SPECT Research Articles

Radiomics analysis of dual-layer detector spectral CT-derived iodine maps for predicting Ki-67 PI in pancreatic ductal adenocarcinoma

ObjectiveTo evaluate the feasibility of radiomics analysis using dual-layer detector spectral CT (DLCT)-derived iodine maps for the preoperative prediction of the Ki-67 proliferation index (PI) in pancreatic ductal adenocarcinoma (PDAC).Materials and methodsA total of 168 PDAC patients who underwent DLCT examination were included and randomly allocated to the training (n = 118) and validation (n = 50) sets. A clinical model was constructed using independent clinicoradiological features identified through multivariate logistic regression analysis in the training set. The radiomics signature was generated based on the coefficients of selected features from iodine maps in the arterial and portal venous phases of DLCT. Finally, a radiomics-clinical model was developed by integrating the radiomics signature and significant clinicoradiological features. The predictive performance of three models was evaluated using the Receiver Operating Characteristic (ROC) curve and Decision Curve Analysis. The optimal model was then used to develop a nomogram, with goodness-of-fit evaluated through the calibration curve.ResultsThe radiomics-clinical model integrating radiomics signature, CA19-9, and CT-reported regional lymph node status demonstrated excellent performance in predicting Ki-67 PI in PDAC, which showed an area under the ROC curve of 0.979 and 0.956 in the training and validation sets, respectively. The radiomics-clinical nomogram demonstrated the improved net benefit and exhibited satisfactory consistency.ConclusionsThis exploratory study demonstrated the feasibility of using DLCT-derived iodine map-based radiomics to predict Ki-67 PI preoperatively in PDAC patients. While preliminary, our findings highlight the potential of functional imaging combined with radiomics for personalized treatment planning.

Current and future role of CT and advanced CT applications in inflammatory arthritis in the clinic and trials.

Computed tomography (CT) has traditionally been underutilized in the imaging of inflammatory arthritis due to its limitations in assessing soft tissue inflammation and concerns over radiation exposure. However, recent technological advancements have positioned CT as a more viable imaging modality for arthritis, offering high specificity and sensitivity in detecting structural bone changes. However, advances in ultra-low-dose CT protocols and AI-driven image reconstruction have significantly reduced radiation exposure while maintaining diagnostic quality. Dynamic CT and spectral CT techniques, including dual-energy CT (DECT), have broadened CT's application in assessing dynamic joint instabilities and visualizing inflammatory changes through material-specific imaging. Techniques such as CT subtraction imaging and iodine mapping have enhanced the detection of active soft-tissue inflammation, virtual non-calcium reconstructions, and the detection of bone marrow edema. Possible CT applications span various forms of arthritis, including gout, calcium pyrophosphate deposition disease (CPPD), psoriatic arthritis, and axial spondyloarthritis. Beyond its diagnostic capabilities, CT's ability to provide detailed structural assessment positions is a valuable tool for monitoring disease progression and therapeutic response, particularly in clinical trials. While MRI remains superior for soft tissue evaluation, CT's specificity for bone-related changes and its potential for integration into routine arthritis management warrant further exploration and research. This review explores the current and emerging roles of CT in arthritis diagnostics, with a focus on novel applications and future potential.

A Three-Material Model for Dual-Layer Detector Spectral CT Measurements of Marrow Adipose Tissue and Bone Mineral Density

A Three-Material Model for Dual-Layer Detector Spectral CT Measurements of Marrow Adipose Tissue and Bone Mineral Density

Photon-Counting Computed Tomography-Based Hepatic iron Quantification Using a Tungsten-Based Contrast Agent.

