Standardization of a CT Protocol for Imaging Patients with Suspected COVID-19-A RACOON Project.

CT protocols that diagnose COVID-19 vary in regard to the associated radiation exposure and the desired image quality (IQ). This study aims to evaluate CT protocols of hospitals participating in the RACOON (Radiological Cooperative Network) project, consolidating CT protocols to provide recommendations and strategies for future pandemics. In this retrospective study, CT acquisitions of COVID-19 patients scanned between March 2020 and October 2020 (RACOON phase 1) were included, and all non-contrast protocols were evaluated. For this purpose, CT protocol parameters, IQ ratings, radiation exposure (CTDIvol), and central patient diameters were sampled. Eventually, the data from 14 sites and 534 CT acquisitions were analyzed. IQ was rated good for 81% of the evaluated examinations. Motion, beam-hardening artefacts, or image noise were reasons for a suboptimal IQ. The tube potential ranged between 80 and 140 kVp, with the majority between 100 and 120 kVp. CTDIvol was 3.7 ± 3.4 mGy. Most healthcare facilities included did not have a specific non-contrast CT protocol. Furthermore, CT protocols for chest imaging varied in their settings and radiation exposure. In future, it will be necessary to make recommendations regarding the required IQ and protocol parameters for the majority of CT scanners to enable comparable IQ as well as radiation exposure for different sites but identical diagnostic questions.

[Is JAK inhibition an option in the treatment of interstitial lung disease in rheumatoid arthritis?] / Ist JAK-Hemmung eine Option in der Behandlung der interstitiellen Lungenerkrankung bei einer rheumatoiden Arthritis?

A 69-year-old male patient with seropositive erosive rheumatoid arthritis (RA) presented to our clinic due to progressive dyspnea. High-resolution computed tomography (HRCT) and immunological bronchioalveolar lavage revealed ground-glass opacities and a lymphocytic alveolitis caused by interstitial lung disease (ILD) in RA. Considering previous forms of treatment, disease-modifying antirheumatic drug (DMARD) treatment was switched to tofacitinib. Tofacitinib treatment demonstrated a 33% reduction in ground-glass opacities by artificial intelligence-based quantification of pulmonary HRCT over the course of 6 months, which was associated with an improvement in dyspnea symptoms. In conclusion, tofacitinib represents an effective anti-inflammatory therapeutic option in the treatment of RA-ILD.

Added value of chest CT in a machine learning-based prediction model to rule out COVID-19 before inpatient admission: A retrospective university network study.

PURPOSE: During the coronavirus disease 2019 (COVID-19) pandemic, hospitals still face the challenge of timely identification of infected individuals before inpatient admission. An artificial intelligence approach based on an established clinical network may improve prospective pandemic preparedness. METHOD: Supervised machine learning was used to construct diagnostic models to predict COVID-19. A pooled database was retrospectively generated from 4437 participant data that were collected between January 2017 and October 2020 at 12 German centers that belong to the radiological cooperative network of the COVID-19 (RACOON) consortium. A total of 692 (15.6 %) participants were COVID-19 positive according to the reference of the reverse transcription-polymerase chain reaction test. The diagnostic models included chest CT features (model R), clinical examination and laboratory test features (model CL), or all three feature categories (model RCL). Performance outcomes included accuracy, sensitivity, specificity, negative and positive predictive value, and area under the receiver operating curve (AUC). RESULTS: Performance of predictive models improved significantly by adding chest CT features to clinical evaluation and laboratory test features. Without (model CL) and with inclusion of chest CT (model RCL), sensitivity was 0.82 and 0.89 (p < 0.0001), specificity was 0.84 and 0.89 (p < 0.0001), negative predictive value was 0.96 and 0.97 (p < 0.0001), AUC was 0.92 and 0.95 (p < 0.0001), and proportion of false negative classifications was 2.6 % and 1.7 % (p < 0.0001), respectively. CONCLUSIONS: Addition of chest CT features to machine learning-based predictive models improves the effectiveness in ruling out COVID-19 before inpatient admission to regular wards.

Artificial intelligence for assistance of radiology residents in chest CT evaluation for COVID-19 pneumonia: a comparative diagnostic accuracy study.

