Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques.

OBJECTIVES: To perform a multi-reader comparison of multiparametric dual-energy computed tomography (DECT) images reconstructed with deep-learning image reconstruction (DLIR) and standard-of-care adaptive statistical iterative reconstruction-V (ASIR-V). METHODS: This retrospective study included 100 patients undergoing portal venous phase abdominal CT on a rapid kVp switching DECT scanner. Six reconstructed DECT sets (ASIR-V and DLIR, each at three strengths) were generated. Each DECT set included 65 keV monoenergetic, iodine, and virtual unenhanced (VUE) images. Using a Likert scale, three radiologists performed qualitative assessments for image noise, contrast, small structure visibility, sharpness, artifact, and image preference. Quantitative assessment was performed by measuring attenuation, image noise, and contrast-to-noise ratios (CNR). For the qualitative analysis, Gwet's AC2 estimates were used to assess agreement. RESULTS: DECT images reconstructed with DLIR yielded better qualitative scores than ASIR-V images except for artifacts, where both groups were comparable. DLIR-H images were rated higher than other reconstructions on all parameters (p-value < 0.05). On quantitative analysis, there was no significant difference in the attenuation values between ASIR-V and DLIR groups. DLIR images had higher CNR values for the liver and portal vein, and lower image noise, compared to ASIR-V images (p-value < 0.05). The subgroup analysis of patients with large body habitus (weight ≥ 90 kg) showed similar results to the study population. Inter-reader agreement was good-to-very good overall. CONCLUSION: Multiparametric post-processed DECT datasets reconstructed with DLIR were preferred over ASIR-V images with DLIR-H yielding the highest image quality scores. CLINICAL RELEVANCE STATEMENT: Deep-learning image reconstruction in dual-energy CT demonstrated significant benefits in qualitative and quantitative image metrics compared to adaptive statistical iterative reconstruction-V. KEY POINTS: Dual-energy CT (DECT) images reconstructed using deep-learning image reconstruction (DLIR) showed superior qualitative scores compared to adaptive statistical iterative reconstruction-V (ASIR-V) reconstructed images, except for artifacts where both reconstructions were rated comparable. While there was no significant difference in attenuation values between ASIR-V and DLIR groups, DLIR images showed higher contrast-to-noise ratios (CNR) for liver and portal vein, and lower image noise (p value < 0.05). Subgroup analysis of patients with large body habitus (weight ≥ 90 kg) yielded similar findings to the overall study population.

Image quality of lung perfusion with photon-counting-detector CT: comparison with dual-source, dual-energy CT.

PURPOSE: To evaluate the quality of lung perfusion imaging obtained with photon-counting-detector CT (PCD-CT) in comparison with dual-source, dual-energy CT (DECT). METHODS: Seventy-one consecutive patients scanned with PCD-CT were compared to a paired population scanned with dual-energy on a 3rd-generation DS-CT scanner using (a) for DS-CT (Group 1): collimation: 64 × 0.6 × 2 mm; pitch: 0.55; (b) for PCD-CT (Group 2): collimation: 144 × 0.4 mm; pitch: 1.5; single-source acquisition. The injection protocol was similar in both groups with the reconstruction of perfusion images by subtraction of high- and low-energy virtual monoenergetic images. RESULTS: Compared to Group 1, Group 2 examinations showed: (a) a shorter duration of data acquisition (0.93 ± 0.1 s vs 3.98 ± 0.35 s; p < 0.0001); (b) a significantly lower dose-length-product (172.6 ± 55.14 vs 339.4 ± 75.64 mGy·cm; p < 0.0001); and (c) a higher level of objective noise (p < 0.0001) on mediastinal images. On perfusion images: (a) the mean level of attenuation did not differ (p = 0.05) with less subjective image noise in Group 2 (p = 0.049); (b) the distribution of scores of fissure visualization differed between the 2 groups (p < 0.0001) with a higher proportion of fissures sharply delineated in Group 2 (n = 60; 84.5% vs n = 26; 26.6%); (c) the rating of cardiac motion artifacts differed between the 2 groups (p < 0.0001) with a predominance of examinations rated with mild artifacts in Group 2 (n = 69; 97.2%) while the most Group 1 examinations showed moderate artifacts (n = 52; 73.2%). CONCLUSION: PCD-CT acquisitions provided similar morphologic image quality and superior perfusion imaging at lower radiation doses. CLINICAL RELEVANCE STATEMENT: The improvement in the overall quality of perfusion images at lower radiation doses opens the door for wider applications of lung perfusion imaging in clinical practice. KEY POINTS: The speed of data acquisition with PCD-CT accounts for mild motion artifacts. Sharply delineated fissures are depicted on PCD-CT perfusion images. High-quality perfusion imaging was obtained with a 52% dose reduction.