This study explores the potential of quantifying hepatic iron in computed tomography (CT) scans in the presence of iodine (I)- or tungsten (W)-based contrast media (CM). Experimental work was performed on a commercial photon-counting CT system able to simultaneously acquire up to 4 spectral data sets in a single scan. We examined 2 anthropomorphic abdominal phantoms with material samples of liquid liver tissue surrogate, fat, iron, and I- or W-based CM to mimic different liver compositions in an enhanced CT scan. Iron was quantified by material decomposition of reconstructed spectral CT images. Two-material decomposition based on 2 spectral data sets provided material images of iron and liver with an accuracy of 1.4 mg/mL in the iron image of CM-free samples. The presence of W affected the iron quantification: For 2 and 4 mgW/mL in the material samples, the iron concentration was overestimated (P < 0.05) with accuracies of 2.7 and 4.7 mg/mL, respectively. Three-material decomposition based on 4 spectral data sets provided material images of iron, liver, and W, with an accuracy of 1.4 mg/mL in the images without W and 1.5 (nonsignificant difference, P > 0.07) and 1.6 mg/mL (overestimation, P > 0.03) in the iron image at 2 and 4 mgW/mL, respectively. The presence of I affected the iron quantification more than W in both 2- and 3-material decomposition: For 2 and 4 mgI/mL in the material samples, the measured iron concentration was even higher (P < 0.05), with accuracies >18 and >37 mg/mL, respectively. The accuracy of iron quantification from a 3-material decomposition suggested clinically feasible detection and quantification of critical hepatic iron levels in enhanced CT scans with suitable CM. In a 2-material decomposition, severe pathology is required to detect an iron liver. W-based CM was superior to I-based CM.

Penumbra-effect induced spectral mixing in x-ray computed tomography: a multi-ray spectrum estimation model and subsampled weighting algorithm

Objective.With the development of spectral CT, various spectral imaging technologies have been proposed. Among these, filter-based spectral imaging methods have been greatly advanced in recent years, such as split filters used in clinical diagnose, spectral modulators studied for spectral imaging and scatter correction. However, due to the finite size of the focal spot of x-ray source, spectral filters cause spectral mixing in the penumbra region. Traditional spectrum estimation methods fail to account for it, resulting in reduced spectral accuracy. To address this challenge, we develop a multi-ray spectrum estimation model and propose an Adaptive Subsampled WeIghting of Filter Thickness (A-SWIFT) method.Approach.First, we estimate the unfiltered spectrum using traditional methods. Next, we model the final spectra as a weighted summation of spectra attenuated by multiple filters. The weights and equivalent lengths are obtained by x-ray transmission measurements taken with altered spectra using different kVp or flat filters. Finally, the spectra are approximated by using the multi-ray model. To mimic the penumbra effect, we used a spectral modulator (0.2 mm Mo, 0.6 mm Mo), a split filter (0.07 mm Au, 0.7 mm Sn), and the abdominal images of an XCAT phantom in simulations; in experiments, we used spectral modulators made by molybdenum or copper along with a pure water phantom, a Gammex multi-energy CT phantom and a Kyoto chest phantom for validation.Main results.Simulation results show that the mean energy bias in the penumbra region decreased from 7.43 keV using the previous Spectral Compensation for Modulator (SCFM) method to 0.72 keV using the A-SWIFT method for the split filter, and from 1.98 keV to 0.61 keV for the spectral modulator. In physics experiments, for the pure water phantom with a molybdenum modulator, the average error of the mean values (ERMSE) in selected regions of interests decreased from 77 to 7 Hounsfield units (HU) using the A-SWIFT method compared with SCFM method; for the Gammex phantom,ERMSEin iodine images was 0.2 mg ml-1using A-SWIFT method, and 1.5 mg ml-1using SCFM method; for the chest phantom with an added 5 mg ml-1iodine cylinder, the estimated material density of the iodine inserts was 5.0 mg ml-1using A-SWIFT method, and 5.9 mg ml-1using SCFM method.Significance.Based on a multi-ray spectrum estimation model, the A-SWIFT method provides an accurate and robust spectrum estimation in the penumbra region, contributing to enhanced spectral imaging performance of CT systems utilizing spectral filters.

Quantitative CT imaging: Where are we, and what is missing?

Quantitative computed tomography (CT) enables the measurement of biophysical processes characterized by morphology, composition, flow and/or motion to aid in clinical diagnosis and intervention. Since its initial application for determining bone mineral density for skeletal fragility assessment, quantitative CT has continued to evolve alongside CT's technological advancements. A key challenge facing quantitative CT is the lack of standardization pertinent to image acquisition, reconstruction and image analysis. With the introduction of spectral CT involving dual-energy approaches and photon-counting detectors (PCD), we are now able to obtain detailed information regarding mass densities of endogenous and exogeneous materials. Further, energy-resolved CT yields spectral image types (e.g. virtual monoenergetic image) that are, in principle, independent of tube potential and patient size. This paves the way for workflow standardization and to improve the consistency and reproducibility of CT-derived measurements. In this article, we review clinical applications of quantitative CT, discuss key challenges associated with quantitative CT and its adoption into routine practice, and outline the unique benefits ushered by new CT technologies such as PCD-CT towards improving quantitative imaging.

