Deep Learning-Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast-Enhanced MRI.

BACKGROUND: The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs. PURPOSE: To assess the performance of the deep learning-based artificial intelligence (AI) software in identifying and measuring lesions on contrast-enhanced magnetic resonance imaging (MRI) images in patients with FLLs. STUDY TYPE: Retrospective. SUBJECTS: 395 patients with 1149 FLLs. FIELD STRENGTH/SEQUENCE: The 1.5 T and 3 T scanners, including T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging. ASSESSMENT: The diagnostic performance of AI, radiologist, and their combination was compared. Using 20 mm as the cut-off value, the lesions were divided into two groups, and then divided into four subgroups: <10, 10-20, 20-40, and ≥40 mm, to evaluate the sensitivity of radiologists and AI in the detection of lesions of different sizes. We compared the pathologic sizes of 122 surgically resected lesions with measurements obtained using AI and those made by radiologists. STATISTICAL TESTS: McNemar test, Bland-Altman analyses, Friedman test, Pearson's chi-squared test, Fisher's exact test, Dice coefficient, and intraclass correlation coefficients. A P-value <0.05 was considered statistically significant. RESULTS: The average Dice coefficient of AI in segmentation of liver lesions was 0.62. The combination of AI and radiologist outperformed the radiologist alone, with a significantly higher detection rate (0.894 vs. 0.825) and sensitivity (0.883 vs. 0.806). The AI showed significantly sensitivity than radiologists in detecting all lesions <20 mm (0.848 vs. 0.788). Both AI and radiologists achieved excellent detection performance for lesions ≥20 mm (0.867 vs. 0.881, P = 0.671). A remarkable agreement existed in the average tumor sizes among the three measurements (P = 0.174). DATA CONCLUSION: AI software based on deep learning exhibited practical value in automatically identifying and measuring liver lesions. TECHNICAL EFFICACY: Stage 2.

Toward a Platform for Structured Data Acquisition in Oncology: A Pilot Study on Prostate Cancer Screening.

INTRODUCTION: Physicians spend an ever-rising amount of time to collect relevant information from highly variable medical reports and integrate them into the patient's health condition. OBJECTIVES: We compared synoptic reporting based on data elements to narrative reporting in order to evaluate its capabilities to collect and integrate clinical information. METHODS: We developed a novel system to align medical reporting to data integration requirements and tested it in prostate cancer screening. We compared expenditure of time, data quality, and user satisfaction for data acquisition, integration, and evaluation. RESULTS: In a total of 26 sessions, 2 urologists, 2 radiologists, and 2 pathologists conducted the diagnostic work-up for prostate cancer screening with both narrative reporting and the novel system. The novel system led to a significantly reduced time for collection and integration of patient information (91%, p < 0.001), reporting in radiology (44%, p < 0.001) and pathology (33%, p = 0.154). The system usage showed a high positive effect on evaluated data quality parameters completeness, format, understandability, as well as user satisfaction. CONCLUSION: This study provides evidence that synoptic reporting based on data elements is effectively reducing time for collection and integration of patient information. Further research is needed to assess the system's impact for different patient journeys.

AI-based detection and classification of distal radius fractures using low-effort data labeling: evaluation of applicability and effect of training set size.

OBJECTIVES: To evaluate the performance of a deep convolutional neural network (DCNN) in detecting and classifying distal radius fractures, metal, and cast on radiographs using labels based on radiology reports. The secondary aim was to evaluate the effect of the training set size on the algorithm's performance. METHODS: A total of 15,775 frontal and lateral radiographs, corresponding radiology reports, and a ResNet18 DCNN were used. Fracture detection and classification models were developed per view and merged. Incrementally sized subsets served to evaluate effects of the training set size. Two musculoskeletal radiologists set the standard of reference on radiographs (test set A). A subset (B) was rated by three radiology residents. For a per-study-based comparison with the radiology residents, the results of the best models were merged. Statistics used were ROC and AUC, Youden's J statistic (J), and Spearman's correlation coefficient (ρ). RESULTS: The models' AUC/J on (A) for metal and cast were 0.99/0.98 and 1.0/1.0. The models' and residents' AUC/J on (B) were similar on fracture (0.98/0.91; 0.98/0.92) and multiple fragments (0.85/0.58; 0.91/0.70). Training set size and AUC correlated on metal (ρ = 0.740), cast (ρ = 0.722), fracture (frontal ρ = 0.947, lateral ρ = 0.946), multiple fragments (frontal ρ = 0.856), and fragment displacement (frontal ρ = 0.595). CONCLUSIONS: The models trained on a DCNN with report-based labels to detect distal radius fractures on radiographs are suitable to aid as a secondary reading tool; models for fracture classification are not ready for clinical use. Bigger training sets lead to better models in all categories except joint affection. KEY POINTS: • Detection of metal and cast on radiographs is excellent using AI and labels extracted from radiology reports. • Automatic detection of distal radius fractures on radiographs is feasible and the performance approximates radiology residents. • Automatic classification of the type of distal radius fracture varies in accuracy and is inferior for joint involvement and fragment displacement.