BACKGROUND: In hospitals, it is crucial to rule out coronavirus disease 2019 (COVID-19) timely and reliably. Artificial intelligence (AI) provides sufficient accuracy to identify chest computed tomography (CT) scans with signs of COVID-19. PURPOSE: To compare the diagnostic accuracy of radiologists with different levels of experience with and without assistance of AI in CT evaluation for COVID-19 pneumonia and to develop an optimized diagnostic pathway. MATERIAL AND METHODS: The retrospective, single-center, comparative case-control study included 160 consecutive participants who had undergone chest CT scan between March 2020 and May 2021 without or with confirmed diagnosis of COVID-19 pneumonia in a ratio of 1:3. Index tests were chest CT evaluation by five radiological senior residents, five junior residents, and an AI software. Based on the diagnostic accuracy in every group and on comparison of groups, a sequential CT assessment pathway was developed. RESULTS: Areas under receiver operating curves were 0.95 (95% confidence interval [CI]=0.88-0.99), 0.96 (95% CI=0.92-1.0), 0.77 (95% CI=0.68-0.86), and 0.95 (95% CI=0.9-1.0) for junior residents, senior residents, AI, and sequential CT assessment, respectively. Proportions of false negatives were 9%, 3%, 17%, and 2%, respectively. With the developed diagnostic pathway, junior residents evaluated all CT scans with the support of AI. Senior residents were only required as second readers in 26% (41/160) of the CT scans. CONCLUSION: AI can support junior residents with chest CT evaluation for COVID-19 and reduce the workload of senior residents. A review of selected CT scans by senior residents is mandatory.

Quality measures for fully automatic CT histogram-based fat estimation on a corpse sample.

In a previous article a new algorithm for fully automatic 'CT histogram based Fat Estimation and quasi-Segmentation' (CFES) was validated on synthetic data, on a special CT phantom, and tested on one corpse. Usage of said data in FE-modelling for temperature-based death time estimation is the investigation's number one long-term goal. The article presents CFES's results on a human corpse sample of size R = 32, evaluating three different performance measures: the τ-value, measuring the ability to differentiate fat from muscle, the anatomical fat-muscle misclassification rate D, and the weighted distance S between the empirical and the theoretical grey-scale value histogram. CFES-performance on the sample was: D = 3.6% for weight exponent α = 1, slightly higher for α ≥ 2 and much higher for α ≤ 0. Investigating τ, S and D on the sample revealed some unexpected results: While large values of τ imply small D-values, rising S implies falling D and there is a positive linear relationship between τ and S. The latter two findings seem to be counter-intuitive. Our Monte Carlo analysis detected a general umbrella type relation between τ and S, which seems to stem from a pivotal problem in fitting Normal mixture distributions.

CT-based thermometry with virtual monoenergetic images by dual-energy of fat, muscle and bone using FBP, iterative and deep learning-based reconstruction.

OBJECTIVES: The aim of this study was to evaluate the sensitivity of CT-based thermometry for clinical applications regarding a three-component tissue phantom of fat, muscle and bone. Virtual monoenergetic images (VMI) by dual-energy measurements and conventional polychromatic 120-kVp images with modern reconstruction algorithms adaptive statistical iterative reconstruction-Volume (ASIR-V) and deep learning image reconstruction (DLIR) were compared. METHODS: A temperature-regulating water circuit system was developed for the systematic evaluation of the correlation between temperature and Hounsfield units (HU). The measurements were performed on a Revolution CT with gemstone spectral imaging technology (GSI). Complementary measurements were performed without GSI (voltage 120 kVp, current 130-545 mA). The measured object was a tissue equivalent phantom in a temperature range of 18 to 50°C. The evaluation was carried out for VMI at 40 to 140 keV and polychromatic 120-kVp images. RESULTS: The regression analysis showed a significant inverse linear dependency between temperature and average HU regardless of ASIR-V and DLIR. VMI show a higher temperature sensitivity compared to polychromatic images. The temperature sensitivities were 1.25 HU/°C (120 kVp) and 1.35 HU/°C (VMI at 140 keV) for fat, 0.38 HU/°C (120 kVp) and 0.47 HU/°C (VMI at 40 keV) for muscle and 1.15 HU/°C (120 kVp) and 3.58 HU/°C (VMI at 50 keV) for bone. CONCLUSIONS: Dual-energy with VMI enables a higher temperature sensitivity for fat, muscle and bone. The reconstruction with ASIR-V and DLIR has no significant influence on CT-based thermometry, which opens up the potential of drastic dose reductions. KEY POINTS: • Virtual monoenergetic images (VMI) enable a higher temperature sensitivity for fat (8%), muscle (24%) and bone (211%) compared to conventional polychromatic 120-kVp images. • With VMI, there are parameters, e.g. monoenergy and reconstruction kernel, to modulate the temperature sensitivity. In contrast, there are no parameters to influence the temperature sensitivity for conventional polychromatic 120-kVp images. • The application of adaptive statistical iterative reconstruction-Volume (ASIR-V) and deep learning-based image reconstruction (DLIR) has no effect on CT-based thermometry, opening up the potential of drastic dose reductions in clinical applications.