Evaluation of pancreatic iodine uptake and related influential factors in multiphase dual-energy CT.

OBJECTIVES: To establish normative values and identify potential factors influencing pancreatic iodine uptake using dual-energy CT (DECT). MATERIALS AND METHODS: This retrospective study included participants without pancreatic diseases undergoing DECT at two institutions with different platforms. Their protocols both included arterial phase (AP), portal venous phase (PP), and equilibrium phase (EP), defined as 35 s-40 s, 60 s-70 s, and 150 s-180 s after injection of contrast agent, respectively. Both iodine concentration (IC) and normalised IC (NIC) were measured. Demographic features, local measurements of the pancreas and visceral fat area (VFA) were considered as potential factors influencing iodine uptake using multivariate linear regression analyses. RESULTS: A total of 562 participants (median age 58 years [interquartile range: 47-67], with 282 men) were evaluated. The mean IC differed significantly between two institutions (all p < 0.001) across three contrast-enhanced phases, while the mean NIC showed no significant differences (all p > 0.05). The mean values of NIC were 0.22 at AP, 0.43 at PP and 0.45 at EP. NICAP was independently affected by VFA (ß = 0.362, p < 0.001), smoking (ß = -0.240, p = 0.001), and type-II diabetes (ß = -0.449, p < 0.001); NICPP by VFA (ß = -0.301, p = 0.017) and smoking (ß = -0.291, p < 0.001); and NICEP by smoking (ß = -0.154, p = 0.10) and alcohol consumption (ß = -0.350, p < 0.001) with statistical power values over 0.81. CONCLUSION: NIC values were consistent across institutions. Abdominal obesity, smoking, alcohol consumption, and diabetes are independent factors influencing pancreatic iodine uptake. CLINICAL RELEVANCE STATEMENT: This study has provided reference normative values, influential factors and effective normalisation methods of pancreatic iodine uptake in multiphase dual-energy CT for future studies in this area as a new biological marker. KEY POINTS: Evaluation of pancreatic iodine uptake measured by dual-energy CT is a promising method for future studies. Abdominal obesity, smoking, alcohol consumption, diabetes, and sex are independent factors influencing pancreatic iodine uptake. Utility of normalised iodine concentration is necessary to ensure the consistency across different institutions.

Assessing muscle invasion in bladder cancer via virtual biopsy: a study on quantitative parameters and classical radiomics features from dual-energy CT imaging.

OBJECTIVE: To evaluate the prediction value of Dual-energy CT (DECT)-based quantitative parameters and radiomics model in preoperatively predicting muscle invasion in bladder cancer (BCa). MATERIALS AND METHODS: A retrospective study was performed on 126 patients with BCa who underwent DECT urography (DECTU) in our hospital. Patients were randomly divided into training and test cohorts with a ratio of 7:3. Quantitative parameters derived from DECTU were identified through univariate and multivariate logistic regression analysis to construct a DECT model. Radiomics features were extracted from the 40, 70, 100 keV and iodine-based material-decomposition (IMD) images in the venous phase to construct radiomics models from individual and combined images using a support vector machine classifier, and the optimal performing model was chosen as the final radiomics model. Subsequently, a fusion model combining the DECT parameters and the radiomics model was established. The diagnostic performances of all three models were evaluated through receiver operating characteristic (ROC) curves and the clinical usefulness was estimated using decision curve analysis (DCA). RESULTS: The normalized iodine concentration (NIC) in DECT was an independent factor in diagnosing muscle invasion of BCa. The optimal multi-image radiomics model had predictive performance with an area-under-the-curve (AUC) of 0.867 in the test cohort, better than the AUC = 0.704 with NIC. The fusion model showed an increased level of performance, although the difference in AUC (0.893) was not statistically significant. Additionally, it demonstrated superior performance in DCA. For lesions smaller than 3 cm, the fusion model showed a high predictive capability, achieving an AUC value of 0.911. There was a slight improvement in model performance, although the difference was not statistically significant. This improvement was observed when comparing the AUC values of the DECT and radiomics models, which were 0.726 and 0.884, respectively. CONCLUSION: The proposed fusion model combing NIC and the optimal multi-image radiomics model in DECT showed good diagnostic capability in predicting muscle invasiveness of BCa.

