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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.

The aim of this study was to establish an objective segmentation-based evaluation of metal artifact reduction algorithms in the context of percutaneous microwave ablation in a porcine model.Five computed tomography acquisitions from a previous animal study on computed tomography-guided percutaneous applicator positioning for microwave antenna were reconstructed with 6 different algorithms (30 image series total): standard filtered backprojection (B30f) and iterative reconstruction (ADMIRE-I30-1, ADMIRE-I30-3), all with and without metal artifact reduction. For artifact quantification, 3-dimensional segmentation of liver parenchyma without visible artifacts (VLiverReference) and liver volume surrounding the antenna (VLiverVOI) was performed, determining thresholds for artifact segmentation and calculating volume of voxels influenced by artifacts. Objective image analysis was based on relative volume of artifacts, and subjective image quality (ie, metal artifact extent) was evaluated by 2 independent observers. Correlation between objective and subjective evaluation was calculated.Both objective and subjective evaluations showed a significant reduction in metal artifacts when using dedicated metal artifact reduction algorithms (both P0.05). No significant reduction in metal artifacts was found when using iterative reconstruction (both P0.05). A good correlation between subjective and objective image quality was found (Spearman rank correlation coefficient rs = 0.65; P0.05). Interreader agreement was substantial (κ = 0.67).Segmentation-based objective evaluation of metal artifacts shows good agreement with conventional subjective evaluations and offers a promising quantitative and precise approach with limited time expenditure.

Background In computed tomography (CT) quality assurance, alignment of image quality phantoms is crucial for quantitative and reproducible evaluation and may be improved by alignment correction. Our goal was to develop an alignment correction algorithm to facilitate geological sampling of sediment cores taken from a cold-water coral mount. Methods An alignment correction algorithm was developed and tested with a CT acquisition at 120 kVp and 150 mAs of an image quality phantom. Random translation (maximum 15 mm) and rotation (maximum 2.86°) were applied and ground-truth was compared to parameters determined by alignment correction. Furthermore, mean densities were evaluated in four regions of interest (ROIs) placed in the phantom low-contrast section, comparing values before and after correction to ground truth. This process was repeated 1000 times. After validation, alignment correction was applied to CT acquisitions (140 kVp, 570 mAs) of sediment core sections up to 1 m in length, and sagittal reconstructions were calculated for sampling planning. Results In the phantom, average absolute differences between applied and detected parameters after alignment correction were 0.01 ± 0.06 mm (mean ± standard deviation) along the x-axis, 0.11 ± 0.08 mm along the y-axis, 0.15 ± 0.07° around the x-axis, and 0.02 ± 0.02° around the y-axis, respectively. For ROI analysis, differences in densities were 63.12 ± 30.57, 31.38 ± 32.10, 18.27 ± 35.57, and 9.59 ± 26.37 HU before alignment correction and 1.22 ± 1.40, 0.76 ± 0.9, 0.45 ± 0.86, and 0.36 ± 0.48 HU after alignment correction, respectively. For sediment core segments, average absolute detected parameters were 3.93 ± 2.89 mm, 7.21 ± 2.37 mm, 0.37 ± 0.33°, and 0.21 ± 0.22°, respectively. Conclusions The alignment correction algorithm was successfully evaluated in the phantom and allowed a correct alignment of sediment core segments, thus aiding in sampling planning. Application to other tasks, like image quality analysis, seems possible.

Objectives To compare image quality and metal artifact reduction between virtual monochromatic spectral imaging (VMSI), linearly blended dual-energy (DE) and single-energy (SE) images, each with and without dedicated iterative metal artifact reduction (iMAR) for CT-guided biopsy. Materials and methods A biopsy trocar was positioned in the liver of six pigs. DE (Sn140/100kVp) and SE (120kVp/200mAs) acquisitions were performed with equivalent dose. From dual-energy datasets DE Q30-3 images and VMSI between 40-180 keV in steps of 20 keV were generated. From SE datasets I30-3 images were reconstructed. All images were reconstructed with and without iMAR. Objective image quality was analyzed applying density measurements at standardized positions (e.g. trocar tip and liver parenchyma adjacent to the trocar tip) and semi-automated threshold based segmentation. Subjective image quality was performed using semi-quantitative scores. Analyses were performed by two observers. Results At the trocar tip quantitative image analysis revealed significant difference in CT numbers between reconstructions with iMAR compared to reconstructions without iMAR for VMSI at lower keV levels (80 and 100 keV; p = 0.03) and DE (p = 0.03). For liver parenchyma CT numbers were significantly higher in VMSI at high keV compared to low keV (p≤0.01). VMSI at high keV also showed higher CT numbers compared to DE and SE images, though not the level of statistical significance. The best signal-to-noise ratio for VMSI was at 80 keV and comparable to DE and SE. Noise was lowest at 80 keV and lower than in DE and SE. Subjective image quality was best with VMSI at 80 keV regardless of the application of iMAR. iMAR significantly improved image quality at levels of 140 keV and 160 keV. Interreader-agreement was good for quantitative and qualitative analysis. Conclusion iMAR improved image quality in all settings. VMSI with iMAR provided metal artifact reduction and better image quality at 80 keV and thus could improve the accurate positioning in CT-guided needle biopsy. In comparison, DE imaging did not improve image quality compared to SE.