Development of a balanced scorecard as a strategic performance measurement system for clinical radiology as a cost center.

OBJECTIVES: To develop a goal-oriented indicator system based on the balanced scorecard (BSC) concept, which takes into account the perspectives of the referring physician and patient and emphasizes the focus on the internal processes of the radiology department. METHODS: Development of a BSC occurred in six steps: (Step 1) strengths/weaknesses and opportunities/risks (SWOT-) analysis of the radiology department, (Step 2) setting-specific objectives (model, core values, key objective) followed by the development of 4 perspectives, (Step 3) and definition of strategic issues oriented to the value-added chain of the processes of the radiology department. (Step 4) Creation of a "Strategy Map" with regard to the perspective and their cause-effect relationships. (Step 5) Development of an automated key performance indicator (KPI) cockpit for the monitoring, reporting, and management scorecard. RESULTS: A total of 10 success factors were identified using SWOT analysis. The core values include high quality in clinical, teaching, and research areas. The radiological value-added chain is composed of three processing steps. 1. registration, 2. examination, and 3. reading/X-ray demonstration. Three action programs were derived: 1. increase competency (e.g., specialist standard), 2. improve referring physician/patient satisfaction, 3. increase productivity. Daily process monitoring was added to the management cockpit as a monitoring scorecard. The scorecard comprises 18 KPIs and is automatically updated every month. The annual management scorecard comprises 10 KPIs. CONCLUSIONS: The BSC makes it possible to implement a strategy for radiology that is strongly oriented toward the requirements of the referring physicians and the demands of patients.

MRI following scoliosis surgery? An analysis of implant heating, displacement, torque, and susceptibility artifacts.

OBJECTIVES: The implant constructs used in scoliosis surgery are often long with a high screw density. Therefore, it is generally believed that magnetic resonance imaging (MRI) should not be carried out after scoliosis surgery, with the result that computed tomography is often preferred despite the ionizing radiation involved. The objective of this study was to evaluate the MRI compatibility of long pedicle-screw-rod constructs at 1.5 T and 3 T using standardized methods of the American Society for Testing and Materials (ASTM). METHODS: Constructs between 130 and 430 mm long were systematically examined according to the ASTM standards F2182 (radio frequency-induced heating), F2119 (susceptibility artifacts), F2213 (magnetically induced torque), and F2052 (magnetically induced displacement force). RESULTS: The maximum heating in the magnetic field was 1.3 K. Heating was significantly influenced by magnetic field strength (p < 0.001), implant length (p = 0.048), and presence of cross-links (p = 0.001). The maximum artifact width for different lengths of the anatomically bent titanium rods with CoCr alloy ranged between 14.77 ± 2.93 mm (TSE) and 17.49 ± 1.82 mm (GRE) for 1.5 T and between 23.67 ± 2.39 mm (TSE) and 27.77 ± 2.37 mm (GRE) for 3 T. TiCP and TiAl showed the smallest and CoCr and CoCr Plus the largest artifact widths. The magnetically induced torque and displacement force were negligible. CONCLUSIONS: MRI following scoliosis surgery with long implant constructs is safe with the patient in supine position. Although susceptibility artifacts can severely limit the diagnostic value, the examination of other regions is possible. KEY POINTS: • Large spinal implants are not necessarily a contraindication for MRI; MR conditional status can be examined according to the ASTM standards F2182, F2119, F2213, and F2052. • A metallic pedicle-screw-rod system could be reliably and safely examined in all combinations of length (130 to 430 mm), configuration, and material in a B0 at 1.5 T and 3 T. • According to ASTM F2503, the examined pedicle-screw-rod system is MR conditional and especially the young patients can benefit from a non-ionizing radiation MRI examination.