Incremental predictive value of liver fat fraction based on spectral detector CT for major adverse cardiovascular events in T2DM patients with suspected coronary artery disease

BackgroundThe purpose of this study was to explore the incremental predictive value of liver fat fraction (LFF) in forecasting major adverse cardiovascular events (MACE) among patients with type 2 diabetes mellitus (T2DM).MethodsWe prospectively enrolled 265 patients with T2DM who presented to our hospital with symptoms of chest distress and pain suggestive of coronary artery disease (CAD) between August 2021 and August 2022. All participants underwent both coronary computed tomography angiography (CCTA) and upper abdominal dual-layer spectral detector computed tomography (SDCT) examinations within a 7-day interval. Detailed clinical data, CCTA imaging features, and LFF determined by SDCT multi-material decomposition algorithm were meticulously recorded. MACE was defined as the occurrence of cardiac death, acute coronary syndrome (ACS), late-phase coronary revascularization procedures, and hospital admissions due to heart failure.ResultsAmong 265 patients (41% male), 51 cases of MACE were documented during a median follow-up of 30 months. The LFF in T2DM patients who experienced MACE was notably higher compared to those without MACE (p < 0.001). The LFF was divided into tertiles using the cutoffs of 4.10 and 8.30. Kaplan-Meier analysis indicated that patients with higher LFF were more likely to develop MACE, regardless of different subgroups in framingham risk score (FRS) or coronary artery calcium score (CACS). The multivariate Cox regression results indicated that, compared with patients in the lowest tertile, those in the second tertile (hazard ratio [HR] = 3.161, 95% confidence interval [CI] 1.163–8.593, P = 0.024) and third tertile (HR = 4.372, 95% CI 1.591–12.014, P = 0.004) had a significantly higher risk of MACE in patients with T2DM. Even after adjusting for early revascularization, both LFF tertile and CACS remained independently associated with MACE. Moreover, compared with the traditional FRS model, the model that included LFF, CACS, and FRS showed stable clinical net benefit and demonstrated better predictive performance, with a C-index of 0.725, a net reclassification improvement (NRI) of 0.397 (95% CI 0.187–0.528, P < 0.01), and an integrated discrimination improvement (IDI) of 0.100 (95% CI 0.043–0.190, P < 0.01).ConclusionsThe elevated LFF emerged as an independent prognostic factor for MACE in patients with T2DM. Incorporating LFF with FRS and CACS provided incremental predictive power for MACE in patients with T2DM.Graphical abstractResearch insightsWhat is currently known about this topic?T2DM is associated with increased MACE rates, underscoring the need for improved risk prediction. CACS is a well-established tool for MACE risk assessment but may not capture all risk factors. Hepatic steatosis is a common comorbidity in metabolic syndrome and T2DM.What is the key research question?Does the incorporation of LFF derived from SDCT into existing risk prediction models enhance the accuracy of MACE forecasting in patients with T2DM?What is new?SDCT-LFF measurement introduces a more accurate method for assessing hepatic steatosis. LFF as an independent predictor of MACE in T2DM patients is a novel finding. The study presents LFF as an additional tool for risk stratification, complementing FRS and CACS.How might this study influence clinical practice?Study findings may guide personalized prevention for T2DM patients at higher MACE risk.

Spectral computed tomography parameters for predicting vessels encapsulating tumor clusters (VETC) pattern in hepatocellular carcinoma: a pilot study.