Objectives Using dual-energy computed tomography (DECT) for quantifying iodine content after injection of contrast agent could provide a quantitative basis for dynamic computed tomography (CT) perfusion measurements by means of established mathematical models of contrast agent kinetics, thus improving results by combining the strength of both techniques, which was investigated in this study. Materials and methods A dynamic DECT acquisition over 51 seconds performed at 80/Sn140 kVp in 17 patients with pancreatic carcinoma was used to calculate iodine-enhancement images for each time point by means of 3-material decomposition. After motion correction, perfusion maps of blood flow were calculated using the maximum-slope model from both 80 kVp image data and iodine-enhancement images. Blood flow was measured in regions of interest placed in healthy pancreatic tissue and carcinoma for both of the derived perfusion maps. To assess image quality of input data, an adjusted contrast-to-noise ratio was calculated for 80 kVp images and iodine-enhancement images. Susceptibility of perfusion results to residual patient breathing motion during acquisition was investigated by measuring blood flow in fatty tissue surrounding the pancreas, where blood flow should be negligible compared with the pancreas. Results For both 80 kVp and iodine-enhancement images, blood flow was significantly higher in healthy tissue (114.2 ± 37.4 mL/100 mL/min or 115.1 ± 36.2 mL/100 mL/min, respectively) than in carcinoma (46.5 ± 26.6 mL/100 mL/min or 49.7 ± 24.7 mL/100 mL/min, respectively). Differences in blood flow between 80 kVp image data and iodine-enhancement images were statistically significant in healthy tissue, but not in carcinoma. For 80 kVp images, adjusted contrast-to-noise ratio was significantly higher (1.3 ± 1.1) than for iodine-enhancement images (1.1 ± 0.9). When evaluating fatty tissue surrounding the pancreas for estimating influence of patient motion, measured blood flow was significantly lower for iodine-enhancement images (30.7 ± 12.0 mL/100 mL/min) than for 80 kVp images (39.0 ± 19.1 mL/100 mL/min). Average patient radiation exposure was 8.01 mSv for dynamic DECT acquisition, compared with 4.60 mSv for dynamic 80 kVp acquisition. Discussion Iodine enhancement images can be used to calculate CT perfusion maps of blood flow, and compared with 80 kVp images, results showed only a small difference of 1 mL/100 mL/min in blood flow in healthy tissue, whereas patient radiation exposure was increased for dynamic DECT. Perfusion maps calculated based on iodine-enhancement images showed lower blood flow in fatty tissues surrounding the pancreas, indicating reduced susceptibility to residual patient breathing motion during the acquisition.

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

To assess the influence of image noise on computed tomography (CT) perfusion studies, CT perfusion software algorithms were evaluated for susceptibility to image noise and results applied to clinical perfusion studies. Digital perfusion phantoms were generated using a published deconvolution model to create time-attenuation curves (TACs) for 16 different combinations of blood flow (BF; 30/60/90/120 ml/100 ml/min) and flow extraction product (FEP; 10/20/30/40 ml/100 ml/min) corresponding to values encountered in clinical studies. TACs were distorted with Gaussian noise at 50 different strengths to approximate image noise, performing 200 repetitions for each noise level. A total of 160,000 TACs were evaluated by measuring BF and FEP with CT perfusion software, comparing results for the maximum slope and Patlak models with those obtained with a deconvolution model. To translate results to clinical practice, data of 23 patients from a CT perfusion study were assessed for image noise, and the accuracy of reported CT perfusion measurements was estimated. Perfusion measurements depend on image noise as means and standard deviations of BF and FEP over repetitions increase with increasing image noise, especially for low BF and FEP values. BF measurements derived by deconvolution show larger standard deviations than those performed with the maximum slope model. Image noise in the evaluated CT perfusion study was 26.46 ± 3.52 HU, indicating possible overestimation of BF by up to 85% in a clinical setting. Measurements of perfusion parameters depend heavily upon the magnitude of image noise, which has to be taken into account during selection of acquisition parameters and interpretation of results, e.g., as a quantitative imaging biomarker. • CT perfusion results depend heavily upon the magnitude of image noise. • Different CT perfusion models react differently to the presence of image noise. • Blood flow may be overestimated by 85% in clinical CT perfusion studies.