In vivo 3D tomography of the lumbar spine using a twin robotic X-ray system: quantitative and qualitative evaluation of the lumbar neural foramina in supine and upright position.

OBJECTIVES: Supine lumbar spine examinations underestimate body weight effects on neuroforaminal size. Therefore, our purpose was to evaluate size changes of the lumbar neuroforamina using supine and upright 3D tomography and to initially assess image quality compared with computed tomography (CT). METHODS: The lumbar spines were prospectively scanned in 48 patients in upright (3D tomographic twin robotic X-ray) and supine (30 with 3D tomography, 18 with CT) position. Cross-sectional area (CSA), cranio-caudal (CC), and ventro-dorsal (VD) diameters of foramina were measured by two readers and additionally graded in relation to the intervertebral disc height. Visibility of bone/soft tissue structures and image quality were assessed independently on a 5-point Likert scale for the 18 patients scanned with both modalities. Descriptive statistics, Wilcoxon's signed-rank test (p < 0.05), and interreader reliability were calculated. RESULTS: Neuroforaminal size significantly decreased at all levels for both readers from the supine (normal intervertebral disc height; CSA 1.25 ± 0.32 cm2; CC 1.84 ± 0.24 cm2; VD 0.88 ± 0.16 cm2) to upright position (CSA 1.12 ± 0.34 cm2; CC 1.78 ± 0.24 cm2; VD 0.83 ± 0.16 cm2; each p < 0.001). Decrease in intervertebral disc height correlated with decrease in foraminal size (supine: CSA 0.88 ± 0.34 cm2; CC 1.39 ± 0.33 cm2; VD 0.87 ± 0.26 cm2; upright: CSA 0.83 ± 0.37 cm2, p = 0.010; CC 1.32 ± 0.33 cm2, p = 0.015; VD 0.80 ± 0.21 cm2, p = 0.021). Interreader reliability for area was fair to excellent (0.51-0.89) with a wide range for cranio-caudal (0.32-0.74) and ventro-dorsal (0.03-0.70) distances. Image quality was superior for CT compared with that for 3D tomography (p < 0.001; κ, CT = 0.66-0.92/3D tomography = 0.51-1.00). CONCLUSIONS: The size of the lumbar foramina is smaller in the upright weight-bearing position compared with that in the supine position. Image quality, especially nerve root delineation, is inferior using 3D tomography compared to CT. KEY POINTS: • Weight-bearing examination demonstrates a decrease of the neuroforaminal size. • Patients with higher decrease in intervertebral disc showed a narrower foraminal size. • Image quality is superior with CT compared to 3D tomographic twin robotic X-ray at the lumbar spine.

Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium.