Vessels encapsulating tumor clusters (VETC) is a novel microvascular pattern associated with poor prognosis in patients with hepatocellular carcinoma (HCC). Reliable preoperative predictors of VETC may substantially improve prognostic outcomes. This study aimed to evaluate the predictive value of multiparameter spectral computed tomography (CT) in identifying VETC pattern in HCC. This retrospective analysis included 50 patients with histopathologically confirmed HCC who underwent preoperative abdominal spectral CT and CD34 immunohistochemical staining. VETC(+) was defined as a visible vessel-encapsulating tumor cluster occupying ≥5% of the tumor area. Patients were divided into VETC(+) (n=21) and VETC(-) (n=29) groups. Eight qualitative imaging features, including tumor size, intratumor vascularity, nonrim arterial phase (AP) hyperenhancement, nonperipheral portal phase (PP) washout, well-defined capsule, nonsmooth tumor margin, intratumoral necrosis, and AP hypovascular component, were assessed via 40-keV virtual monoenergetic images (VMIs). Quantitative spectral parameters, including iodine concentration (IC), normalized IC (NIC), effective atomic number (Zeff), and energy spectrum curve slope (λ), were measured in both the PP and equilibrium phase (EP). Multivariate logistic regression analysis was performed to identify independent risk factors for VETC pattern, receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance, and the Kaplan-Meier method was used to assess recurrence-free survival (RFS). Significant differences were found between the VETC(+) and VETC(-) groups in alpha-fetoprotein level, intratumor AP hypovascular component (IAPHC), and portal- and equilibrium-phase spectral parameters (IC, Zeff, and λ; all P values <0.05). Multivariate logistic regression identified the IAPHC and EP IC as independent VETC predictors of VETC patter [odds ratio (OR) =4.149 and OR =12.724, respectively]. The combined model demonstrated superior diagnostic performance (AUC =0.810) compared to the individual parameters (IAPHC: AUC =0.672; EP IC: AUC =0.766), achieving a 66.67% sensitivity and an 89.66% specificity. Kaplan-Meier survival analysis indicated a shorter RFS in the VETC(+) group than in the VETC(-) group (P=0.02). IAPHC and EP IC derived from spectral CT hold significant potential for VETC prediction in HCC. The application of a combined model enhances diagnostic efficiency.

Impact of clinical factors on reproducibility of dopamine transporter imaging: A 99mTc-TRODAT SPECT study with sufficient size.

Impact of clinical factors on reproducibility of dopamine transporter imaging: A 99mTc-TRODAT SPECT study with sufficient size.

Multimodal Neuroimaging as a SUDEP Predictor: What Is Known and What Still Needs to Be Uncovered?

Sudden Unexpected Death in Epilepsy (SUDEP) is the most common cause of death in patients with poorly controlled epilepsy. To date, a higher risk of developing SUDEP is mainly identified by clinical factors, among which generalized tonic-clonic seizures and their frequency stand out as part of the highly debated SUDEP-7 Scoring. This review investigates the role of neuroimaging-based approaches as a tool to help predict SUDEP. We carried out a systematic search of the literature to identify multimodal neuroimaging modifications (i.e., MRI, fMRI, PET, and SPECT) in patients with epilepsy who died from SUDEP. The following databases were used: PubMed and Google Scholar. The review was registered on the PROSPERO platform (Registration code: CRD42024558765). Fifteen articles were selected, investigating 104 SUDEP cases compared with 792 non-SUDEP epileptic patients and 280 healthy controls (HC) (overall mean age 33.9 ± 1.6). Results suggest that SUDEP and non-SUDEP cases and HC differ anatomically and functionally. In the SUDEP population, MRI data indicate relevant volume differences in the gray matter of the hippocampus and cerebellar cortices. In addition, functional imaging reveals discrepancies in network modulation within the brainstem and its relationship with several cortical structures. Although less consistent, PET and SPECT scan data point toward alterations in metabolism and perfusion in the frontal and brainstem areas. The reviewed data support correlations between the occurrence of SUDEP and neuroimaging multimodal alterations that could be relevant in death prediction.

Extracellular volume quantified by spectral CT to assess vessels encapsulating tumor clusters (VETC) pattern in hepatocellular carcinoma: A pilot study

Extracellular volume quantified by spectral CT to assess vessels encapsulating tumor clusters (VETC) pattern in hepatocellular carcinoma: A pilot study

Construction of a postoperative liver metastasis prediction model for colorectal cancer based on spectral CT imaging, CEA, and CA19-9.

