coefficients for each material and phase. Main results. The experimentally determined expansion coefficients well-matched existing published values and variations with temperature—maximally differing by 5% of the known value. As a proof of concept, a CT-generated temperature map was produced during a heating time point of the water-based gel phantom, demonstrating the capability to map changes in electron density weighted signal to temperature. Significance. This study has demonstrated that spectral CT can be used to estimate local temperature changes for different materials and phases across temperature ranges produced by thermal ablations.

Detecting bone marrow infiltration in nonosteolytic multiple myeloma through separation of hydroxyapatite via the two-material decomposition technique in spectral computed tomography.

Conventional computed tomography (CT) has low sensitivity for the diagnosis of bone marrow infiltration in nonosteolytic multiple myeloma (NOL-MM). This study aimed to compare the performance of the two-material decomposition technique of spectral CT with the removal of X-ray absorption components of calcium (Ca) versus that of hydroxyapatite (HAP) for diagnosis of NOL-MM. From October 2022 to March 2023, a total of 41 consecutive patients with MM without focal bone lesions undergoing chest spectral CT and thoracic spine magnetic resonance imaging (MRI) in Fujian Medical University Union Hospital were prospectively enrolled; meanwhile, another set of 41 age- and sex-matched healthy consecutive participants were selected as a comparison group. Based on MRI findings, patients with MM were classified with a diffuse infiltration pattern MM (DP-MM) or a normal pattern MM (NP-MM). Regions of interest (ROIs) were manually drawn on vertebrae. CT values of 70-keV images and basic material density within the ROIs were stored. The basic two-material pairs included a Ca-related pair (Ca-X) and an HAP-related pair (HAP-X), with X referring to fat, water, or muscle. Material density values DCa(X), DX(Ca), DHAP(X), and DX(HAP) were each used to diagnose MM, and the area under the receiver operating characteristic curve (AUC) was used to assess diagnostic performance. The 41 patients with NOL-MM included 30 with DP-MM and 11 with NP-MM. CT value, DCa(X), and DHAP(X) were comparable between the NOL-MM, DP-MM, NP-MM, and comparison groups. DX(HAP) was better than DX(Ca) for distinguishing the NOL-MM group from the comparison group {AUC [95% confidence interval (CI)], 0.874 (0.800, 0.949) vs. 0.737 (0.630, 0.844); P=0.02}, the DP-MM group from the comparison group [AUC (95% CI), 0.933 (0.878, 0.989) vs. 0.785 (0.677, 0.894); P=0.01], the NP-MM group from the comparison group [AUC (95% CI), 0.714 (0.540, 0.888) vs. 0.605 (0.429, 0.782); P=0.03], and the DP-MM group from the NP-MM group [AUC (95% CI), 0.809 (0.654, 0.964) vs. 0.736 (0.566, 0.907); P=0.049]. The diagnostic performance of DX(HAP) and DX(Ca) was influenced only by the removed material, while the X material had no influence. The spectral CT two-material decomposition technique with removal of X-ray absorption components of HAP is useful for diagnosis of NOL-MM, irrespective of the paired material.

Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning.