BACKGROUND: Artificial intelligence can assist in cardiac image interpretation. Here, we achieved a substantial reduction in time required to read a cardiovascular magnetic resonance (CMR) study to estimate left atrial volume without compromising accuracy or reliability. Rather than deploying a fully automatic black-box, we propose to incorporate the automated LA volumetry into a human-centric interactive image-analysis process. METHODS AND RESULTS: Atri-U, an automated data analysis pipeline for long-axis cardiac cine images, computes the atrial volume by: (i) detecting the end-systolic frame, (ii) outlining the endocardial borders of the LA, (iii) localizing the mitral annular hinge points and constructing the longitudinal atrial diameters, equivalent to the usual workup done by clinicians. In every step human interaction is possible, such that the results provided by the algorithm can be accepted, corrected, or re-done from scratch. Atri-U was trained and evaluated retrospectively on a sample of 300 patients and then applied to a consecutive clinical sample of 150 patients with various heart conditions. The agreement of the indexed LA volume between Atri-U and two experts was similar to the inter-rater agreement between clinicians (average overestimation of 0.8 mL/m2 with upper and lower limits of agreement of - 7.5 and 5.8 mL/m2, respectively). An expert cardiologist blinded to the origin of the annotations rated the outputs produced by Atri-U as acceptable in 97% of cases for step (i), 94% for step (ii) and 95% for step (iii), which was slightly lower than the acceptance rate of the outputs produced by a human expert radiologist in the same cases (92%, 100% and 100%, respectively). The assistance of Atri-U lead to an expected reduction in reading time of 66%-from 105 to 34 s, in our in-house clinical setting. CONCLUSIONS: Our proposal enables automated calculation of the maximum LA volume approaching human accuracy and precision. The optional user interaction is possible at each processing step. As such, the assisted process sped up the routine CMR workflow by providing accurate, precise, and validated measurement results.

Building Large-Scale Quantitative Imaging Databases with Multi-Scale Deep Reinforcement Learning: Initial Experience with Whole-Body Organ Volumetric Analyses.

To explore the feasibility of a fully automated workflow for whole-body volumetric analyses based on deep reinforcement learning (DRL) and to investigate the influence of contrast-phase (CP) and slice thickness (ST) on the calculated organ volume. This retrospective study included 431 multiphasic CT datasets-including three CP and two ST reconstructions for abdominal organs-totaling 10,508 organ volumes (10,344 abdominal organ volumes: liver, spleen, and kidneys, 164 lung volumes). Whole-body organ volumes were determined using multi-scale DRL for 3D anatomical landmark detection and 3D organ segmentation. Total processing time for all volumes and mean calculation time per case were recorded. Repeated measures analyses of variance (ANOVA) were conducted to test for robustness considering CP and ST. The algorithm calculated organ volumes for the liver, spleen, and right and left kidney (mean volumes in milliliter (interquartile range), portal venous CP, 5 mm ST: 1868.6 (1426.9, 2157.8), 350.19 (45.46, 395.26), 186.30 (147.05, 214.99) and 181.91 (143.22, 210.35), respectively), and for the right and left lung (2363.1 (1746.3, 2851.3) and 1950.9 (1335.2, 2414.2)). We found no statistically significant effects of the variable contrast phase or the variable slice thickness on the organ volumes. Mean computational time per case was 10 seconds. The evaluated approach, using state-of-the art DRL, enables a fast processing of substantial amounts irrespective of CP and ST, allowing building up organ-specific volumetric databases. The thus derived volumes may serve as reference for quantitative imaging follow-up.

Structured Data Acquisition in Oncology.

BACKGROUND: Demographic changes and improvement in therapy have shifted the focus of treatment towards chronic diseases and multiple health conditions. This has caused a tremendous increase in data per patient that needs to be integrated longitudinally and across departmental silos. The general increase in the volume of data per diagnostic examination and the number of diagnostic procedures per diagnostic pathway additionally accentuate this data integration challenge. SUMMARY: Subspecialization in medicine has led to largely autonomously organized departments with in-dependent IT ecosystems. This patchwork of IT infrastructure is not prepared to meet the data integration challenge. The resulting lack of integrated information makes the treatment of chronically ill patients increasingly difficult and error prone. Key Message: A sustainable method for data ac-quisition is needed to aid multimodal treatment and improve efficiency in healthcare.

Effective dose to patient measurements for flat-detector computed tomography protocols in acute stroke care.