Most existing models for predicting liver metastasis primarily rely on single clinical indicators or traditional imaging features, which, while useful, offer limited accuracy and reliability. In recent years, spectral computed tomography (CT) has emerged as a dual-energy imaging technology that provides detailed quantitative analyses of the blood supply characteristics and metabolic activity of tumors. The integration of serum biomarkers such as carcinoembryonic antigen (CEA) and cancer antigen 19-9 (CA19-9) with spectral CT features holds great potential for significantly enhancing the accuracy of liver metastasis predictions. This study aims to develops a novel nomogram prediction model based on spectral CT, CEA, and CA19-9 to predict the risk of liver metastasis after colorectal cancer (CRC) surgery. This study recruited 100 patients diagnosed with CRC and receiving initial treatment at Jiangsu Cancer Hospital between June 2020 and June 2022. All patients underwent preoperative spectral CT examination. Patients were categorized into two groups based on the occurrence of liver metastasis within two years post-surgery: the liver metastasis group (n=60) and the non-metastasis group (n=40). A comparison was made between the two groups regarding the clinical and pathological characteristics and the changes in spectral CT parameters of the primary lesion before surgery. The predictive efficacy of preoperative spectral CT parameters of the primary lesion for liver metastasis was assessed. The risk factors for liver metastasis following CRC surgery were determined using multivariable logistic regression analysis, and a nomogram prediction model was established. A 7:3 ratio was used to randomly divide the dataset into a training set (n=70) and a validation set (n=30). The model's performance was evaluated using the receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA). Following CRC surgery, liver metastasis was found to be independently associated with CEA, cancer antigen 19-9 (CA19-9), and preoperative spectral CT characteristics of the original lesion during the venous phase, including lesion iodine concentration (IClesion), spectral slope in Hounsfield units (λHU), and the CT values of the tumor lesions on the 40-keV (CT40 keV). The nomogram developed from these predictors demonstrated high discriminative ability, with area under the curve (AUC) of 0.9078 [95% confidence interval (CI): 0.8419-0.9738] in the training cohort and 0.9502 (95% CI: 0.8792-1.0000) in the internal validation cohort at the optimal cutoff of 0.6460. The calibration curve showed that the observed and expected values agreed well. According to DCA, the nomogram model had good clinical value. The nomogram model constructed based on spectral CT parameters, CEA, and CA19-9 demonstrates potential in predicting postoperative liver metastasis in CRC, providing a reference for preoperative personalized treatment. However, its generalizability needs to be further confirmed through multi-center external validation.

Imaging Cell Surface Plectin in PDAC Patients - A First-In-Human Phase 0 Study Report.

Imaging Cell Surface Plectin in PDAC Patients - A First-In-Human Phase 0 Study Report.

A statistical method for predicting amyloid-β deposits from severity, extend, and ratio indices of the 99mTc-ECD SPECT.

BackgroundAmyloid-β (Aβ) deposit prediction accuracy is necessary for clinicians treating patients desiring Alzheimer's disease (AD) modifying drugs. Aβ-PET imaging is useful for diagnosis, but high in cost compared to brain perfusion SPECT. However, SPECT displays regional cerebral blood flow (rCBF) and does not detect Aβ deposits, therefore requiring additional clinical information.ObjectiveThis article describes a novel statistical method to predict amyloid deposits via PET images (Aβ+ or Aβ-) using the three indices of the 99mTc-ECD SPECT - severity, extend, and ratio - for the three ROIs.MethodsCandidate patients (N = 114 patients [55% male], 81 Aβ+ 33 Aβ-, mean age 74.2 ± 6.6 years, mean MMSE score 23.7 ± 2.8) underwent MRI and 99mTc-ECD SPECT scanning. After examining SPECT index, demographic, and age data, age and sex were treated as confounders in one, two, and three-index logistic additive models with severity, extend, and ratio as explanatory variables. Area under curve (AUC), sensitivity and specificity were used as statistical indices for model fitness and accuracies. Three-hold cross validation analyses were conducted to evaluate error rates.ResultsAccording to ROC analysis, best scores for fitness and accuracy were obtained from the three-index model with patients' age and sex for the configured ROIs including precuneus, posterior cingulate and temporal-parietal region of SPECT (AUC: 0.818, Sensitivity: 0.803, Specificity: 0.727).ConclusionsThis technique using 99mTc-ECD SPECT data can predict amyloid deposits with acceptable accuracy. To confirm the reliability and validity, a multicenter SPECT study is needed.

