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Analyzing night lights images allows identifying and monitoring affected areas during conflicts, as an overall decrease in brightness indicates abandoned or destroyed buildings, curfews, or power shortages. While detecting decrease in lights intensity is an established technique to identify critical areas during conflicts, the causes of appearing bright lights have not been extensively studied. An increase in brightness may be related to extreme events, such as fires resulting from bombing. This paper shows several examples supporting this new use of these images, validated using high-resolution optical earth observation data and ancillary information sources in the frame of the conflict in Ukraine which has been ongoing since February 2022.

Every year, numerous major events with thousands of participants take place around the world, for which extensive safety measures are taken to ensure the well-being and safety of visitors. Nevertheless, incidents that lead to injuries and deaths occur time and again. This year, Germany is looking forward to the UEFA EURO 2024, an international football championship for which the host country, Germany, has made extensive preparations on several levels. One component of this is the Medical Task Force (MTF) of the Federal Office of Civil Protection and Disaster Assistance (BBK), which has trained a possible medical service deployment in the event of a mass casualty incident with a specially coordinated series of exercises. The extent to which current remote sensing data can make a useful contribution and be meaningfully integrated was analyzed during the large-scale EUROMED exercise with the help of an airborne camera system on board a helicopter. The system provided up-to-date aerial images of the exercise site, but was also used to record the traffic situation during the transfer of the vehicle convoys to the exercise site. In addition, state-of-the-art AI-based image analysis methods were tested on site and the results were provided and evaluated. An important operational exercise to bring science and practice together step by step in order to be better prepared for an emergency.

This paper focuses on person detection in aerial and drone imagery, which is crucial for various operations such as situational awareness, search and rescue, and safe delivery of supplies. We aim to improve disaster response efforts by enhancing the speed, safety, and effectiveness of the process. Therefore, we introduce a new person detection dataset comprising 311 annotated aerial and drone images, acquired from helicopters and drones in different scenes, including urban and rural areas, and for different scenarios, such as estimation of damage in disaster-affected zones, and search and rescue operations in different countries. The amount of data considered and level of detail of the annotations resulted in a total of 10,050 annotated persons. To detect people in aerial and drone images, we propose a multi-stage training procedure to improve YOLOv3’s ability. The proposed procedure aims at addressing challenges such as variations in scenes, scenarios, people poses, as well as image scales and viewing angles. To evaluate the effectiveness of our proposed training procedure, we split our dataset into a training and a test set. The latter includes images acquired during real search and rescue exercises and operations, and is therefore representative for the challenges encountered during operational missions and suitable for an accurate assessment of the proposed models. Experimental results demonstrate the effectiveness of our proposed training procedure, as the model’s average precision exhibits a relevant increase with respect to the baseline value.

In the early morning hours on Tuesday, February 6, 2023, the southern part of Türkiye was struck by two large and several smaller earthquakes, causing destruction and casualties over a remarkably large area. In such cases, quick response and well-informed coordination is a key factor to successful first aid responses since damage and the number of people buried or in need often remain unclear in the hours after the disaster. The German Aerospace Center (DLR) responded to the earthquake by rapidly providing a number of information products, all above very high-resolution imagery in an easy-to-use web-based application. Next to satellite and drone imagery, damage information and pre-disaster imagery were provided to the users. Drone imagery was acquired in person for Kirikhan, a city in the south of the disaster area. Access to the viewer was granted to authorized users from public authorities, humanitarian aid organisations, and research institutes. Furthermore, DLR generated information products in the fields of settlement pattern, AI based damage assessment and tectonic movements. These data, as scientifically significant as they are, were not part of the web viewer. Within this paper, the reasons will be assessed as well as the general workflow of the activation. The paper will also discuss what steps need to be taken to ensure research outcomes being integrated into information products for users in future and how to prepare for the next disaster to still ensure a quick response but with an enriched product suite.

