Multiscale and multimodal imaging for three-dimensional vascular and histomorphological organ structure analysis of the pancreas.

Exocrine and endocrine pancreas are interconnected anatomically and functionally, with vasculature facilitating bidirectional communication. Our understanding of this network remains limited, largely due to two-dimensional histology and missing combination with three-dimensional imaging. In this study, a multiscale 3D-imaging process was used to analyze a porcine pancreas. Clinical computed tomography, digital volume tomography, micro-computed tomography and Synchrotron-based propagation-based imaging were applied consecutively. Fields of view correlated inversely with attainable resolution from a whole organism level down to capillary structures with a voxel edge length of 2.0 µm. Segmented vascular networks from 3D-imaging data were correlated with tissue sections stained by immunohistochemistry and revealed highly vascularized regions to be intra-islet capillaries of islets of Langerhans. Generated 3D-datasets allowed for three-dimensional qualitative and quantitative organ and vessel structure analysis. Beyond this study, the method shows potential for application across a wide range of patho-morphology analyses and might possibly provide microstructural blueprints for biotissue engineering.

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.

Metal Artifact Reduction in Photon-Counting Detector CT: Quantitative Evaluation of Artifact Reduction Techniques.

OBJECTIVES: With the introduction of clinical photon-counting detector computed tomography (PCD-CT) and its novel reconstruction techniques, a quantitative investigation of different acquisition and reconstruction settings is necessary to optimize clinical acquisition protocols for metal artifact reduction. MATERIALS AND METHODS: A multienergy phantom was scanned on a clinical dual-source PCD-CT (NAEOTOM Alpha; Siemens Healthcare GmbH) with 4 different central inserts: water-equivalent plastic, aluminum, steel, and titanium. Acquisitions were performed at 120 kVp and 140 kVp (CTDIvol 10 mGy) and reconstructed as virtual monoenergetic images (VMIs; 110-150 keV), as T3D, and with the standard reconstruction "none" (70 keV VMI) using different reconstruction kernels (Br36, Br56) and with as well as without iterative metal artifact reduction (iMAR). Metal artifacts were quantified, calculating relative percentages of metal artifacts. Mean CT numbers of an adjacent water-equivalent insert and different tissue-equivalent inserts were evaluated, and eccentricity of metal rods was measured. Repeated-measures analysis of variance was performed for statistical analysis. RESULTS: Metal artifacts were most prevalent for the steel insert (12.6% average artifacts), followed by titanium (4.2%) and aluminum (1.0%). The strongest metal artifact reduction was noted for iMAR (with iMAR: 1.4%, without iMAR: 10.5%; P < 0.001) or VMI (VMI: 110 keV 2.6% to 150 keV 3.3%, T3D: 11.0%, and none: 16.0%; P < 0.001) individually, with best results when combining iMAR and VMI at 110 keV (1.2%). Changing acquisition tube potential (120 kV: 6.6%, 140 kV: 5.2%; P = 0.33) or reconstruction kernel (Br36: 5.5%, Br56: 6.4%; P = 0.17) was less effective. Mean CT numbers and standard deviations were significantly affected by iMAR (with iMAR: -3.0 ± 21.5 HU, without iMAR: -8.5 ± 24.3 HU; P < 0.001), VMI (VMI: 110 keV -3.6 ± 21.6 HU to 150 keV -1.4 ± 21.2 HU, T3D: -11.7 ± 23.8 HU, and none: -16.9 ± 29.8 HU; P < 0.001), tube potential (120 kV: -4.7 ± 22.8 HU, 140 kV: -6.8 ± 23.0 HU; P = 0.03), and reconstruction kernel (Br36: -5.5 ± 14.2 HU, Br56: -6.8 ± 23.0 HU; P < 0.001). Both iMAR and VMI improved quantitative CT number accuracy and metal rod eccentricity for the steel rod, but iMAR was of limited effectiveness for the aluminum rod. CONCLUSIONS: For metal artifact reduction in PCD-CT, a combination of iMAR and VMI at 110 keV demonstrated the strongest artifact reduction of the evaluated options, whereas the impact of reconstruction kernel and tube potential was limited.

Pancreatic CT perfusion: quantitative meta-analysis of disease discrimination, protocol development, and effect of CT parameters.