OBJECTIVES: The aim was to measure the effective dose of flat-detector CT (FDCT) whole-brain imaging, biphasic FDCT angiography (FDCT-A), and FDCT perfusion (FDCT-P) protocols and compare it to previously reported effective dose values of multidetector CT (MDCT) applications. MATERIALS: We measured effective dose according to the IRCP 103 using an anthropomorphic phantom equipped with thermoluminescent dosimeters (TLDs). Placement was according to anatomical positions of each organ. In total, 60 TLDs (≥ 4 TLDs/organ) were placed into and onto the phantom to account for all relevant organs. Organs within the primary beam were covered with more TLDs. Additionally, we measured dose to the eye lens with two TLDs per eye. Protocols which we routinely use in clinical practice were measured on a biplane angiography system. RESULTS: The effective dose of the 20-s protocol/7-s protocol for whole-brain imaging was 2.6 mSv/2.4 mSv. The radiation dose to the eye lens was 24/23 mGy. For the biphasic high-/low-dose FDCT-A protocol, the effective dose was 8.9/2.8 mSv respectively. The eye lens dose was 60/14 mGy. The contribution of bolus tracking to the effective dose was 0.66 mSv (assuming average duration of 14 s). The multisweep FDCT-P protocol had an effective dose of 5.9 mSv and an eye lens dose of 46 mGy. CONCLUSION: Except for the high-dose biphasic FDCT-A protocol, FDCT applications used in neuroradiology have effective doses, which do not deviate more than 1 mSv from previously reported values for MDCT applications. However, the effective dose to the eye lens in commonly used stroke paradigms exceeds the recommended annual dose twofold. KEY POINTS: • Flat-detector computed tomography (FDCT) can be used for acute and periinterventional imaging of acute stroke patients and in neurointerventions. • Except for the high-dose FDCT angiography protocol, the effective doses do not deviate more than 1 mSv from previously reported values for multidetector CT applications. • Strategies to decrease the effective lens dose especially in younger patients should be evaluated in the future.

Automated detection of pulmonary embolism in CT pulmonary angiograms using an AI-powered algorithm.

OBJECTIVES: To evaluate the performance of an AI-powered algorithm for the automatic detection of pulmonary embolism (PE) on chest computed tomography pulmonary angiograms (CTPAs) on a large dataset. METHODS: We retrospectively identified all CTPAs conducted at our institution in 2017 (n = 1499). Exams with clinical questions other than PE were excluded from the analysis (n = 34). The remaining exams were classified into positive (n = 232) and negative (n = 1233) for PE based on the final written reports, which defined the reference standard. The fully anonymized 1-mm series in soft tissue reconstruction served as input for the PE detection prototype algorithm that was based on a deep convolutional neural network comprising a Resnet architecture. It was trained and validated on 28,000 CTPAs acquired at other institutions. The result series were reviewed using a web-based feedback platform. Measures of diagnostic performance were calculated on a per patient and a per finding level. RESULTS: The algorithm correctly identified 215 of 232 exams positive for pulmonary embolism (sensitivity 92.7%; 95% confidence interval [CI] 88.3-95.5%) and 1178 of 1233 exams negative for pulmonary embolism (specificity 95.5%; 95% CI 94.2-96.6%). On a per finding level, 1174 of 1352 findings marked as embolus by the algorithm were true emboli. Most of the false positive findings were due to contrast agent-related flow artifacts, pulmonary veins, and lymph nodes. CONCLUSION: The AI prototype algorithm we tested has a high degree of diagnostic accuracy for the detection of PE on CTPAs. Sensitivity and specificity are balanced, which is a prerequisite for its clinical usefulness. KEY POINTS: • An AI-based prototype algorithm showed a high degree of diagnostic accuracy for the detection of pulmonary embolism on CTPAs. • It can therefore help clinicians to automatically prioritize exams with a high suspection of pulmonary embolism and serve as secondary reading tool. • By complementing traditional ways of worklist prioritization in radiology departments, this can speed up the diagnostic and therapeutic workup of patients with pulmonary embolism and help to avoid false negative calls.

Exploring cortical predictors of clinical response to electroconvulsive therapy in major depression.

Electroconvulsive therapy (ECT) is a rapid and highly effective treatment option for treatment-resistant major depressive disorder (TRD). The neural mechanisms underlying such beneficial effects are poorly understood. Exploring associations between changes of brain structure and clinical response is crucial for understanding ECT mechanisms of action and relevant for the validation of potential biomarkers that can facilitate the prediction of ECT efficacy. The aim of this explorative study was to identify cortical predictors of clinical response in TRD patients treated with ECT. We longitudinally investigated 12 TRD patients before and after ECT. Twelve matched healthy controls were studied cross sectionally. Demographical, clinical, and structural magnetic resonance imaging data at 3 T and multiple cortical markers derived from surface-based morphometry (SBM) analyses were considered. Multiple regression models were computed to identify predictors of clinical response to ECT, as reflected by Hamilton Depression Rating Scale (HAMD) score changes. Symptom severity differences pre-post-ECT were predicted by models including demographic data, clinical data and SBM of frontal, cingulate, and entorhinal structures. Using all-subsets regression, a model comprising HAMD score at baseline and cortical thickness of the left rostral anterior cingulate gyrus explained most variance in the data (multiple R2 = 0.82). The data suggest that SBM provides powerful measures for identifying biomarkers for ECT response in TRD. Rostral anterior cingulate thickness and HAMD score at baseline showed the greatest predictive power of clinical response, in contrast to cortical complexity, cortical gyrification, or demographical data.