Lymphovascular invasion (LVI) and perineural invasion (PNI) are important prognostic factors for gastric cancer (GC) that indicate an increased risk of metastasis and poor outcomes. Accurate preoperative prediction of LVI/PNI status could help clinicians identify high-risk patients and guide treatment decisions. However, prior models using conventional computed tomography (CT) images to predict LVI or PNI separately have had limited accuracy. Spectral CT provides quantitative enhancement parameters that may better capture tumor invasion. We hypothesized that a predictive model combining clinical and spectral CT parameters would accurately preoperatively predict LVI/PNI status in GC patients. To develop and test a machine learning model that fuses spectral CT parameters and clinical indicators to predict LVI/PNI status accurately. This study used a retrospective dataset involving 257 GC patients (training cohort, n = 172; validation cohort, n = 85). First, several clinical indicators, including serum tumor markers, CT-TN stages and CT-detected extramural vein invasion (CT-EMVI), were extracted, as were quantitative spectral CT parameters from the delineated tumor regions. Next, a two-step feature selection approach using correlation-based methods and information gain ranking inside a 10-fold cross-validation loop was utilized to select informative clinical and spectral CT parameters. A logistic regression (LR)-based nomogram model was subsequently constructed to predict LVI/PNI status, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC). In both the training and validation cohorts, CT T3-4 stage, CT-N positive status, and CT-EMVI positive status are more prevalent in the LVI/PNI-positive group and these differences are statistically significant (P < 0.05). LR analysis of the training group showed preoperative CT-T stage, CT-EMVI, single-energy CT values of 70 keV of venous phase (VP-70 keV), and the ratio of standardized iodine concentration of equilibrium phase (EP-NIC) were independent influencing factors. The AUCs of VP-70 keV and EP-NIC were 0.888 and 0.824, respectively, which were slightly greater than those of CT-T and CT-EMVI (AUC = 0.793, 0.762). The nomogram combining CT-T stage, CT-EMVI, VP-70 keV and EP-NIC yielded AUCs of 0.918 (0.866-0.954) and 0.874 (0.784-0.936) in the training and validation cohorts, which are significantly higher than using each of single independent factors (P < 0.05). The study found that using portal venous and EP spectral CT parameters allows effective preoperative detection of LVI/PNI in GC, with accuracy boosted by integrating clinical markers.

Extracellular volume fraction as a potential predictor to differentiate lung cancer from benign lung lesions with dual-layer detector spectral CT.

Extracellular volume (ECV) fraction has been used in cardiovascular diseases, pancreatic fibrosis, and hepatic fibrosis. The diagnostic value of ECV for focal lung lesions remains to be explored. The aim of this study was to evaluate the feasibility of ECV derived from a dual-layer detector computed tomography (DLCT) to differentiate lung cancer (LC) from benign lung lesions (BLLs). Retrospectively, 128 consecutive patients with pathologically confirmed LC (n=86) or BLLs (n=42) were included. Conventional computed tomography (CT) characteristics and spectral CT parameters were assessed. All patients' hematocrits were measured to correct contrast volume distributions in blood while calculating ECV. After performing logistic regression analysis, a conventional CT-based model (Model A), DLCT-based model (Model B), combined diagnostic models (Model C), and an ECV-based model (Model D) were developed. The diagnostic effectiveness of each model was examined using the receiver operating characteristic (ROC) curve and their corresponding 95% confidence intervals (CIs). The area under the curve (AUC) of each model was compared using the DeLong test. Certain conventional CT features (such as lesion size, lobulation, spiculation, pleural indentation, and enlarged lymph nodes) differed significantly between the LC and BLL groups (all P<0.05). Statistical differences were found in the following DLCT parameters (all P<0.05): effective atomic number (Zeff) (non-enhancement), electron density (ED) (non-enhancement), ECV, iodine concentration (IC), and normalized iodine concentration (NIC). Models A, B, C, and D had AUCs of 0.801 [95% confidence interval (CI): 0.721-0.866], 0.805 (95% CI: 0.726-0.870), 0.925 (95% CI: 0.865-0.964), and 0.754 (95% CI: 0.671-0.826), respectively. The AUC of Model D (ECV) showed no significant difference from that of Models A and B (DeLong test, P>0.05). The ECV derived from DLCT may be a potential new method to differentiate LC from BLLs, broadening the scope of ECV in clinical research.