BACKGROUND: This study provides a quantitative meta-analysis of pancreatic CT perfusion studies, investigating choice of study parameters, ability for quantitative discrimination of pancreatic diseases, and influence of acquisition and reconstruction parameters on reported results. METHODS: Based on a PubMed search with key terms 'pancreas' or 'pancreatic,' 'dynamic' or 'perfusion,' and 'computed tomography' or 'CT,' 491 articles published between 1982 and 2020 were screened for inclusion in the study. Inclusion criteria were: reported original data, human subjects, five or more datasets, measurements of pancreas or pancreatic pathologies, and reported quantitative perfusion parameters. Study parameters and reported quantitative measurements were extracted, and heterogeneity of study parameters and trends over time are analyzed. Pooled data were tested with weighted ANOVA and ANCOVA models for differences in perfusion results between normal pancreas, pancreatitis, PDAC (pancreatic ductal adenocarcinoma), and non-PDAC (e.g., neuroendocrine tumors, insulinomas) and based on study parameters. RESULTS: Reported acquisition parameters were heterogeneous, except for contrast agent amount and injection rate. Tube potential and slice thickness decreased, whereas tube current time product and scan coverage increased over time. Blood flow and blood volume showed significant differences between pathologies (both p < 0.001), unlike permeability (p = 0.11). Study parameters showed a significant effect on reported quantitative measurements (p < 0.05). CONCLUSIONS: Significant differences in perfusion measurements between pathologies could be shown for pooled data despite observed heterogeneity in study parameters. Statistical analysis indicates most influential parameters for future optimization and standardization of acquisition protocols. CRITICAL RELEVANCE STATEMENT: Quantitative CT perfusion enables differentiation of pancreatic pathologies despite the heterogeneity of study parameters in current clinical practice.

Evaluation and timing optimization of CT perfusion first pass analysis in comparison to maximum slope model in pancreatic adenocarcinoma.

For implementation, performance evaluation and timing optimization of CT perfusion first pass analysis (FPA) by correlation with maximum slope model (MSM) in pancreatic adenocarcinoma, dynamic CT perfusion acquisitions of 34 time-points were performed in 16 pancreatic adenocarcinoma patients. Regions of interest were marked in both parenchyma and carcinoma. FPA, a low radiation exposure CT perfusion technique, was implemented. Blood flow (BF) perfusion maps were calculated using FPA and MSM. Pearson's correlation between FPA and MSM was calculated at each evaluated time-point to determine optimum timing for FPA. Differences in BF between parenchyma and carcinoma were calculated. Average BF for MSM was 106.8 ± 41.5 ml/100 ml/min in parenchyma and 42.0 ± 24.8 ml/100 ml/min in carcinoma, respectively. For FPA, values ranged from 85.6 ± 37.5 ml/100 ml/min to 117.7 ± 44.5 ml/100 ml/min in parenchyma and from 27.3 ± 18.8 ml/100 ml/min to 39.5 ± 26.6 ml/100 ml/min in carcinoma, depending on acquisition timing. A significant difference (p value < 0.0001) between carcinoma and parenchyma was observed at all acquisition times based on FPA measurements. FPA shows high correlation with MSM (r > 0.90) and 94% reduction in the radiation dose compared to MSM. CT perfusion FPA, where the first scan is obtained after the arterial input function exceeds a threshold of 120 HU, followed by a second scan after 15.5-20.0 s, could be used as a potential imaging biomarker with low radiation exposure for diagnosing and evaluating pancreatic carcinoma in clinical practice, showing high correlation with MSM and the ability to differentiate between parenchyma and carcinoma.

Evaluation of optimal acquisition delays of DECT iodine maps in pancreatic adenocarcinoma: A potential alternative to the Patlak model of CT perfusion.

Introduction: By using bolus tracking with an appropriate acquisition delay dual-energy computed tomography (DECT) iodine maps might serve as a replacement of CT perfusion maps at reduced radiation exposure. This study aimed to evaluate the optimal acquisition delays of DECT for the replacement of parameter maps calculated with the Patlak model in pancreatic adenocarcinoma by corresponding iodine maps. Materials and methods: Dual-source dynamic DECT acquisitions at 80 kVp/Sn140 kVp of 14 patients with pancreatic carcinoma were used to calculate CT perfusion maps of blood volume and permeability with the Patlak model. DECT iodine maps were generated from individual DECT acquisitions, matching acquisition times relative to prior bolus-triggered three-phase CT acquisitions for investigating different acquisition delays. Correlation between perfusion parameters and iodine concentrations was determined for acquisition delays between -6 s and 33 s. Results: Correlation between iodine concentrations and perfusion parameters ranged from -0.05 to 0.63 for blood volume and from -0.05 to 0.71 for permeability, depending on potential trigger delay. The correlation was significant for potential acquisition delays above 1.5 s for blood volume and above 9.0 s for permeability (both p < 0.05). Maximum correlation occurred at an acquisition delay of 15.0 s for blood volume (r = 0.63) and at 25.5 s for permeability (r = 0.71), with significantly lower iodine concentrations in carcinoma (15.0 s: 1.3 ± 0.5 mg/ml; 22.5 s: 1.4 ± 0.7 mg/ml) than in non-neoplastic pancreatic parenchyma (15.0 s: 2.3 ± 0.8 mg/ml; 22.5 s: 2.4 ± 0.6 mg/ml; p < 0.05). Discussion: In the future, well-timed DECT iodine maps acquired with bolus tracking could provide an alternative to permeability and blood volume maps calculated with the Patlak model.