Assessment of bone marrow edema on dual-energy CT scans in people with diabetes mellitus and suspected Charcot neuro-osteoarthropathy.

OBJECTIVE: This study aimed to quantitatively assess the diagnostic value of bone marrow edema (BME) detection on virtual non-calcium (VNCa) images calculated from dual-energy CT (DECT) in people with diabetes mellitus and suspected Charcot neuro-osteoarthropathy (CN). MATERIALS AND METHODS: People with diabetes mellitus and suspected CN who underwent DECT of the feet (80kVp/Sn150kVp) were included retrospectively. Two blinded observers independently measured CT values on VNCa images using circular regions of interest in five locations in the midfoot (cuneiforms, cuboid and navicular) and the calcaneus of the contralateral or (if one foot was available) the ipsilateral foot. Two clinical groups were formed, one with active CN and one without active CN (no-CN), based on the clinical diagnosis. RESULTS: Thirty-two people with diabetes mellitus and suspected CN were included. Eleven had clinically active CN. The mean CT value in the midfoot was significantly higher in the CN group (-55.6 ± 18.7 HU) compared to the no-CN group (-94.4 ± 23.5 HU; p < 0.001). In the CN group, the difference in CT value between the midfoot and calcaneus was statistically significant (p = 0.003); this was not the case in the no-CN group (p = 0.357). The overall observer agreement was good for the midfoot (ICC = 0.804) and moderate for the calcaneus (ICC = 0.712). Sensitivity was 100.0% and specificity was 71.4% using a cutoff value of -87.6 HU. CONCLUSION: The detection of BME on VNCa images has a potential value in people with diabetes mellitus and suspected active CN.

CT in musculoskeletal imaging: still helpful and for what?

Computed tomography (CT) is a common modality employed for musculoskeletal imaging. Conventional CT techniques are useful for the assessment of trauma in detection, characterization and surgical planning of complex fractures. CT arthrography can depict internal derangement lesions and impact medical decision making of orthopedic providers. In oncology, CT can have a role in the characterization of bone tumors and may elucidate soft tissue mineralization patterns. Several advances in CT technology have led to a variety of acquisition techniques with distinct clinical applications. These include four-dimensional CT, which allows examination of joints during motion; cone-beam CT, which allows examination during physiological weight-bearing conditions; dual-energy CT, which allows material decomposition useful in musculoskeletal deposition disorders (e.g., gout) and bone marrow edema detection; and photon-counting CT, which provides increased spatial resolution, decreased radiation, and material decomposition compared to standard multi-detector CT systems due to its ability to directly translate X-ray photon energies into electrical signals. Advanced acquisition techniques provide higher spatial resolution scans capable of enhanced bony microarchitecture and bone mineral density assessment. Together, these CT acquisition techniques will continue to play a substantial role in the practices of orthopedics, rheumatology, metabolic bone, oncology, and interventional radiology.

Applications of dual-energy CT in acute musculoskeletal and trauma imaging-a review.

Recent advances in computed tomography have resulted in new applications of CT scans in musculoskeletal imaging. Dual-energy CT technology involves the acquisition of data at high and low kilovolts, allowing differentiation and quantification of materials with different X-ray absorption. Newer CT scanners with a variety of post-processing options allow interesting applications of dual-energy CT in musculoskeletal and trauma imaging. This article provides an overview of the basic principles and physics of DECT. We review applications of DECT in the evaluation of the acute painful joint with suspicion of gout, metal artefact reduction in the prosthetic joint and in imaging of patients following major trauma. We present a review of literature and case examples to illustrate the strengths and limitations of this modality in the diagnosis of acute musculoskeletal conditions.

Spectral performance evaluation of a second-generation spectral detector CT.