Application value of double-layer spectral detector CT in differentiating central lung cancer from atelectasis

Central lung cancer with obstructive atelectasis is very common in clinical practice. Determination of the tumor borderline is important. Conventional computed tomography (CT) alone may not be sufficiently accurate to distinguish central lung cancer from obstructive atelectasis. Spectral CT can improve the soft-tissue resolution greatly. In this study, we evaluated the application value of double-layer spectral detector CT in differentiating central lung cancer from atelectasis. A total of 51 patients (37 males) with pathologically confirmed central lung cancer accompanied by atelectasis were enrolled. The rates of differentiation between tumors and atelectasis were retrospectively analyzed using conventional CT and three types of spectral images (40 keV virtual monoenergetic imaging, iodine density map, and their fusion image) of unenhanced scans as well as arterial and venous phases. Cochran's Q test and Friedman test were used to compare the differentiation rates and the maximal diameters of the tumors in each image. Among the 51 cases, conventional CT, 40 keV monoenergetic, iodine density, and their fusion images of the venous phase were successful in differentiating tumors from atelectasis in 17 (33.33%), 35 (68.63%), 39 (76.47%), and 38 (74.51%) cases, respectively. The differentiation rates of the 40 keV monoenergetic, iodine density, and fusion images were significantly higher than those of conventional images (χ2=-0.35, -0.43, and -0.41, respectively, all P<0.001). There were no significant differences in the differentiation rates among the 40 keV monoenergetic, iodine density, and fusion images (χ2=-0.06, -0.08, 0.02, respectively, all P=1.00). The maximal tumor diameters in the four images did not significantly differ (χ2=3.61, P=0.31). Conventional and spectral images of unenhanced and arterial phases could not/barely identify the tumor borderlines. Venous-phase spectral images of double-layer spectral detector CT can differentiate most central lung cancers from atelectasis, and the maximal diameter measurement of the tumor is reliable. Double-layer spectral detector CT can accurately identify the borderlines of most central lung cancers through spectral images during routine CT examinations without requiring other imaging modalities. Therefore, this method has considerable clinical value for applications in tumor staging, efficacy evaluation, and radiotherapy.

Iterative material decomposition for spectral CT using self-supervised Noise2Noise prior

Compared to conventional computed tomography (CT), spectral CT can provide the capability of material decomposition, which can be used in many clinical diagnosis applications. However, the decomposed images can be very noisy due to the dose limit in CT scanning and the noise magnification of the material decomposition process. To alleviate this situation, we proposed an iterative one-step inversion material decomposition algorithm with a Noise2Noise prior. The algorithm estimated material images directly from projection data and used a Noise2Noise prior for denoising. In contrast to supervised deep learning methods, the designed Noise2Noise prior was built based on self-supervised learning and did not need external data for training. In our method, the data consistency term and the Noise2Noise network were alternatively optimized in the iterative framework, respectively, using a separable quadratic surrogate (SQS) and the Adam algorithm. The proposed iterative algorithm was validated and compared to other methods on simulated spectral CT data, preclinical photon-counting CT data and clinical dual-energy CT data. Quantitative analysis showed that our proposed method performs promisingly on noise suppression and structure detail recovery.

Spectral CT imaging method based on blind separation of polychromatic projections with Poisson prior.

The linear reconstruction of narrow-energy-width projections can suppress hardening artifacts in conventional computed tomography (CT). We develop a spectral CT blind separation algorithm for obtaining narrow-energy-width projections under a blind scenario where the incident spectra are unknown. The algorithm relies on an X-ray multispectral forward model. Based on the Poisson statistical properties of measurements, a constrained optimization problem is established and solved by a block coordinate descent algorithm that alternates between nonnegative matrix factorization and Gauss-Newton algorithm. Experiments indicate that the decomposed projections conform to the characteristics of narrow-energy-width projections. The new algorithm improves the accuracy of obtaining narrow-energy-width projections.