Dual-energy CT-based stopping power prediction for dental materials in particle therapy.

Radiotherapy with protons or light ions can offer accurate and precise treatment delivery. Accurate knowledge of the stopping power ratio (SPR) distribution of the tissues in the patient is crucial for improving dose prediction in patients during planning. However, materials of uncertain stoichiometric composition such as dental implant and restoration materials can substantially impair particle therapy treatment planning due to related SPR prediction uncertainties. This study investigated the impact of using dual-energy computed tomography (DECT) imaging for characterizing and compensating for commonly used dental implant and restoration materials during particle therapy treatment planning. Radiological material parameters of ten common dental materials were determined using two different DECT techniques: sequential acquisition CT (SACT) and dual-layer spectral CT (DLCT). DECT-based direct SPR predictions of dental materials via spectral image data were compared to conventional single-energy CT (SECT)-based SPR predictions obtained via indirect CT-number-to-SPR conversion. DECT techniques were found overall to reduce uncertainty in SPR predictions in dental implant and restoration materials compared to SECT, although DECT methods showed limitations for materials containing elements of a high atomic number. To assess the influence on treatment planning, an anthropomorphic head phantom with a removable tooth containing lithium disilicate as a dental material was used. The results indicated that both DECT techniques predicted similar ranges for beams unobstructed by dental material in the head phantom. When ion beams passed through the lithium disilicate restoration, DLCT-based SPR predictions using a projection-based method showed better agreement with measured reference SPR values (range deviation: 0.2 mm) compared to SECT-based predictions. DECT-based SPR prediction may improve the management of certain non-tissue dental implant and restoration materials and subsequently increase dose prediction accuracy.

Quantification of airway wall contrast enhancement on virtual monoenergetic images from spectral computed tomography.

OBJECTIVES: Quantitative computed tomography (CT) plays an increasingly important role in phenotyping airway diseases. Lung parenchyma and airway inflammation could be quantified by contrast enhancement at CT, but its investigation by multiphasic examinations is limited. We aimed to quantify lung parenchyma and airway wall attenuation in a single contrast-enhanced spectral detector CT acquisition. METHODS: For this cross-sectional retrospective study, 234 lung-healthy patients who underwent spectral CT in four different contrast phases (non-enhanced, pulmonary arterial, systemic arterial, and venous phase) were recruited. Virtual monoenergetic images were reconstructed from 40-160 keV, on which attenuations of segmented lung parenchyma and airway walls combined for 5th-10th subsegmental generations were assessed in Hounsfield Units (HU) by an in-house software. The spectral attenuation curve slope between 40 and 100 keV (λHU) was calculated. RESULTS: Mean lung density was higher at 40 keV compared to that at 100 keV in all groups (p < 0.001). λHU of lung attenuation was significantly higher in the systemic (1.7 HU/keV) and pulmonary arterial phase (1.3 HU/keV) compared to that in the venous phase (0.5 HU/keV) and non-enhanced (0.2 HU/keV) spectral CT (p < 0.001). Wall thickness and wall attenuation were higher at 40 keV compared to those at 100 keV for the pulmonary and systemic arterial phase (p ≤ 0.001). λHU for wall attenuation was significantly higher in the pulmonary arterial (1.8 HU/keV) and systemic arterial (2.0 HU/keV) compared to that in the venous (0.7 HU/keV) and non-enhanced (0.3 HU/keV) phase (p ≤ 0.002). CONCLUSIONS: Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition, and may separate arterial and venous enhancement. Further studies are warranted to analyze spectral CT for inflammatory airway diseases. KEY POINTS: • Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition. • Spectral CT may separate arterial and venous enhancement of lung parenchyma and airway wall. • The contrast enhancement can be quantified by calculating the spectral attenuation curve slope from virtual monoenergetic images.

Ultrahigh resolution whole body photon counting computed tomography as a novel versatile tool for translational research from mouse to man.