Deep Learning CT Image Reconstruction in Clinical Practice.

BACKGROUND: Computed tomography (CT) is a central modality in modern radiology contributing to diagnostic medicine in almost every medical subspecialty, but particularly in emergency services. To solve the inverse problem of reconstructing anatomical slice images from the raw output the scanner measures, several methods have been developed, with filtered back projection (FBP) and iterative reconstruction (IR) subsequently providing criterion standards. Currently there are new approaches to reconstruction in the field of artificial intelligence utilizing the upcoming possibilities of machine learning (ML), or more specifically, deep learning (DL). METHOD: This review covers the principles of present CT image reconstruction as well as the basic concepts of DL and its implementation in reconstruction. Subsequently commercially available algorithms and current limitations are being discussed. RESULTS AND CONCLUSION: DL is an ML method that utilizes a trained artificial neural network to solve specific problems. Currently two vendors are providing DL image reconstruction algorithms for the clinical routine. For these algorithms, a decrease in image noise and an increase in overall image quality that could potentially facilitate the diagnostic confidence in lesion conspicuity or may translate to dose reduction for given clinical tasks have been shown. One study showed equal diagnostic accuracy in the detection of coronary artery stenosis for DL reconstructed images compared to IR at higher image quality levels. Consequently, a lot more research is necessary and should aim at diagnostic superiority in the clinical context covering a broadness of pathologies to demonstrate the reliability of such DL approaches. KEY POINTS: · Following iterative reconstruction, there is a new approach to CT image reconstruction in the clinical routine using deep learning (DL) as a method of artificial intelligence.. · DL image reconstruction algorithms decrease image noise, improve image quality, and have potential to reduce radiation dose.. · Diagnostic superiority in the clinical context should be demonstrated in future trials.. CITATION FORMAT: · Arndt C, Güttler F, Heinrich A et al. Deep Learning CT Image Reconstruction in Clinical Practice. Fortschr Röntgenstr 2021; 193: 252 - 261.

Systematic evaluation of particle loss during handling in the percutaneous transluminal angioplasty for eight different drug-coated balloons.

Paclitaxel drug coated balloons (DCBs) should provide optimal drug transfer exclusively to the target tissue. The aim of this study was to evaluate the particle loss by handling during angioplasty. A robotic arm was developed for systematic and reproducible drug abrasion experiments. The contact force on eight different commercially available DCB types was gradually increased, and high-resolution microscopic images of the deflated and inflated balloons were recorded. Three types of DCBs were classified: no abrasion of the drug in both statuses (deflated and inflated), significant abrasion only in the inflated status, and significant abrasion in both statuses. Quantitative measurements via image processing confirmed the qualitative classification and showed changes of the drug area between 2.25 and 45.73% (13.28 ± 14.29%) in the deflated status, and between 1.66 and 40.41% (21.43 ± 16.48%) in the inflated status. The structures and compositions of the DCBs are different, some are significantly more susceptible to drug loss. Particle loss by handling during angioplasty leads to different paclitaxel doses in the target regions for same DCB types. Susceptibility to involuntary drug loss may cause side effects, such as varying effective paclitaxel doses, which may explain variations in studies regarding the therapeutic outcome.

Automatic human identification based on dental X-ray radiographs using computer vision.