Comparison of 3D X-ray tomography with computed tomography in patients with distal extremity fractures.

OBJECTIVE: To compare fracture detection, image quality, and radiation dose in patients with distal extremity fractures using 3D tomography and computed tomography (CT). MATERIALS AND METHODS: IRB approval was obtained including informed consent for this prospective study from June to December 2016. Patients diagnosed with an acute fracture at CT were consecutively scanned on the same day using 3D tomography. Anatomical location (effected bone and location within the bone) and morphological characteristics of fractures (avulsion, articular involvement, mono- vs. multifragmented, displacement), visibility of bone/soft tissue structures, and image quality were assessed independently by two blinded readers on a 5-point Likert scale. Dose-length-product (DLP; mGy*cm) was compared between both modalities. Descriptive statistics, Wilcoxon signed rank test (P < 0.05), Student's t test (P < 0.05), and Cohen's kappa (κ) for interreader reliability were calculated. RESULTS: In 46 patients (28 males; 18 females; mean age, 53 ± 20 years) with 28 hand/wrist and 18 foot/ankle examinations, 86 out of 92 fractures were diagnosed with 3D tomography compared with CT. No false-positive finding occurred at 3D tomography. The six missed fractures on 3D tomography were five avulsion fractures of the carpals/metacarpals or tarsals/metatarsals, respectively, and one nondisplaced fracture of the capitate. Interreader agreement of anatomical location and morphological characteristics was substantial to almost perfect for upper (κ = 0.80-0.96) and lower (κ = 0.70-0.97) extremity fractures. Visibility of bone and soft tissue structures and image quality were slightly inferior using 3D tomography compared with CT (upper extremity P < 0.001-0.038 and lower extremity P < 0.001-0.035). DLP of a comparable scan coverage was significantly lower for 3D tomography (P < 0.001) for both upper (3D mean, 19.4 ± 5.9 mGy*cm; estimated CT mean, 336.5 ± 52.2 mGy*cm) and lower extremities (3D mean, 24.1 ± 11.1 mGy*cm; estimated CT mean, 182.9 ± 6.5 mGy*cm). Even the highest DLP with 3D tomography was < 30% of the mean estimated CT dose of a comparable area of coverage. CONCLUSION: Fracture assessment of peripheral extremities is reliable utilizing a low-dose 3D tomography X-ray system, with slightly reduced image quality.

Gadoxetate Disodium versus Gadoterate Meglumine: Quantitative Respiratory and Hemodynamic Metrics by Using Compressed-Sensing MRI.

Background Gadoxetate disodium has been associated with various respiratory irregularities at arterial imaging MRI. Purpose To measure the relationship between gadolinium-based contrast agent administration and irregularities by comparing gadoxetate disodium and gadoterate meglumine at free breathing. Materials and Methods This prospective observational cohort study (January 2015 to May 2017) included consecutive abdominal MRI performed with either gadoxetate disodium or gadoterate meglumine enhancement. Participants underwent dynamic imaging by using the golden-angle radial sparse parallel sequence at free breathing. The quantitative assessment evaluated the aortic contrast enhancement, the respiratory hepatic translation, and the k-space-derived respiratory pattern. Analyses of variance compared hemodynamic metrics, respiratory-induced hepatic motion, and respiratory parameters before and after respiratory gating. Results A total of 497 abdominal MRI examinations were included. Of these, 338 participants were administered gadoxetate disodium (mean age, 59 years ± 15; 153 women) and 159 participants were administered gadoterate meglumine (mean age, 59 years ± 17; 85 women). The arterial bolus of gadoxetate disodium arrived later than gadoterate meglumine (19.7 vs 16.3 seconds, respectively; P < .001). Evaluation of the hepatic respiratory translation showed respiratory motion occurring in 70.7% (239 of 338) of participants who underwent gadoxetate-enhanced examinations and in 28.9% (46 of 159) of participants who underwent gadoterate-enhanced examinations (P < .001). The duration of motion irregularities was longer for gadoxetate than for gadoterate (19.2 seconds vs 17.2 seconds, respectively) and the motion irregularities were more severe (P < .001). Both the respiratory frequency and amplitude were shorter for participants administered gadoxetate from the prebolus phase to the late arterial phase compared with gadoterate (P < .001). Conclusion The administration of two different gadolinium-based contrast agents, gadoxetate and gadoterate, at free-breathing conditions potentially leads to respiratory irregularities with differing intensity and onset. © RSNA, 2019 Online supplemental material is available for this article.