X-ray computed tomography (CT) is a cardinal tool in clinical practice. It provides cross-sectional images within seconds. The recent introduction of clinical photon-counting CT allowed for an increase in spatial resolution by more than a factor of two resulting in a pixel size in the center of rotation of about 150 µm. This level of spatial resolution is in the order of dedicated preclinical micro-CT systems. However so far, the need for different dedicated clinical and preclinical systems often hinders the rapid translation of early research results to applications in men. This drawback might be overcome by ultra-high resolution (UHR) clinical photon-counting CT unifying preclinical and clinical research capabilities in a single machine. Herein, the prototype of a clinical UHR PCD CT (SOMATOM CounT, Siemens Healthineers, Forchheim, Germany) was used. The system comprises a conventional energy-integrating detector (EID) and a novel photon-counting detector (PCD). While the EID provides a pixel size of 0.6 mm in the centre of rotation, the PCD provides a pixel size of 0.25 mm. Additionally, it provides a quantification of photon energies by sorting them into up to four distinct energy bins. This acquisition of multi-energy data allows for a multitude of applications, e.g. pseudo-monochromatic imaging. In particular, we examine the relation between spatial resolution, image noise and administered radiation dose for a multitude of use-cases. These cases include ultra-high resolution and multi-energy acquisitions of mice administered with a prototype bismuth-based contrast agent (nanoPET Pharma, Berlin, Germany) as well as larger animals and actual patients. The clinical EID provides a spatial resolution of about 9 lp/cm (modulation transfer function at 10%, MTF10%) while UHR allows for the acquisition of images with up to 16 lp/cm allowing for the visualization of all relevant anatomical structures in preclinical and clinical specimen. The spectral capabilities of the system enable a variety of applications previously not available in preclinical research such as pseudo-monochromatic images. Clinical ultra-high resolution photon-counting CT has the potential to unify preclinical and clinical research on a single system enabling versatile imaging of specimens and individuals ranging from mice to man.

Asynchronous calibration of quantitative computed tomography bone mineral density assessment for opportunistic osteoporosis screening: phantom-based validation and parameter influence evaluation.

Asynchronous calibration could allow opportunistic screening based on routine CT for early osteoporosis detection. In this phantom study, a bone mineral density (BMD) calibration phantom and multi-energy CT (MECT) phantom were imaged on eight different CT scanners with multiple tube voltages (80-150 kVp) and image reconstruction settings (e.g. soft/hard kernel). Reference values for asynchronous BMD estimation were calculated from the BMD-phantom and validated with six calcium composite inserts of the MECT-phantom with known ground truth. Relative errors/changes in estimated BMD were calculated and investigated for influence of tube voltage, CT scanner and reconstruction setting. Reference values for 282 acquisitions were determined, resulting in an average relative error between calculated BMD and ground truth of - 9.2% ± 14.0% with a strong correlation (R2 = 0.99; p < 0.0001). Tube voltage and CT scanner had a significant effect on calculated BMD (p < 0.0001), with relative differences in BMD of 3.8% ± 28.2% when adapting reference values for tube voltage, - 5.6% ± 9.2% for CT scanner and 0.2% ± 0.2% for reconstruction setting, respectively. Differences in BMD were small when using reference values from a different CT scanner of the same model (0.0% ± 1.4%). Asynchronous phantom-based calibration is feasible for opportunistic BMD assessment based on CT images with reference values adapted for tube voltage and CT scanner model.

Potential of a Second-Generation Dual-Layer Spectral CT for Dose Calculation in Particle Therapy Treatment Planning.

In particle therapy treatment planning, dose calculation is conducted using patient-specific maps of tissue ion stopping power ratio (SPR) to predict beam ranges. Improving patient-specific SPR prediction is therefore essential for accurate dose calculation. In this study, we investigated the use of the Spectral CT 7500, a second-generation dual-layer spectral computed tomography (DLCT) system, as an alternative to conventional single-energy CT (SECT) for patient-specific SPR prediction. This dual-energy CT (DECT)-based method allows for the direct prediction of SPR from quantitative measurements of relative electron density and effective atomic number using the Bethe equation, whereas the conventional SECT-based method consists of indirect image data-based prediction through the conversion of calibrated CT numbers to SPR. The performance of the Spectral CT 7500 in particle therapy treatment planning was characterized by conducting a thorough analysis of its SPR prediction accuracy for both tissue-equivalent materials and common non-tissue implant materials. In both instances, DLCT was found to reduce uncertainty in SPR predictions compared to SECT. Mean deviations of 0.7% and 1.6% from measured SPR values were found for DLCT- and SECT-based predictions, respectively, in tissue-equivalent materials. Furthermore, end-to-end analyses of DLCT-based treatment planning were performed for proton, helium, and carbon ion therapies with anthropomorphic head and pelvic phantoms. 3D gamma analysis was performed with ionization chamber array measurements as the reference. DLCT-predicted dose distributions revealed higher passing rates compared to SECT-predicted dose distributions. In the DLCT-based treatment plans, measured distal-edge evaluation layers were within 1 mm of their predicted positions, demonstrating the accuracy of DLCT-based particle range prediction. This study demonstrated that the use of the Spectral CT 7500 in particle therapy treatment planning may lead to better agreement between planned and delivered dose compared to current clinical SECT systems.

µCT to quantify muco-obstructive lung disease and effects of neutrophil elastase knockout in mice.