PURPOSE: The aim of this study was to characterize a second-generation wide-detector dual-layer spectral computed tomography (CT) system for material quantification accuracy, acquisition parameter and patient size dependencies, and tissue characterization capabilities. METHODS: A phantom with multiple tissue-mimicking and material-specific inserts was scanned with a dual-layer spectral detector CT using different tube voltages, collimation widths, radiation dose levels, and size configurations. Accuracy of iodine density maps and virtual monoenergetic images (MonoE) were investigated. Additionally, differences between conventional and MonoE 70 keV images were calculated to evaluate acquisition parameter and patient size dependencies. To demonstrate material quantification and differentiation, liver-mimicking inserts with adipose and iron were analyzed with a two-base decomposition utilizing MonoE 50 and 150 keV, and root mean square error (RMSE) for adipose and iron content was reported. RESULTS: Measured inserts exhibited quantitative accuracy across a wide range of MonoE levels. MonoE 70 keV images demonstrated reduced dependence compared to conventional images for phantom size (1 vs. 27 HU) and acquisition parameters, particularly tube voltage (4 vs. 37 HU). Iodine density quantification was successful with errors ranging from -0.58 to 0.44 mg/mL. Similarly, inserts with different amounts of adipose and iron were differentiated, and the small deviation in values within inserts corresponded to a RMSE of 3.49 ± 1.76% and 1.67 ± 0.84 mg/mL for adipose and iron content, respectively. CONCLUSION: The second-generation dual-layer CT enables acquisition of quantitatively accurate spectral data without compromises from differences in patient size and acquisition parameters.

An adult and pediatric size-based contrast administration reduction phantom study for single and dual-energy CT through preservation of contrast-to-noise ratio.

BACKGROUND: Global shortages of iodinated contrast media (ICM) during COVID-19 pandemic forced the imaging community to use ICM more strategically in CT exams. PURPOSE: The purpose of this work is to provide a quantitative framework for preserving iodine CNR while reducing ICM dosage by either lowering kV in single-energy CT (SECT) or using lower energy virtual monochromatic images (VMI) from dual-energy CT (DECT) in a phantom study. MATERIALS AND METHODS: In SECT study, phantoms with effective diameters of 9.7, 15.9, 21.1, and 28.5 cm were scanned on SECT scanners of two different manufacturers at a range of tube voltages. Statistical based iterative reconstruction and deep learning reconstruction were used. In DECT study, phantoms with effective diameters of 20, 29.5, 34.6, and 39.7 cm were scanned on DECT scanners from three different manufacturers. VMIs were created from 40 to 140 keV. ICM reduction by lowering kV levels for SECT or switching from SECT to DECT was calculated based on the linear relationship between iodine CNR and its concentration under different scanning conditions. RESULTS: On SECT scanner A, while matching CNR at 120 kV, ICM reductions of 21%, 58%, and 72% were achieved at 100, 80, and 70 kV, respectively. On SECT scanner B, 27% and 80% ICM reduction was obtained at 80 and 100 kV. On the Fast-kV switch DECT, with CNR matched at 120 kV, ICM reductions were 35%, 30%, 23%, and 15% with VMIs at 40, 50, 60, and 68 keV, respectively. On the dual-source DECT, ICM reductions were 52%, 48%, 42%, 33%, and 22% with VMIs at 40, 50, 60, 70, and 80 keV. On the dual-layer DECT, ICM reductions were 74%, 62%, 45%, and 22% with VMIs at 40, 50, 60, and 70 keV. CONCLUSIONS: Our work provided a quantitative baseline for other institutions to further optimize their scanning protocols to reduce the use of ICM.

Dual-Energy Computed Tomography Iodine Maps: Application in the Diagnosis of Periprosthetic Joint Infection in Total Hip Arthroplasty.

BACKGROUND: The challenge of early and rapid diagnosis of periprosthetic joint infection (PJI) remains important. This study aimed to assess the efficacy of dual-energy computed tomography (CT) (DECT) iodine maps for diagnosing PJI in total hip arthroplasty. METHODS: We prospectively enrolled 68 patients who had postoperative joint pain after hip arthroplasty. All patients underwent preoperative DECT iodine imaging to quantify iodine concentration (IC) in periprosthetic tissues during arterial and venous phases. The diagnostic efficacy of DECT iodine maps was evaluated by constructing receiver operating characteristic (ROC) curves according to the Musculoskeletal Infection Society criteria. RESULTS: Compared with erythrocyte sedimentation rate (Area Under the Curve (AUC) = 0.837), polymorphonuclear cell percentage (AUC = 0.703), and C-reactive protein (AUC = 0.837), periprosthetic tissue venous-phase iodine concentration (IC) (AUC = 0.970) and arterial-phase IC (AUC = 0.964) exhibited outstanding discriminative capability between PJI and aseptic failure. The PJI cut-off point was venous IC = 1.225 mg/ml, with a sensitivity of 92.31% and specificity of 90.48%; for arterial IC = 1.065 mg/ml, the sensitivity was 96.15%, and a specificity was 90.70%. CONCLUSION: This study demonstrates the great potential of DECT iodine maps for the diagnosis of PJI around hip arthroplasty, which helps to differentiate between periprosthetic infection and aseptic failure after hip arthroplasty.