Serum levels of hyaluronic acid are associated with COPD severity and predict survival.

Hyaluronic acid (HA) and its degradation products play an important role in lung pathophysiology and airway remodelling in chronic obstructive pulmonary disease (COPD).We investigated if HA and its degrading enzyme hyaluronidase (HYAL)-1 are associated with COPD severity and outcome.Serum HA was assessed in a discovery cohort of 80 COPD patients at stable state and exacerbations. HA, HYAL-1 and HYAL-1 enzymatic activity were evaluated at stable state, exacerbations and 4 weeks after exacerbations in 638 COPD patients from the PROMISE validation cohort.In the discovery cohort, serum HA was higher at exacerbations compared with the stable state (p=0.015). In the validation cohort, HA was higher at moderate and severe exacerbations than at baseline (p<0.001), and remained higher after 4 weeks (p<0.001). HA was strongly predictive for overall survival since it was associated with time to death (p<0.001) independently of adjusted Charlson score, annual exacerbation rate and BODE (body mass, airflow obstruction, dyspnoea, exercise capacity) index. Serum HYAL-1 was increased at moderate (p=0.004) and severe (p=0.003) exacerbations, but decreased after 4 weeks (p<0.001). HYAL-1 enzymatic activity at stable state was inversely correlated with FEV1 % pred (p=0.034) and survival time (p=0.017).Serum HA is associated with COPD severity and predicts overall survival. Degradation of HA is associated with airflow limitation and impairment of lung function.

Artificial Intelligence in Musculoskeletal Imaging: Review of Current Literature, Challenges, and Trends.

Artificial intelligence (AI) has gained major attention with a rapid increase in the number of published articles, mostly recently. This review provides a general understanding of how AI can or will be useful to the musculoskeletal radiologist. After a brief technical background on AI, machine learning, and deep learning, we illustrate, through examples from the musculoskeletal literature, potential AI applications in the various steps of the radiologist's workflow, from managing the request to communication of results. The implementation of AI solutions does not go without challenges and limitations. These are also discussed, as well as the trends and perspectives.

Comparison of image quality and radiation dose between split-filter dual-energy images and single-energy images in single-source abdominal CT.

OBJECTIVES: To compare image quality and radiation dose of abdominal split-filter dual-energy CT (SF-DECT) combined with monoenergetic imaging to single-energy CT (SECT) with automatic tube voltage selection (ATVS). METHODS: Two-hundred single-source abdominal CT scans were performed as SECT with ATVS (n = 100) and SF-DECT (n = 100). SF-DECT scans were reconstructed and subdivided into composed images (SF-CI) and monoenergetic images at 55 keV (SF-MI). Objective and subjective image quality were compared among single-energy images (SEI), SF-CI and SF-MI. CNR and FOM were separately calculated for the liver (e.g. CNRliv) and the portal vein (CNRpv). Radiation dose was compared using size-specific dose estimate (SSDE). Results of the three groups were compared using non-parametric tests. RESULTS: Image noise of SF-CI was 18% lower compared to SEI and 48% lower compared to SF-MI (p < 0.001). Composed images yielded higher CNRliv over single-energy images (23.4 vs. 20.9; p < 0.001), whereas CNRpv was significantly lower (3.5 vs. 5.2; p < 0.001). Monoenergetic images overcame this inferiority in CNRpv and achieved similar results compared to single-energy images (5.1 vs. 5.2; p > 0.628). Subjective sharpness was equal between single-energy and monoenergetic images and diagnostic confidence was equal between single-energy and composed images. FOMliv was highest for SF-CI. FOMpv was equal for SEI and SF-MI (p = 0.78). SSDE was significant lower for SF-DECT compared to SECT (p < 0.022). CONCLUSIONS: The combined use of split-filter dual-energy CT images provides comparable objective and subjective image quality at lower radiation dose compared to single-energy CT with ATVS. KEY POINTS: • Split-filter dual-energy results in 18% lower noise compared to single-energy with ATVS. • Split-filter dual-energy results in 11% lower SSDE compared to single-energy with ATVS. • Spectral shaping of split-filter dual-energy leads to an increased dose-efficiency.