Muco-obstructive lung diseases are characterized by airway obstruction and hyperinflation, which can be quantified by imaging. Our aim was to evaluate µCT for longitudinal quantification of muco-obstructive lung disease in ß-epithelial Na+ channel overexpressing (Scnn1b-TG) mice and of the effects of neutrophil elastase (NE) knockout on its progression. Lungs from wild-type (WT), NE-/-, Scnn1b-TG, and Scnn1b-TG/NE-/- mice were scanned with 9-µm resolution at 0, 5, 14, and 60 days of age, and airway and parenchymal disease was quantified. Mucus adhesion lesions (MAL) were persistently increased in Scnn1b-TG compared with WT mice from 0 days (20.25 ± 6.50 vs. 9.60 ± 2.07, P < 0.05), and this effect was attenuated in Scnn1b-TG/NE-/- mice (5.33 ± 3.67, P < 0.001). Airway wall area percentage (WA%) was increased in Scnn1b-TG mice compared with WT from 14 days onward (59.2 ± 6.3% vs. 49.8 ± 9.0%, P < 0.001) but was similar in Scnn1b-TG/NE-/- compared with WT at 60 days (46.4 ± 9.2% vs. 45.4 ± 11.5%, P = 0.97). Air proportion (Air%) and mean linear intercept (Lm) were persistently increased in Scnn1b-TG compared with WT from 5 days on (53.9 ± 4.5% vs. 30.0 ± 5.5% and 78.82 ± 8.44 µm vs. 65.66 ± 4.15 µm, respectively, P < 0.001), whereas in Scnn1b-TG/NE-/-, Air% and Lm were similar to WT from birth (27.7 ± 5.5% vs. 27.2 ± 5.9%, P = 0.92 and 61.48 ± 9.20 µm vs. 61.70 ± 6.73 µm, P = 0.93, respectively). Our results suggest that µCT is sensitive to detect the onset and progression of muco-obstructive lung disease and effects of genetic deletion of NE on morphology of airways and lung parenchyma in Scnn1b-TG mice, and that it may serve as a sensitive endpoint for preclinical studies of novel therapeutic interventions for muco-obstructive lung diseases.

Dual-layer spectral CT for proton, helium, and carbon ion beam therapy planning of brain tumors.

Pretreatment computed tomography (CT) imaging is an essential component of the particle therapy treatment planning chain. Treatment planning and optimization with charged particles require accurate and precise estimations of ion beam range in tissues, characterized by the stopping power ratio (SPR). Reduction of range uncertainties arising from conventional CT-number-to-SPR conversion based on single-energy CT (SECT) imaging is of importance for improving clinical practice. Here, the application of a novel imaging and computational methodology using dual-layer spectral CT (DLCT) was performed toward refining patient-specific SPR estimates. A workflow for DLCT-based treatment planning was devised to evaluate SPR prediction for proton, helium, and carbon ion beam therapy planning in the brain. DLCT- and SECT-based SPR predictions were compared in homogeneous and heterogeneous anatomical regions. This study included eight patients scanned for diagnostic purposes with a DLCT scanner. For each patient, four different treatment plans were created, simulating tumors in different parts of the brain. For homogeneous anatomical regions, mean SPR differences of about 1% between the DLCT- and SECT-based approaches were found. In plans of heterogeneous anatomies, relative (absolute) proton range shifts of 0.6% (0.4 mm) in the mean and up to 4.4% (2.1 mm) at the distal fall-off were observed. In the investigated cohort, 12% of the evaluated organs-at-risk (OARs) presented differences in mean or maximum dose of more than 0.5 Gy (RBE) and up to 6.8 Gy (RBE) over the entire treatment. Range shifts and dose differences in OARs between DLCT and SECT in helium and carbon ion treatment plans were similar to protons. In the majority of investigated cases (75th percentile), SECT- and DLCT-based range estimations were within 0.6 mm. Nonetheless, the magnitude of patient-specific range deviations between SECT and DLCT was clinically relevant in heterogeneous anatomical sites, suggesting further study in larger, more diverse cohorts. Results indicate that patients with brain tumors may benefit from DLCT-based treatment planning.

Iodine concentration and tissue attenuation in dual-energy contrast-enhanced CT as a potential quantitative parameter in early detection of local pancreatic carcinoma recurrence after surgical resection.