Intestinal fibrosis assessment in Crohn’s disease patient using unenhanced spectral CT combined with 3D-printing technique

ObjectivesTo integrate multiple parameters derived from unenhanced spectral CT with 3D-printing technique to accurately evaluate intestinal lesions in patients with Crohn’s disease (CD).MethodsPatients with proven CD who underwent preoperative spectral CT and surgery were included. The spectral CT images and histopathological specimens were achieved by employing 3D-printing technique. Diagnostic models were developed utilizing Z-Effective, Electron Density (ED), and Hounsfield unit (HU) values derived from spectral CT, along with spectral curve slopes λ1 and λ2, as well as ΔHUMonoE. The area under the receiver operating characteristic curve (AUC) and the influence of inflammation on the efficacy of the models were analyzed.ResultsThe ED and HU at MonoE 50 keV of the spectral CT were determined to exhibit the highest correlation with the fibrosis degree of the diseased intestine. The training dataset yielded an AUC of 0.828 (95% CI: 0.705–0.951). The sensitivity and specificity were calculated to be 77.3% and 82.6%, respectively. The AUC of the validation set was 0.812 (95% CI: 0.676–0.948) with a sensitivity of 63.6% and specificity of 89.7%. Moreover, our model demonstrated enhanced diagnostic accuracy for detecting fibrosis with an AUC value of 0.933 (95% CI: 0.856–1.000), sensitivity of 90.9%, and specificity of 87.0%, after regulating the influence of inflammation.ConclusionThe integration of unenhanced multi-parametric spectral CT and 3D-printing technique seems to be able to assess the intestinal fibrosis. Our diagnostic model remains effective in assessing the severity of fibrosis under presence of inflammation.Critical relevance statementOur diagnostic model accurately assessed the degree of intestinal wall fibrosis in Crohn’s disease patients by using unenhanced spectral CT and 3D-printing technique, which could facilitate individualized treatment.Key PointsEvaluating the extent of Crohn’s disease-related fibrosis is important.The combination of 3D-printing technique and multi-parametric spectral CT enhances diagnostic accuracy.The developed model using spectral CT allows for the assessment of intestinal fibrosis using multi-parameters.Graphical

Multiparametric gadoxetic acid-enhanced MR versus dual-layer spectral detector CT for differentiating hepatocellular carcinoma from hypervascular pseudolesions.

BackgroundIt can be difficult to differentiate hypervascular hepatocellular carcinoma (HCC) from hypervascular pseudolesion (HPL) such as arteriovenous shunts.PurposeTo determine retrospectively whether double-layer detector computed tomography (DLCT) can differentiate HCC from HPL compared to gadoxetate-enhanced magnetic resonance imaging (EOB-MRI).Material and MethodsWe retrospectively analyzed 46 patients who underwent EOB-MRI and DLCT for suspected HCCs. Arterial/portal phase and hepatobiliary phase (HBP) on EOB-MRI, T2-weighted (T2W) imaging, diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), CT value, iodine-density (ID), atomic-number (Zeff), and electron-density (ED) of the lesion and liver were evaluated. The reduction rates of ID (R-ID) between each phase of the arterial/portal phase on EOB-MRI were calculated. ROC analysis was performed to determine the accuracy for differentiating HCC from HPL.ResultsThere were 55 HCCs and 14 HPLs. On DWI, 42, 11, and two HCCs showed high, slightly high, and iso intensity, respectively. However, all 14 HPLs showed iso intensity on DWI. Area under ROC curve (AUC) of DWI (0.982, 95% confidence interval [CI]=0.957-1) was significantly higher than that of HBP (AUC=0.714; 95% CI=0.580-0.849; P < 0.001), R-ID (AUC=0.742, 95% CI=0.580-0.903; P = 0.004), and ED of portal phase (AUC=0.786, 95% CI=0.640-0.891; P = 0.001) in differentiating HCC and HPL. ADC (<0.001), T2W imaging (<0.001), HBP (<0.001), ED-arterial-phase (<0.001), ED-portal-phase (=0.003), ED-equilibrium-phase (=0.001), R-ID-between-arterial/equilibrium-phase (=0.032), and R-ID-between-portal/equilibrium-phase (=0.042) showed significant differences between HPL and HCC.ConclusionDWI is most useful for differentiating HCC from HPL, although ADC, T2W, HBP, R-ID, and ED may also be relatively useful to differentiate between HPLs and HCCs.