Highly accurate reference vehicle trajectories are required in the automotive domain e. g. for testing mobile GNSS devices. Common methods used to determine reference trajectories are based on the same working principles as the device under test and suffer from the same underlying error problems. In this paper, a new method to generate reference vehicle trajectories in real-world situations using simultaneously acquired aerial imagery from a helicopter is presented. This method requires independent height information which is coming from a LIDAR DTM and the relative height of the GNSS device. The reference trajectory is then derived by forward intersection of the vehicle position in each image with the DTM. In this context, the influence of all relevant error sources were analysed, like the error from the LIDAR DTM, from the sensor latency, from the semi-automatic matching of the vehicle marking, and from the image orientation. Results show that the presented method provides a tool for creating reference trajectories that is independent of the GNSS reception at the vehicle. Moreover, it can be demonstrated that the proposed method reaches an accuracy level of 10 cm, which is defined as necessary for certification and validation of automotive GNSS devices.

Terrestrial sensors are commonly used to inspect and document the condition of roads at regular intervals and according to defined rules. For example in Germany, extensive data and information is obtained, which is stored in the Federal Road Information System and made available in particular for deriving necessary decisions. Transverse and longitudinal evenness, for example, are recorded by vehicles using laser techniques. To detect damage to the road surface, images are captured and recorded using area or line scan cameras. All these methods provide very accurate information about the condition of the road, but are time-consuming and costly. Aerial imagery (e.g. multi- or hyperspectral, SAR) provide an additional possibility for the acquisition of the specific parameters describing the condition of roads, yet a direct transfer from objects extractable from aerial imagery to the required objects or parameters, which determine the condition of the road is difficult and in some cases impossible. In this work, we investigate the transferability of objects commonly used for the terrestrial-based assessment of road surfaces to an aerial image-based assessment. In addition, we generated a suitable dataset and developed a deep learning based image segmentation method capable of extracting two relevant road condition parameters from high-resolution multispectral aerial imagery, namely cracks and working seams. The obtained results show that our models are able to extraction these thin features from aerial images, indicating the possibility of using more automated approaches for road surface condition assessment in the future.

The management of large-scale events with a widely distributed camping area is a special challenge for organisers and security forces and requires both comprehensive preparation and attentive monitoring to ensure the safety of the participants. Crucial to this is the availability of up-to-date situational information, e.g. from remote sensing data. In particular, information on the number and distribution of people is important in the event of a crisis in order to be able to react quickly and effectively manage the corresponding rescue and supply logistics. One way to estimate the number of persons especially at night is to classify the type and size of objects such as tents and vehicles on site and to distinguish between objects with and without a sleeping function. In order to make this information available in a timely manner, an automated situation assessment is required. In this work, we have prepared the first high-quality dataset in order to address the aforementioned challenge which contains aerial images over a large-scale festival of different dates. We investigate the feasibility of this task using Convolutional Neural Networks for instance-wise semantic segmentation and carry out several experiments using the Mask-RCNN algorithm and evaluate the results. Results are promising and indicate the possibility of function-based tent classification as a proof-of-concept. The results and thereof discussions can pave the way for future developments and investigations.

Emerging traffic management technologies, smart parking applications, together with transport researchers and urban planners are interested in fine-grained data on parking space in cities. However, there are no standardized, complete and up-to-date databases for many urban areas. Moreover, manual data collection is expensive and time-consuming. Aerial imagery of entire cities can be used to inventory not only publicly accessible and dedicated parking lots, but also roadside parking areas and those on private property. For a realistic estimation of the total parking space, the observed use of multi-functional traffic areas is taken into account by segmenting not only parking areas but also roads according to their purpose. In this paper, different U-Net based architectures are tested for detecting all these types of visible traffic areas. A new large-scale, high-quality dataset of manual annotations is used in combination with selected contextual information from OpenStreetMap (OSM) to depict the actual use as parking space. Our models achieve a good performance on parking area segmentation, and we show the significant impact of OSM data fusion in deep neural networks on the simultaneous extraction of multiple traffic areas compared to using aerial imagery alone.

After a natural disaster or humanitarian crisis, rescue forces and relief organisations are dependent on fast, area-wide and accurate information on the damage caused to infrastructure and the situation on the ground. This study focuses on the assessment of building damage levels on optical satellite imagery with a two-step ensemble model performing building segmentation and damage classification trained on a public dataset. We provide an extensive generalization study on pre- and post-disaster data from the passage of the cyclone Idai over Beira, Mozambique, in 2019 and the explosion in Beirut, Lebanon, in 2020. Critical challenges are addressed, including the detection of clustered buildings with uncommon visual appearances, the classification of damage levels by both humans and deep learning models, and the impact of varying imagery acquisition conditions. We show promising building damage assessment results and highlight the strong performance impact of data pre-processing on the generalization capability of deep convolutional models.