PURPOSE: Due to the difficult differentiation from non-specific postoperative soft tissue formation (PSF), early diagnosis of pancreatic carcinoma recurrence remains challenging. Thus, we investigated the diagnostic potential of dual-energy (DE) contrast-enhanced CT. METHOD: After potentially curative pancreatic carcinoma resection, 31 consecutive patients with PSF were examined via DE perfusion CT, acquiring 34 images (80 kVp/140 kVp) every 1.5 s, as the initial purpose of this study was evaluating CT-Perfusion. Corresponding time points of arterial, pancreatic, and early venous phase were calculated from bolus trigger times in prior conventional CT. Iodine and 120 kVp-equivalent images were calculated. Regions of interest were placed in each soft tissue formation. Diagnosis of local recurrence was confirmed by regular follow-up or histopathology. RESULTS: Final diagnosis was local recurrence in 17 patients and non-specific PSF in 14 patients. Iodine concentrations in early venous phase were significantly higher in recurrent carcinoma than in non-specific PSF (1.47 mg/ml vs. 0.96 mg/ml, p = 0.007). In earlier contrast phases iodine concentrations tended to be higher, but not significantly. CT numbers in recurrent carcinoma in 120 kVp-equivalent images in venous phase were significantly higher, too (74HU vs 47HU, p = 0.002). ROC-curve analysis for iodine concentrations in early venous phase suggests a cut-off value of ≥ 1.55 mg/ml for local recurrence (AUC = 0.78, specificity = 1.0, sensitivity = 0.53) and for CT numbers in 120kVp-equivalent images a cut-off value of ≥ 57HU (AUC = 0.82, specificity = 0.82, sensitivity = 0.71). CONCLUSION: In difficult cases, measuring iodine concentrations or CT numbers in PSF in (early) venous phase DECT could be a valuable additional parameter for differentiating local recurrence from non-specific PSF.

Consolidated lung on contrast-enhanced chest CT: the use of spectral-detector computed tomography parameters in differentiating atelectasis and pneumonia.

OBJECTIVES: To investigate the value of spectral-detector computed tomography (SDCT) parameters for the quantitative differentiation between atelectasis and pneumonia on contrast-enhanced chest CT. MATERIAL AND METHODS: Sixty-three patients, 22 clinically diagnosed with pneumonia and 41 with atelectasis, underwent contrast-enhanced SDCT scans during the venous phase. CT numbers (Hounsfield Units [HU]) were measured on conventional reconstructions (CON120kVp) and the iodine concentration (Ciodine, [mg/ml]), and effective atomic number (Zeff) on spectral reconstructions, using region-of-interest (ROI) analysis. Receiver operating characteristics (ROC) and contrast-to-noise ratios (CNRs) were calculated to assess each reconstruction's potential to differentiate between atelectasis and pneumonia. RESULTS: On contrast-enhanced SDCT, the difference between atelectasis and pneumonia was significant on CON120kVp, Ciodine, and Zeff images (p < 0.001). On CON120kVp images, a threshold of 81 HU achieved a sensitivity of 93 % and a specificity of 95 % for identifying pneumonia, while Ciodine and Zeff images reached the same sensitivity but lower specificities of 85 % and 83 %. CON120kVp images showed significantly higher CNRs between normal lung and atelectasis or pneumonia with 30.63 and 27.69 compared to Ciodine images with 3.54 and 1.27 and Zeff images with 4.22 and 7.63 (p < 0.001). None of the parameters could differentiate atelectasis and pneumonia without contrast media. CONCLUSIONS: Contrast-enhanced SDCT can differentiate atelectasis and pneumonia based on the spectral parameters Ciodine, and Zeff. However, they had no added value compared to CT number measurement on CON120kVp images. Furthermore, contrast media is still needed for a differentiation based on quantitative SDCT parameters.

Computed Tomography Perfusion Analysis of Pancreatic Adenocarcinoma using Deconvolution, Maximum Slope, and Patlak Methods - Evaluation of Diagnostic Accuracy and Interchangeability of Cut-Off Values.

PURPOSE: The goal of this study was to evaluate the diagnostic accuracy of perfusion computed tomography (CT) parameters obtained by different mathematical-kinetic methods for distinguishing pancreatic adenocarcinoma from normal tissue. To determine cut-off values and to assess the interchangeability of cut-off values, which were determined by different methods. MATERIALS AND METHODS: Perfusion CT imaging of the pancreas was prospectively performed in 23 patients. 19 patients with histopathologically confirmed pancreatic adenocarcinoma were included in the study. Blood flow (BF), blood volume (BV) and permeability-surface area product (PS) were measured in pancreatic adenocarcinoma and normal tissue with the deconvolution (BF, BV, PS), maximum slope (BF), and Patlak methods (BV, PS). The interchangeability of cut-off values was examined by assessing agreement between BF, BV, and PS measured with different mathematical-kinetic methods. RESULTS: Bland-Altman analysis demonstrated poor agreement between perfusion parameters, measured with different mathematical-kinetic methods. According to receiver operating characteristic (ROC) analysis, PS measured with the Patlak method had the significantly lowest diagnostic accuracy (area under ROC curve = 0.748). All other parameters were of high diagnostic accuracy (area under ROC curve = 0.940-0.997), although differences in diagnostic accuracy were not statistically different. Cut-off values for BF of ≤ 91.83 ml/100 ml/min and for BV of ≤ 5.36 ml/100 ml, both measured with the deconvolution method, appear to be the most appropriate cut-off values to distinguish pancreatic adenocarcinoma from normal tissue. CONCLUSION: Perfusion parameters obtained by different methods are not interchangeable. Therefore, cut-off values, which were determined using different methods, are not interchangeable either. Perfusion parameters can help to distinguish pancreatic adenocarcinoma from normal tissue with high diagnostic accuracy, except for PS measured with the Patlak method. KEY POINTS: · Perfusion CT parameters showed high diagnostic accuracy in differentiating between pancreatic adenocarcinoma and normal tissue.. · Only PS measured with the Patlak method showed a significantly lower diagnostic accuracy.. · Perfusion parameters measured with different mathematical-kinetic methods are not interchangeable.. · A specific cut-off value must be determined for each method and each perfusion parameter.. CITATION FORMAT: · Koell M, Klauss M, Skornitzke S et al. Computed Tomography Perfusion Analysis of Pancreatic Adenocarcinoma with the Deconvolution, Maximum Slope, and Patlak Methods - Evaluation of Diagnostic Accuracy and Interchangeability of Cut-Off Values. Fortschr Röntgenstr 2021; 193: 1062 - 1073.