Left atrial anatomy, atrial fibrillation burden, and P-wave duration-relationships and predictors for single-procedure success after pulmonary vein isolation.

Aims: Atrial fibrillation (AF) is associated with changes in left atrial (LA) volume, but the relationship between LA size, AF burden, and electrical conduction behaviour is still uncertain. The aim of this study was to quantify the association and impact of these parameters on the single-procedure outcome after circumferential antral ablation for pulmonary vein isolation. Methods and results: Left atrial assessment was performed in 129 consecutive patients using pre-procedural imaging in three dimensions (sphericity, indexed volume), two dimensions (diameters), and from echocardiography in one dimension (long axis). Atrial fibrillation burden was classified based on the clinical assessment as paroxysmal and persistent and based on a validated scoring system including frequency, duration of AF episodes, and number of cardioversions into four grades (minimal, mild, moderate, and severe). P-wave duration and PR interval was measured on the 12-lead electrocardiogram at the end of the procedure. Atrial fibrillation burden score (AFB) was minimal (2%), mild (75%), moderate (9%), and severe (14%) and 65% had paroxysmal and 35% had persistent AF. The recurrence rate was significantly higher in patients with persistent AF, with higher AFB, with prolonged P-wave, and with an indexed LA volume > 55 mL/m2. In multivariable analysis, AFB (hazard ratio: 2.018(1.383-2.945), P > 0.001) and a prolonged P-wave (hazard ratio: 2.612(1.248-5.466), P = 0.011) were identified as significant predictors for AF recurrence. Conclusions: In our cohort of patients with symptomatic AF, the AFB and the P-wave duration but none of the anatomical parameter revealed to be independent predictors for AF/AT recurrence after circumferential antral pulmonary vein isolation.

Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages.

OBJECTIVES: To evaluate the impact of model-based iterative reconstruction (MBIR) on image quality and low-contrast lesion detection compared with filtered back projection (FBP) in abdominal computed tomography (CT) of simulated medium and large patients at different tube voltages. METHODS: A phantom with 45 hypoattenuating lesions was placed in two water containers and scanned at 70, 80, 100, and 120 kVp. The 120-kVp protocol served as reference, and the volume CT dose index (CTDIvol) was kept constant for all protocols. The datasets were reconstructed with MBIR and FBP. Image noise and contrast-to-noise-ratio (CNR) were assessed. Low-contrast lesion detectability was evaluated by 12 radiologists. RESULTS: MBIR decreased the image noise by 24% and 27%, and increased the CNR by 30% and 29% for the medium and large phantoms, respectively. Lower tube voltages increased the CNR by 58%, 46%, and 16% at 70, 80, and 100 kVp, respectively, compared with 120 kVp in the medium phantom and by 9%, 18% and 12% in the large phantom. No significant difference in lesion detection rate was observed (medium: 79-82%; large: 57-65%; P > 0.37). CONCLUSIONS: Although MBIR improved quantitative image quality compared with FBP, it did not result in increased low-contrast lesion detection in abdominal CT at different tube voltages in simulated medium and large patients. KEY POINTS: • MBIR improved quantitative image quality but not lesion detection compared with FBP. • Increased CNR by low tube voltages did not improve lesion detection. • Changes in image noise and CNR do not directly influence diagnostic accuracy.

Automatic tube current modulation for whole-body polytrauma CT with immobilization devices: is there an increase in radiation dose and degradation of image quality?