OBJECTIVE: Quantitative evaluation of different bolus tracking trigger delays for acquisition of dual energy (DE) CT iodine maps as an alternative to CT perfusion. METHODS: Prior to this retrospective analysis of prospectively acquired data, DECT perfusion sequences were dynamically acquired in 22 patients with pancreatic carcinoma using dual source CT at 80/140 kV(p) with tin filtration. After deformable motion-correction, perfusion maps of blood flow (BF) were calculated from 80 kV(p) image series of DECT, and iodine maps were calculated for each of the 34 DECT acquisitions per patient. BF and iodine concentrations were measured in healthy pancreatic tissue and carcinoma. To evaluate potential DECT acquisition triggered by bolus tracking, measured iodine concentrations from the 34 DECT acquisitions per patient corresponding to different trigger delays were assessed for correlation to BF and intergroup differences between tissue types depending on acquisition time. RESULTS: Average BF measured in healthy pancreatic tissue and carcinoma was 87.6 ± 28.4 and 38.6 ± 22.2 ml/100 ml min(–1), respectively. Correlation between iodine concentrations and BF was statistically significant for bolus tracking with trigger delay greater than 0 s (r(max) = 0.89; p < 0.05). Differences in iodine concentrations between healthy pancreatic tissue and carcinoma were statistically significant for DECT acquisitions corresponding to trigger delays of 15–21 s (p < 0.05). CONCLUSION: An acquisition window between 15 and 21 s after exceeding bolus tracking threshold shows promising results for acquisition of DECT iodine maps as an alternative to CT perfusion measurements of BF. ADVANCES IN KNOWLEDGE: After clinical validation, DECT iodine maps of pancreas acquired using bolus tracking with appropriate trigger delay as determined in this study could offer an alternative quantitative imaging biomarker providing functional information for tumor assessment at reduced patient radiation exposure compared to CT perfusion measurements of BF.

Perfusion CT is well-suited for diagnosis of pancreatic tumors but tends to be associated with a high radiation exposure. Dual-energy CT (DECT) might be an alternative to perfusion CT, offering correlating contrasts while being acquired at lower radiation doses. While previous studies compared intensities of Dual Energy iodine maps and CT-perfusion maps, no study has assessed the combined discriminative power of all information that can be generated from an acquisition of both functional imaging methods. We therefore propose the use of a machine learning algorithm for assessing the amount of information that becomes available by the combination of multiple images. For this, we train a classifier on both imaging methods, using a new approach that allows us to train only from small regions of interests (ROIs). This makes our study comparable to other - ROI-based analysis - and still allows comparing the ability of both classifiers to discriminate between healthy and tumorous tissue. We were able to train classifiers that yield DICE scores over 80% with both imaging methods. This indicates that Dual Energy Iodine maps might be used for diagnosis of pancreatic tumors instead of Perfusion CT, although the detection rate is lower. We also present tumor risk maps that visualize possible tumorous areas in an intuitive way and can be used during diagnosis as an additional information source.

Purpose Based on the guidelines from “Report 87: Radiation Dose and Image-quality Assessment in Computed Tomography” of the International Commission on Radiation Units and Measurements (ICRU), a software framework for automated quantitative image quality analysis was developed and its usability for a variety of scientific questions demonstrated. Methods The extendable framework currently implements the calculation of the recommended Fourier image quality (IQ) metrics modulation transfer function (MTF) and noise-power spectrum (NPS), and additional IQ quantities such as noise magnitude, CT number accuracy, uniformity across the field-of-view, contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of simulated lesions for a commercially available cone-beam phantom. Sample image data were acquired with different scan and reconstruction settings on CT systems from different manufacturers. Results Spatial resolution is analyzed in terms of edge-spread function, line-spread-function, and MTF. 3D NPS is calculated according to ICRU Report 87, and condensed to 2D and radially averaged 1D representations. Noise magnitude, CT numbers, and uniformity of these quantities are assessed on large samples of ROIs. Low-contrast resolution (CNR, SNR) is quantitatively evaluated as a function of lesion contrast and diameter. Simultaneous automated processing of several image datasets allows for straightforward comparative assessment. Conclusions The presented framework enables systematic, reproducible, automated and time-efficient quantitative IQ analysis. Consistent application of the ICRU guidelines facilitates standardization of quantitative assessment not only for routine quality assurance, but for a number of research questions, e.g. the comparison of different scanner models or acquisition protocols, and the evaluation of new technology or reconstruction methods.