Multi-limited-angle spectral CT image reconstruction based on average image induced relative total variation model.

In recent years, spectral computed tomography (CT) has attracted extensive attention. The purpose of this study is to achieve a low-cost and fast energy spectral CT reconstruction algorithm by implementing multi-limited-angle scans. General spectral CT projection data are collected over a full-angular range of 360 degrees. We simulate multi-source spectral CT by using a pair of X-ray source/detector. To speed up scanning, multi-limited-angle scanning was used in each energy channel. On this basis, an average image induced relative total variation (Aii-RTV) with multi-limited-angle spectral CT image reconstruction model is proposed. The iterative algorithm is used to solve Aii-RTV. Before iteration, the weighted average projection data of the multi-limited-angle energy spectral is carried out. In each step of the iterative algorithm flow is as follows: First, the relative total variation (RTV) reconstruction model is used to reconstruct the average image using average projection data. Then, the partial derivative of the average image is used to calculate the inherent variation in RTV model due to the integrity of the average image, and take its reciprocal as the weight coefficient of the windowing total variation of each energy channel reconstruction image. Finally, the average energy image is used to guide the multi-limited-angle projection data to reconstruct the image of each energy channel so as to suppress the limited-angle artifact of each energy channel image. In addition, we also discuss the influence of parameter selection on reconstructed image quality, which is important for regularization model. Through the reconstruction of multi-limited-angle spectral CT projection data, quantitative results and reconstructed images show that our algorithm has better performance than prior image constrained compressed sensing (PICCS) and RTV. The average PSNR of our reconstruction results in different channels was 35.6273, 4.533 and 2.301 higher than RTV (31.0943) and PICCS (33.3263), respectively.

Fully automatic categorical analysis of striatal subregions in dopamine transporter SPECT using a convolutional neural network.

To provide fully automatic scanner-independent 5-level categorization of the [123I]FP-CIT uptake in striatal subregions in dopamine transporter SPECT. A total of 3500 [123I]FP-CIT SPECT scans from two in house (n = 1740, n = 640) and two external (n = 645, n = 475) datasets were used for this study. A convolutional neural network (CNN) was trained for the categorization of the [123I]FP-CIT uptake in unilateral caudate and putamen in both hemispheres according to 5 levels: normal, borderline, moderate reduction, strong reduction, almost missing. Reference standard labels for the network training were created automatically by fitting a Gaussian mixture model to histograms of the specific [123I]FP-CIT binding ratio, separately for caudate and putamen and separately for each dataset. The CNN was trained on a mixed-scanner subsample (n = 1957) and tested on one independent identically distributed (IID, n = 1068) and one out-of-distribution (OOD, n = 475) test dataset. The accuracy of the CNN for the 5-level prediction of the [123I]FP-CIT uptake in caudate/putamen was 80.1/78.0% in the IID test dataset and 78.1/76.5% in the OOD test dataset. All 4 regional 5-level predictions were correct in 54.3/52.6% of the cases in the IID/OOD test dataset. A global binary score automatically derived from the regional 5-scores achieved 97.4/96.2% accuracy for automatic classification of the scans as normal or reduced relative to visual expert read as reference standard. Automatic scanner-independent 5-level categorization of the [123I]FP-CIT uptake in striatal subregions by a CNN model is feasible with clinically useful accuracy.

Correlation of Spectral CT-Based Iodine Concentration Parameters with LI-RADS Classification of Suspected Hepatocellular Carcinoma Nodules in Cirrhotic Patients.