Assessment of tissue perfusion of pancreatic cancer as potential imaging biomarker by means of Intravoxel incoherent motion MRI and CT perfusion: correlation with histological microvessel density as ground truth.

BACKGROUND/OBJECTIVES: The aim of this study was to compare intravoxel incoherent motion (IVIM) diffusion weighted (DW) MRI and CT perfusion to assess tumor perfusion of pancreatic ductal adenocarcinoma (PDAC). METHODS: In this prospective study, DW-MRI and CT perfusion were conducted in nineteen patients with PDAC on the day before surgery. IVIM analysis of DW-MRI was performed and the parameters perfusion fraction f, pseudodiffusion coefficient D*, and diffusion coefficient D were extracted for tumors, upstream, and downstream parenchyma. With a deconvolution-based analysis, the CT perfusion parameters blood flow (BF) and blood volume (BV) were estimated for tumors, upstream, and downstream parenchyma. In ten patients, intratumoral microvessel density (MVDtumor) and microvessel area (MVAtumor) were analyzed microscopically in resection specimens. Correlation coefficients between IVIM parameters, CT perfusion parameters, and histological microvessel parameters in tumors were calculated. Receiver operating characteristic (ROC) analysis was performed for differentiation of tumors and upstream parenchyma. RESULTS: ftumor significantly positively correlated with BFtumor (r = 0.668, p = 0.002) and BVtumor (r = 0.672, p = 0.002). There were significant positive correlations between ftumor and MVDtumor/ MVAtumor (r ≥ 0.770, p ≤ 0.009) as well as between BFtumor and MVDtumor/ MVAtumor (r ≥ 0.697, p ≤ 0.025). Correlation coefficients between ftumor and MVDtumor/ MVAtumor were not significantly different from correlation coefficients between BFtumor and MVDtumor/ MVAtumor (p ≥ 0.400). Moreover, f, BF, BV, and permeability values (PEM) showed excellent performance in distinguishing tumors from upstream parenchyma (area under the ROC curve ≥0.874). CONCLUSIONS: The study shows that IVIM derived ftumor and CT perfusion derived BFtumor similarly reflect vascularity of PDAC and seem to be comparably applicable for the evaluation of tumor perfusion for tumor characterization and as potential quantitative imaging biomarker. TRIAL REGISTRATION: DRKS, DRKS00022227, Registered 26 June 2020, retrospectively registered. https://www.drks.de/drks_web/navigate.do?navigationId=trial . HTML&TRIAL_ID=DRKS00022227.

Virtual monoenergetic reconstructions of dynamic DECT acquisitions for calculation of perfusion maps of blood flow: Quantitative comparison to conventional, dynamic 80 kVp CT perfusion.