Multi‐“Color” Delineation of Bone Microdamages Using Ligand‐Directed Sub‐5 nm Hafnia Nanodots and Photon Counting CT Imaging

AbstractThe early detection of bone microdamages is crucial to make informed decisions about the therapy and taking precautionary treatments to avoid catastrophic fractures. Conventional computed tomography (CT) imaging faces obstacles in detecting bone microdamages due to the strong self‐attenuation of photons from bone and poor spatial resolution. Recent advances in CT technology as well as novel imaging probes can address this problem effectively. Herein, the bone microdamage imaging is demonstrated using ligand‐directed nanoparticles in conjunction with photon counting spectral CT. For the first time, Gram‐scale synthesis of hafnia (HfO2) nanoparticles is reported with surface modification by a chelator moiety. The feasibility of delineating these nanoparticles from bone and soft tissue of muscle is demonstrated with photon counting spectral CT equipped with advanced detector technology. The ex vivo and in vivo studies point to the accumulation of hafnia nanoparticles at microdamage site featuring distinct spectral signal. Due to their small sub‐5 nm size, hafnia nanoparticles are excreted through reticuloendothelial system organs without noticeable aggregation while not triggering any adverse side effects based on histological and liver enzyme function assessments. These preclinical studies highlight the potential of HfO2‐based nanoparticle contrast agents for skeletal system diseases due to their well‐placed K‐edge binding energy.

Role for imaging in spondyloarthritis.

Despite major progress in the imaging diagnosis of spondyloarthritis (SpA), the relative advantages of various available imaging techniques remain unclear. The aim of this study is to assess the current use of imaging in the diagnosis of SpA and to provide suitable recommendations for the use of imaging as an outcome measure as defined in the Assessment in SpondyloArthritis international Society (ASAS) criteria. A systematic literature search regarding imaging in SpA was performed. Articles were assessed by two reviewers to identify and summarized key information pertaining to imaging in SpA. The search identified 180 relevant articles. Conventional radiography (CR) (17 articles), ultrasound (US) (26 articles), conventional computed tomography (CT) (13 articles), spectral computed tomography (spectral CT) (2 articles), bone scintigraphy (24 articles), and magnetic resonance imaging (MRI) were assessed (98 articles). Sacroiliitis and enthesitis were the major imaging findings in SpA. Multiple studies assessed the feasibility, validity, or differences among imaging modalities for the diagnosis of SpA; however, comprehensive assessments were not available due to a paucity of prospective imaging studies. CR is a widely available, inexpensive initial approach to evaluate patients with suspected SpA. CT enables assessment of structural changes from chronic sacroiliitis including bony erosions, subchondral sclerosis, joint space narrowing, and ankyloses; however, both CR and CT modalities are insensitive for demonstrating early enthesitis and sacroiliitis in SpA. US mainly identifies appendicular enthesitis but is more limited with respect to the sacroiliac joints. Bone scintigraphy can identify sacroiliac joint lesions and semi-quantitatively assess active sacroiliitis. MRI optimally evaluates not only early enthesitis and sacroiliitis of SpA but also chronic structural changes to the sacroiliac joints. More than one modality may be required for diagnostic and assessment of SpA depending upon disease characteristics and evolution. CR is a suitable initial examination while MRI is able to detect both early and late changes of SpA. A combination of CR and MRI is recommended for the diagnosis and assessment of SpA.

Material reconstruction for spectral computed tomography with detector response function

Different from conventional computed tomography (CT), spectral CT using energy-resolved photon-counting detectors is able to provide the unprecedented material compositions. However accurate spectral CT needs to account for the detector response function (DRF), which is often distorted by factors such as pulse pileup and charge-sharing. In this work, we propose material reconstruction methods for spectral CT with DRF. The simulation results suggest that the proposed methods reconstructed more accurate material compositions than the conventional method without DRF. Moreover, the proposed linearized method with linear data fidelity from spectral resampling had improved reconstruction quality from the nonlinear method directly based on nonlinear data fidelity.

Numerical observer for atherosclerotic plaque classification in spectral computed tomography.