A person may be identified by comparison between ante- and post-mortem dental panoramic radiographs (DPR). However, it is difficult to find reference material if the person is unknown. This is often the case when victims of crime or mass disaster are found. Computer vision can be a helpful solution to automate the finding of reference material in a large database of images. The purpose of the present study was to improve the automated identification of unknown individuals by comparison of ante- and post-mortem DPR using computer vision. The study includes 61,545 DPRs from 33,206 patients, acquired between October 2006 and June 2018. The matching process is based on the Speeded Up Robust Features (SURF) algorithm to find unique corresponding points between two DPRs (unknown person and database entry). The number of matching points found is an indicator for identification. All 43 individuals (100%) were successfully identified by comparison with the content of the feature database. The experimental setup was designed to identify unknown persons based on their DPR using an automatic algorithm system. The proposed tool is able to filter large databases with many entries of potentially matching partners. This identification method is suitable even if dental characteristics were removed or added in the past.

Development of an Apparatus for Digital Measurement of Magnetically Induced Torque on Medical Implants to Facilitate the Application of the ASTM F2213 Standard.

OBJECTIVE: The ASTM F2213 standard describes the measurement of the magnetically induced torques by means of a torsional spring method. As this method is highly complex, it has rarely ever been employed. The purpose was to develop an easy-to-handle measuring setup for the reproducible, fast, and precise measurement of magnetically induced torques, following ASTM F2213, at a static magnetic field (B0) of 1.5 and 3 T. METHODS: An magnetic resonance (MR)-save measuring platform was developed according to the ASTM F2213 standard and combined with a precision balance. The evaluation was performed with ten test objects, e.g., a steel screw (length 60 mm) and a neurostimulator, at 1.5 and 3 T. The torque was measured at 10° increments as the test object was rotated relative to B0. RESULTS: The measurement setup allows fast and reproducible measurements. The maximum magnetically induced torque measured was 397 ± 2 N·mm for the steel screw and 63 ± 1 N·mm for the neurostimulator. The maximum torque on a test object is insensitive to B0 if the magnetization of the object has reached saturation. CONCLUSION: Ferromagnetic implants may exhibit strong magnetically induced torques during MRI examination. For analyzing these magnetically induced torques, we developed a digital measuring device that allows simplified and accelerated application of the ASTM F2213 standard test method. SIGNIFICANCE: In a B0, ferromagnetic implants may exert considerable torques on the surrounding tissue. These forces may affect the functionality of the implant and inflict severe injuries on a patient.

Forensic Odontology: Automatic Identification of Persons Comparing Antemortem and Postmortem Panoramic Radiographs Using Computer Vision.

PURPOSE: In forensic odontology the comparison between antemortem and postmortem panoramic radiographs (PRs) is a reliable method for person identification. The purpose of this study was to improve and automate identification of unknown people by comparison between antemortem and postmortem PR using computer vision. MATERIALS AND METHODS: The study includes 43 467 PRs from 24 545 patients (46 % females/54 % males). All PRs were filtered and evaluated with Matlab R2014b including the toolboxes image processing and computer vision system. The matching process used the SURF feature to find the corresponding points between two PRs (unknown person and database entry) out of the whole database. RESULTS: From 40 randomly selected persons, 34 persons (85 %) could be reliably identified by corresponding PR matching points between an already existing scan in the database and the most recent PR. The systematic matching yielded a maximum of 259 points for a successful identification between two different PRs of the same person and a maximum of 12 corresponding matching points for other non-identical persons in the database. Hence 12 matching points are the threshold for reliable assignment. CONCLUSION: Operating with an automatic PR system and computer vision could be a successful and reliable tool for identification purposes. The applied method distinguishes itself by virtue of its fast and reliable identification of persons by PR. This Identification method is suitable even if dental characteristics were removed or added in the past. The system seems to be robust for large amounts of data. KEY POINTS: · Computer vision allows an automated antemortem and postmortem comparison of panoramic radiographs (PRs) for person identification.. · The present method is able to find identical matching partners among huge datasets (big data) in a short computing time.. · The identification method is suitable even if dental characteristics were removed or added.. CITATION FORMAT: · Heinrich A, Güttler F, Wendt S et al. Forensic Odontology: Automatic Identification of Persons Comparing Antemortem and Postmortem Panoramic Radiographs Using Computer Vision. Fortschr Röntgenstr 2018; 190: 1152 - 1158.

Fully automatic CT-histogram-based fat estimation in dead bodies.