The objective of this study was the assessment of the image quality and radiation dose in polytrauma CT using immobilization devices. An anthropomorphic whole body and a liver phantom were scanned on a 128-slice CT scanner with four different protocols using automatic tube current modulation (120 kVp, 150 ref. mAs; 120 kV, 200 ref. mAs; 140 kVp, 150 ref. mAs; and 140 kVp, 200 ref. mAs) and four different setups (no immobilization device (setup A), vacuum mattress 1 (setup B), vacuum mattress 2 (setup C), and spineboard (setup D)). Qualitative and quantitative image quality parameters and radiation dose were assessed. Image noise increased on average by 6.6, 11.2, and 9.4 %, and CNR decreased by 11.2, 13.9, and 6.5 for setups B, C, and D, respectively, compared with setup A. The CTDIvol increased up to 6 % using immobilization devices. Severe streak artifacts, provoked by the inflation valve of the mattresses were detected at the level of the head and shoulder. Applying immobilization devices for whole-body CT with automatic tube current modulation increases the radiation dose and decreases the quantitative image quality slightly. Severe artifacts, induced by the inflation valve of the mattress, can influence the diagnostic accuracy at the level of the head and shoulder.

IVIM DW-MRI of autoimmune pancreatitis: therapy monitoring and differentiation from pancreatic cancer.

OBJECTIVES: To evaluate IVIM DW-MRI for changes in IVIM-derived parameters during steroid treatment of autoimmune pancreatitis (AIP) and for the differentiation from pancreatic cancer (PC). METHODS: Fifteen AIP-patients, 11 healthy patients and 20 PC-patients were examined with DWI-MRI using eight b-values (50, 100, 150, 200, 300, 400, 600, 800). 12 AIP-patients underwent follow-up examinations during treatment. IVIM-parameters and ADC800-values were tested for significant differences and an ROC analysis was performed. RESULTS: The perfusion fraction f was significantly lower in patients with AIP at the time of diagnosis (10.5 ± 4.3 %) than in patients without AIP (20.7 ± 4.3 %). In AIP follow-up, f increased significantly to 17.1 ± 7.0 % in the first and 21.0 ± 4.1 % in the second follow up. In PC, the f-values were lower (8.2 ± 4.0 %, n.s.) compared to initial AIP and were significantly lower compared to first and second follow-up examination. In the ROC-analysis AUC-values for f were 0.63, 0.88 and 0.98 for differentiation of PC from initial, first and second follow up AIP-examination. CONCLUSIONS: The found differences in f between AIP, AIP during steroid treatment and pancreatic cancer suggest that IVIM-diffusion MRI could serve as imaging biomarker during treatment in AIP-patients and as a helpful tool for differentiation between PC and AIP. KEY POINTS: • MRI is used for follow-up examinations during therapy in AIP-patients • IVIM-DWI-MRI offers parameters which reflect perfusion and true diffusion • IVIM-parameters are helpful for differentiation between AIP and pancreatic cancer • IVIM-parameters could serve as an imaging biomarker during steroid treatment.

Neuroanatomical Markers of Neurological Soft Signs in Recent-Onset Schizophrenia and Asperger-Syndrome.

Neurological soft signs (NSS) are frequently found in psychiatric disorders of significant neurodevelopmental origin. Previous MRI studies in schizophrenia have shown that NSS are associated with abnormal cortical, thalamic and cerebellar structure and function. So far, however, no neuroimaging studies investigated brain correlates of NSS in individuals with Asperger-Syndrome (AS) and the question whether the two disorders exhibit common or disease-specific cortical correlates of NSS remains unresolved. High-resolution MRI data at 3 T were obtained from 48 demographically matched individuals (16 schizophrenia patients, 16 subjects with AS and 16 healthy individuals). The surface-based analysis via Freesurfer enabled calculation of cortical thickness, area and folding (local gyrification index, LGI). NSS were examined on the Heidelberg Scale and related to cortical measures. In schizophrenia, higher NSS were associated with reduced cortical thickness and LGI in fronto-temporo-parietal brain areas. In AS, higher NSS were associated with increased frontotemporal cortical thickness. This study lends further support to the hypothesis that disorder-specific mechanisms contribute to NSS expression in schizophrenia and AS. Pointing towards dissociable neural patterns may help deconstruct the complex processes underlying NSS in these neurodevelopmental disorders.