PURPOSE: Investigation of potential improvements in dynamic CT perfusion measurements by exploitation of improved visualization of contrast agent in virtual monoenergetic reconstructions of images acquired with dual-energy computed tomography (DECT). METHOD: For 17 patients with pancreatic carcinoma, dynamic dual-source DECT acquisitions were performed at 80kVp/Sn140kVp every 1.5 s over 51 s. Virtual monoenergetic images (VMI) were reconstructed for photon energies between 40 keV and 150 keV (5 keV steps). Using the maximum-slope model, perfusion maps of blood flow were calculated from VMIs and 80kVp images and compared quantitatively with regard to blood flow measured in regions of interest in healthy tissue and carcinoma, standard deviation (SD), and absolute-difference-to-standard-deviation ratio (ADSDR) of measurements. RESULTS: On average, blood flow calculated from VMIs increased with increasing energy levels from 114.3 ± 37.2 mL/100 mL/min (healthy tissue) and 45.6 ± 25.3 mL/100 mL/min (carcinoma) for 40 keV to 128.6 ± 58.9 mL/100 mL/min (healthy tissue) and 75.5 ± 49.8 mL/100 mL/min (carcinoma) for 150 keV, compared to 114.2 ± 37.4 mL/100 mL/min (healthy tissue) and 46.5 ± 26.6 mL/100 mL/min (carcinoma) for polyenergetic 80kVp. Differences in blood flow between tissue types were significant for all energies. Differences between perfusion maps calculated from VMIs and 80kVp images were not significant below 110 keV. SD and ADSDR were significantly better for perfusion maps calculated from VMIs at energies between 40 keV and 55 keV than for those calculated from 80kVp images. Compared to effective dose of dynamic 80kVp acquisitions (4.6 ± 2.2mSv), dose of dynamic DECT/VMI acquisitions (8.0 ± 3.7mSv) was higher. CONCLUSIONS: Perfusion maps of blood flow based on low-energy VMIs between 40 keV and 55 keV offer improved robustness and quality of quantitative measurements over those calculated from 80kVp image data (reference standard), albeit at increased patient radiation exposure.

Influence of acquisition settings and radiation exposure on CT lung densitometry-An anthropomorphic ex vivo phantom study.

OBJECTIVES: To systematically evaluate the influence of acquisition settings in conjunction with raw-data based iterative image reconstruction (IR) on lung densitometry based on multi-row detector computed tomography (CT) in an anthropomorphic chest phantom. MATERIALS AND METHODS: Ten porcine heart-lung explants were mounted in an ex vivo chest phantom shell, six with highly and four with low attenuating chest wall. CT (Somatom Definition Flash, Siemens Healthineers) was performed at 120kVp and 80kVp, each combined with current-time products of 120, 60, 30, and 12mAs, and was reconstructed with filtered back projection (FBP) and IR (Safire, Siemens Healthineers). Mean lung density (LD), air density (AD) and noise were measured by semi-automated region-of interest (ROI) analysis, with 120kVp/120 mAs serving as the standard of reference. RESULTS: Using IR, noise in lung parenchyma was reduced by ~ 31% at high attenuating chest wall and by ~ 22% at low attenuating chest wall compared to FBP, respectively (p<0.05). IR induced changes in the order of ±1 HU to mean absolute LD and AD compared to corresponding FBP reconstructions which were statistically significant (p<0.05). CONCLUSIONS: Densitometry is influenced by acquisition parameters and reconstruction algorithms to a degree that may be clinically negligible. However, in longitudinal studies and clinical research identical protocols and potentially other measures for calibration may be required.

Image quality evaluation of dual-layer spectral CT in comparison to single-layer CT in a reduced-dose setting.

OBJECTIVES: To quantitatively and qualitatively evaluate image quality in dual-layer CT (DLCT) compared to single-layer CT (SLCT) in the thorax, abdomen, and pelvis in a reduced-dose setting. METHODS: Intraindividual, retrospective comparisons were performed in 25 patients who received at least one acquisition of all three acquisition protocols SLCTlow (100 kVp), DLCThigh (120 kVp), and DLCTlow (120 kVp), all covering the venous-phase thorax, abdomen, and pelvis with matched CTDIvol between SLCTlow and DLCTlow. Reconstruction parameters were identical between all scans. Image quality was assessed quantitatively at 10 measurement locations in the thorax, abdomen, and pelvis by two independent observers, and subjectively with an intraindividual forced choice test between the three acquisitions. Dose-length product (DLP) and CTDIvol were extracted for dose comparison. RESULTS: Despite matched CTDIvol in acquisition protocols, CTDIvol and DLP were lower for SLCTlow compared to DLCTlow and DLCThigh (DLP 408.58, 444.68, 647.08 mGy·cm, respectively; p < 0.0004), as automated tube current modulation for DLCTlow reached the lower limit in the thorax (mean 66.1 mAs vs limit 65 mAs). Noise and CNR were comparable between SLCTlow and DLCTlow (p values, 0.29-0.51 and 0.05-0.20), but CT numbers were significantly higher for organs and vessels in the upper abdomen for SLCTlow compared to DLCTlow. DLCThigh had significantly better image quality (Noise and CNR). Subjective image quality was superior for DLCThigh, but no difference was found between SLCTlow and DLCTlow. CONCLUSIONS: DLCTlow showed comparable image quality to SLCTlow, with the additional possibility of spectral post-processing. Further dose reduction seems possible by decreasing the lower limit of the tube current for the thorax. KEY POINTS: • Clinical use of reduced-dose DLCT is feasible despite the required higher tube potential. • DLCT with reduced dose shows comparable objective and subjective image quality to reduced-dose SLCT. • Further dose reduction in the thorax might be possible by adjusting mAs thresholds.