Post-mortem body cooling is the foundation of temperature-based death time estimations (TDE) in homicide cases. Forensic science generally provides two types of p.m. body cooling models, the phenomenological and the physical models. Since both of them have to implement important individual parameters like the quantity of abdominal fat explicitly or implicitly, a more exact quantification and localization of abdominal fat is a desideratum in TDE. Particularly for the physical models, a better knowledge of the abdominal fat distribution could lead to relevant improvements in TDEs. Modern imaging methods in medicine like computed tomography (CT) are opening up the possibility to register the quantity and spatial distribution of body fat in individual cases with unprecedented precision. Since a CT-scan of an individual's abdominal region can comprise 1000 slices as an order of magnitude, it is evident that their evaluation for body fat quantification and localization needs fully automated algorithms. The paper at hand describes the development and validation of such an algorithm called "CT-histogram-based fat estimation and quasi-segmentation" (CFES). The approach can be characterized as a weighted least squares method dealing with the gray value histogram of single CT-slices only. It does not require any anatomical a priori information nor does it perform time-consuming feature detection on the CT-images. The processing result consists in numbers quantifying the amount of abdominal body fat and of muscle-, organ-, and connective tissue. As a by-product, CFES generates a quasi-segmentation of the slices processed differentiating fat from muscle-, organ-, and connective tissue. The tool is validated on synthetic data and on CT-data of a special phantom. It was also applied on a CT-scan of a dead body, where it produced anatomically plausible results.

Automatic CT-based finite element model generation for temperature-based death time estimation: feasibility study and sensitivity analysis.

Temperature-based death time estimation is based either on simple phenomenological models of corpse cooling or on detailed physical heat transfer models. The latter are much more complex but allow a higher accuracy of death time estimation, as in principle, all relevant cooling mechanisms can be taken into account.Here, a complete workflow for finite element-based cooling simulation is presented. The following steps are demonstrated on a CT phantom: Computer tomography (CT) scan Segmentation of the CT images for thermodynamically relevant features of individual geometries and compilation in a geometric computer-aided design (CAD) model Conversion of the segmentation result into a finite element (FE) simulation model Computation of the model cooling curve (MOD) Calculation of the cooling time (CTE) For the first time in FE-based cooling time estimation, the steps from the CT image over segmentation to FE model generation are performed semi-automatically. The cooling time calculation results are compared to cooling measurements performed on the phantoms under controlled conditions. In this context, the method is validated using a CT phantom. Some of the phantoms' thermodynamic material parameters had to be determined via independent experiments.Moreover, the impact of geometry and material parameter uncertainties on the estimated cooling time is investigated by a sensitivity analysis.

In vitro stent assessment by MRI: visibility of lumen and artifacts for 27 modern stents.

PURPOSE: The purpose of this study was to measure artifacts and visibility of lumen for modern and most commonly used stents in vascular interventions according to a standardized test method of the American Society for Testing and Materials (ASTM). MATERIALS AND METHODS: Twenty-four peripheral self-expanding nitinol stents and three stainless steel stents with diameters between 5 and 8 mm and lengths between 30 and 250 mm from seven different manufacturers were compared on a 1.5T and a 3T magnetic resonance (MR) scanner. The visualization of lumen and artifacts was measured according to ASTM F2119 for a turbo spin echo (TSE) [repetition time(TR)/echo time (TE) 500/26 ms] and a gradient echo (GRE) (TR/TE 100/15 ms) sequence. The stents were placed parallel and perpendicular to the radio frequency field (B1). RESULTS: There were large differences in visibility of the lumen for the stent models. The visualization of the lumen varies between 0% and 93% (perpendicular to B1), and 0% and 78% (parallel to B1), respectively. The maximum signal loss beyond the actual diameter was 6 mm (TSE) and 10 mm (GRE) for stents made of stainless steel, and lower than 1 mm (TSE) and 4 mm (GRE) for nitinol stents. CONCLUSION: Reliable stent lumen visualization is possible for Misago, Supera, Tigris, and Viabahn stents, if their axis is perpendicular to B1, and independent of the orientation with respect to B1 for short Tigris stents at 1.5T.

Evaluating fetal head dimension changes during labor using open magnetic resonance imaging.