Spectral computed tomography (SCT) generates better image quality than conventional computed tomography (CT). It has overcome several limitations for imaging atherosclerotic plaque. However, the literature evaluating the performance of SCT based on objective image assessment is very limited for the task of discriminating plaques. We developed a numerical-observer method and used it to assess performance on discrimination vulnerable-plaque features and compared the performance among multienergy CT (MECT), dual-energy CT (DECT), and conventional CT methods. Our numerical observer was designed to incorporate all spectral information and comprised two-processing stages. First, each energy-window domain was preprocessed by a set of localized channelized Hotelling observers (CHO). In this step, the spectral image in each energy bin was decorrelated using localized prewhitening and matched filtering with a set of Laguerre-Gaussian channel functions. Second, the series of the intermediate scores computed from all the CHOs were integrated by a Hotelling observer with an additional prewhitening and matched filter. The overall signal-to-noise ratio (SNR) and the area under the receiver operating characteristic curve (AUC) were obtained, yielding an overall discrimination performance metric. The performance of our new observer was evaluated for the particular binary classification task of differentiating between alternative plaque characterizations in carotid arteries. A clinically realistic model of signal variability was also included in our simulation of the discrimination tasks. The inclusion of signal variation is a key to applying the proposed observer method to spectral CT data. Hence, the task-based approaches based on the signal-known-exactly/background-known-exactly (SKE/BKE) framework and the clinical-relevant signal-known-statistically/background-known-exactly (SKS/BKE) framework were applied for analytical computation of figures of merit (FOM). Simulated data of a carotid-atherosclerosis patient were used to validate our methods. We used an extended cardiac-torso anthropomorphic digital phantom and three simulated plaque types (i.e., calcified plaque, fatty-mixed plaque, and iodine-mixed blood). The images were reconstructed using a standard filtered backprojection (FBP) algorithm for all the acquisition methods and were applied to perform two different discrimination tasks of: (1)calcified plaque versus fatty-mixed plaque and (2)calcified plaque versus iodine-mixed blood. MECT outperformed DECT and conventional CT systems for all cases of the SKE/BKE and SKS/BKE tasks (all [Formula: see text]). On average of signal variability, MECT yielded the SNR improvements over other acquisition methods in the range of 46.8% to 65.3% (all [Formula: see text]) for FBP-Ramp images and 53.2% to 67.7% (all [Formula: see text]) for FBP-Hanning images for both identification tasks. This proposed numerical observer combined with our signal variability framework is promising for assessing material characterization obtained through the additional energy-dependent attenuation information of SCT. These methods can be further extended to other clinical tasks such as kidney or urinary stone identification applications.

WE‐FG‐207B‐02: Material Reconstruction for Spectral Computed Tomography with Detector Response Function

Purpose:Different from the conventional computed tomography (CT), spectral CT based on energy‐resolved photon‐counting detectors is able to provide the unprecedented material composition. However, an important missing piece for accurate spectral CT is to incorporate the detector response function (DRF), which is distorted by factors such as pulse pileup and charge‐sharing. In this work, we propose material reconstruction methods for spectral CT with DRF.Methods:The polyenergetic X‐ray forward model takes the DRF into account for accurate material reconstruction. Two image reconstruction methods are proposed: a direct method based on the nonlinear data fidelity from DRF‐based forward model; a linear‐data‐fidelity based method that relies on the spectral rebinning so that the corresponding DRF matrix is invertible. Then the image reconstruction problem is regularized with the isotropic TV term and solved by alternating direction method of multipliers.Results:The simulation results suggest that the proposed methods provided more accurate material compositions than the standard method without DRF. Moreover, the proposed method with linear data fidelity had improved reconstruction quality from the proposed method with nonlinear data fidelity.Conclusion:We have proposed material reconstruction methods for spectral CT with DRF, whichprovided more accurate material compositions than the standard methods without DRF. Moreover, the proposed method with linear data fidelity had improved reconstruction quality from the proposed method with nonlinear data fidelity.Jiulong Liu and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000), and the Shanghai Pujiang Talent Program (#14PJ1404500).