Background: The LI-RADS classification is widely used for the hepatocellular carcinoma (HCC) risk stratification of liver nodules in cirrhotic patients. The evaluation of nodule enhancement, which is a major criterion, commonly relies on qualitative assessment. This study aims to investigate the potential role of material density (MD) parameters in the iodine maps of spectral computed tomography (SCT) to discriminate between LI-RADS (v2018 CORE) categories in cirrhotic patients. Methods: Dual-energy SCT scans of cirrhotic patients with suspected HCC, taken between March 1st, 2022 and September 30th, 2023, were retrospectively reviewed. All the images were reviewed by trained radiologists to classify nodules as LI-RADS 3, 4, or 5 by consensus. MD maps were generated in the hepatic arterial phase (HAP), portal venous phase (PVP), and equilibrium phase (EP). The iodine concentration density (ICD) values of nodules (ICDnodule) and the non-nodular liver parenchyma (ICDliver) were measured to calculate lesion-to-non-nodular liver ICD ratio (LNR), as well as their differences (ΔICD) and ratios (rLNR). Results were correlated with LI-RADS categories. Results: A total of 69 patients were included and 79 DECT exams were assessed. Overall, 197 nodules (size 24.67 ± 23.11 mm, mean ± SD) were categorized into different LI-RADS classes: 44 were classed as LI-RADS 3 (22.3%), 14 were classed as LI-RADS 4 (7.1%), and 139 were classed as LI-RADS 5 (70.6%). The arterial LNR, arterial ICDnodule, ΔICD, and rLNR between HAP and PVP discriminated between LI-RADS 3 and LI-RADS 4+5 nodules (p < 0.001). All the calculated MD parameters showed high diagnostic accuracy rates (all AUCs = 70-73%). Conclusions: MD parameters of liver nodules measured in SCT scans are viable diagnostic tools that may increase the radiologist's confidence in LI-RADS class allocation in cirrhotic patients. This preliminary and speculative study can serve as a baseline for the potential quantification of iodine concentrations of focal liver lesions to reduce subjectivity in hepatic nodule assessment and reporting. Future perspectives include the quantification of iodine concentration for prognostic stratification before locoregional and systemic treatments in HCC patients.

A short-term predictive model for disease progression in acute-on-chronic liver failure: integrating spectral CT extracellular liver volume and clinical characteristics

BackgroundAcute-on-chronic liver failure (ACLF) is a life-threatening hepatic syndrome. Therefore, this study aimed to develop a comprehensive model combining extracellular liver volume derived from spectral CT (ECVIC−liver) and sarcopenia, for the early prediction of short-term (90-day) disease progression in ACLF.Materials and methodsA retrospective cohort of 126 ACLF patients who underwent hepatic spectral CT scans was included. According to the Asia-Pacific Association for the Study of the Liver (APASL) criteria, patients were divided into the progression group (n = 70) and the stable group (n = 56). ECVIC−liver was measured on the equilibrium period (EP) images of spectral CT, and L3-SMI was measured on unenhanced CT images, with sarcopenia assessed. A comprehensive model was developed by combining independent predictors. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA).ResultsIn the univariate analysis, BMI, WBC, PLT, PTA, L3-SMI, IC-EP, Z-EP, K140-EP, NIC-EP, ECVIC−liver, and Sarcopenia demonstrated associations with disease progression status at 90 days in ACLF patients. In multivariate logistic regression, white blood cell count (WBC) (OR = 1.19, 95% CI: 1.02–1.40; P = 0.026), ECVIC−liver (OR = 1.27, 95% CI: 1.15–1.40; P < 0.001), sarcopenia (OR = 4.15, 95% CI: 1.43–12.01; P = 0.009), MELD-Na score (OR = 1.06, 95%CI: 1.01–1.13;P = 0.042), and CLIF-SOFA score (OR = 1.37, 95%CI:1.15–1.64; P<0.001) emerged as independent risk factors for ACLF progression. The combined model exhibited superior predictive performance (AUCs = 0.910, sensitivity = 80.4%, specificity = 90.0%, PPV = 0.865, NPV = 0.851) compared to CLIF-SOFA, MELD-Na, MELD and CTP scores(both P < 0.001). Calibration curves and DCA confirmed the high clinical utility of the combined model.ConclusionsPatients without sarcopenia and/or with a lower ECVIC−liver have a better prognosis, and the integration of WBC, ECVIC−liver, Sarcopenia, CLIF-SOFA and MELD-Na scores in a composite model offers a concise and effective tool for predicting disease progression in ACLF patients.Trial registrationNot Applicable.