AIM: Fetal skull molding is important for the adaptation of the head to the birth canal during vaginal delivery. Importantly, the fetal head must rotate around the maternal symphysis pubis. The goals of this analysis were to observe a human birth in real-time using an open magnetic resonance imaging (MRI) scanner and describe the fetal head configuration during expulsion. METHODS: Real-time cinematic MRI series (TSE single-shot sequence, TR 1600 ms, TE 150 ms) were acquired from the midsagittal plane of the maternal pelvis during the active second stage of labor at 37 weeks of gestation. Frame-by-frame analyses were performed to measure the frontooccipital diameter (FOD) and distance from the vertex to the base of the fetal skull. RESULTS: During vaginal delivery in an occiput anterior position, the initial FOD was 10.3 cm. When expulsion began, the fetal skull was deformed and elongated, with the FOD increasing to 10.8 cm and 11.2 cm at crowning. In contrast, the distance from the vertex to the base of the skull was reduced from 6.4 cm to 5.6 cm at expulsion. CONCLUSIONS: Fetal head molding is the change in the fetal head due to the forces of labor. The biomechanics of this process are poorly understood. Our visualization of the normal mechanism of late second-stage labor shows that MRI technology can for the first time help define the changes in the diameters of the fetal head during active labor.

Initial experience with imaging of the lower extremity arteries in an open 1.0 Tesla MRI system using the triggered angiography non-contrast-enhanced sequence (TRANCE) compared to digital subtraction angiography (DSA).

PURPOSE: The aim of this study was to evaluate the feasibility and validity of arterial lower limb imaging with triggered angiography non-contrast enhanced (TRANCE) in an open MRI at 1.0 Tesla (T) compared to digital subtraction angiography (DSA). MATERIAL AND METHODS: ECG-gated, non-contrast-enhanced magnetic resonance angiography (MRA) was performed in a 1.0-T high-field open magnetic resonance imaging (MRI) system which generates a vertical magnetic field. Three acquisition levels were defined (abdominal and pelvic level, arterial segments above the knee and segments below the knee) and a total of 1782 vessel diameter measurements were taken on a total of 11 patients with suspected peripheral arterial occlusive disease (PAOD) (8 men, 3 women; average age 66 years). In each patient, 162 vessel segments (81 each with TRANCE and DSA) were defined and measured. Pearson correlation coefficients were calculated. RESULTS: At the abdominal/pelvic level, all mean values measured with DSA exceeded the mean values obtained with TRANCE. Above the knee, mean vessel diameters were measured smaller in DSA in six, equal in three, and larger in two vessel segments. Below the knee, all measured averages, except for the tibiofibular tract (TFT) measurements, were larger in TRANCE. In total, two small (≤0.3), two moderate (>0.3), 11 good (>0.5), 10 high (>0.7) and 13 very high (>0.8) correlations were obtained. CONCLUSIONS: Non-contrast-enhanced imaging of the lower limb arteries using a TRANCE-sequence in a 1.0 T open MRI system is feasible with the protocol presented; however, TRANCE tends to underestimate larger vessels and overestimate smaller vessels compared to DSA.

Measurement of susceptibility artifacts with histogram-based reference value on magnetic resonance images according to standard ASTM F2119.

OBJECTIVE: The standard ASTM F2119 describes a test method for measuring the size of a susceptibility artifact based on the example of a passive implant. A pixel in an image is considered to be a part of an image artifact if the intensity is changed by at least 30% in the presence of a test object, compared to a reference image in which the test object is absent (reference value). The aim of this paper is to simplify and accelerate the test method using a histogram-based reference value. MATERIALS AND METHODS: Four test objects were scanned parallel and perpendicular to the main magnetic field, and the largest susceptibility artifacts were measured using two methods of reference value determination (reference image-based and histogram-based reference value). The results between both methods were compared using the Mann-Whitney U-test. RESULTS: The difference between both reference values was 42.35 ± 23.66. The difference of artifact size was 0.64 ± 0.69 mm. The artifact sizes of both methods did not show significant differences; the p-value of the Mann-Whitney U-test was between 0.710 and 0.521. CONCLUSIONS: A standard-conform method for a rapid, objective, and reproducible evaluation of susceptibility artifacts could be implemented. The result of the histogram-based method does not significantly differ from the ASTM-conform method.