Background Fatty masses are common and may be encountered in the foot and ankle. In some cases, normal subcutaneous fat may be mistaken for a discrete mass. Aims The aim of this study was to evaluate the common finding of prominent subcutaneous fat at the medial midfoot resembling a lipoma and to determine the prevalence of this pseudolesion by applying a series of potential size cutoff criteria. Materials and Methods Three musculoskeletal radiologists retrospectively evaluated 91 sequentially performed magnetic resonance imaging examinations of the ankle to measure fat resembling a discrete lipoma at the medial midfoot. Each blinded reader measured the largest area of continuous subcutaneous fat in orthogonal axial, coronal craniocaudal, and coronal transverse dimensions. Patient age, sex, and study indications were also recorded. Statistical analysis was performed with R and SAS 9.4 software Results A discrete fatty pseudolesion as defined by measuring at least 1 cm in all planes by measurements of at least two of three readers was present in 87% of cases (79 of 91). When a size criterion of 1.5 cm was used, a pseudolesion was documented in 14% of cases (13 of 91). There was a significant correlation between larger pseudolesion size and female sex in the axial plane; however, there was no correlation in the coronal craniocaudal and coronal transverse dimensions. Conclusions Subcutaneous fat at the medial midfoot often has a mass-like appearance that could be mistaken for a lipoma. It is important to recognize this pseudolesion variant and not to confuse the imaging appearance for a discrete mass.

Regular international civil protection exercises are an important part of the European Civil Protection Mechanism. One such exercise, called IRONORE2019, took place in September 2019 in Eisenerz, Austria, with the aim of training international cooperation of relief teams in case of an earthquake. In parallel to this exercise, the European project DRIVER+ conduced a Trail in order to test novel solutions for civil protection. The German Aerospace Center (DLR) provided aerial imagery as well as derived map products to the project and the exercise, which were also made available to the Bavarian Red Cross, among others, as exercise participants. In this way, products developed using the latest scientific methods could be used and tested in practice. The valuable experiences from this operational use, which are explained in this article, serve the enhancement of the processes and products and will be implemented in the future in order to further support disaster management.

DLRAD – a new vision and mapping benchmark dataset for autonomous driving is under development for the validation of intelligent driving algorithms. Stationary, mobile, and airborne sensors monitored simultaneously the environment around a reference vehicle, which was driving on urban, suburb and rural roads in and around the city of Braunschweig/Germany. Airborne images were acquired with the DLR 4k sensor system mounted on a helicopter. The DLR research car FASCarE is equipped with the latest sensor technology like front/rear radar, ultrasound and laser sensors, optical single and stereo cameras, and GNSS/IMU. Additionally, stationary terrestrial sensors like induction loops, optical mono and stereo cameras, radar and laser scanners monitor defined sections of the path from the ground. Simultaneously, the helicopter with the 4k sensor systems follows the reference car by keeping it all the time in the central nadir view. A next crucial step in the construction of the DLRAD benchmark dataset is the annotation of all objects in the reference dataset.The DLRAD benchmark dataset enables a huge variety of validation capabilities and opens a wide field of possibilities for the development, training and validation of machine learning algorithms in the context of autonomous driving. In this paper, we will present details of the sensor configurations and the acquisition campaign, which had taken place between the 18th July and 20th July 2017 in Braunschweig/Germany. Also, we show a first analysis of the data including the completeness and geometrical quality. The dataset will be published as soon as the coregistration and annotations are complete.

This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR) optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search) and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

Image registration is required for different remote sensing applications, like change detection or image fusion. Since research studies have shown the outstanding absolute geometric accuracy of high resolution radar satellites images like TerraSAR-X, the importance of SAR images as source for geolocation enhancement has increased. Due to this fact, multi-sensor image to image registration of optical and SAR images can be used for the improvement of the absolute geometric processing and accuracy of optical images with TerraSAR-X as reference. In comparison to the common optical and SAR image registration methods the proposed method is a combination of intensity-based and feature-based approaches. The proposed method avoids the direct and often difficult detection of features from the SAR images. SAR-like templates are generated from features detected from the optical image. These templates are used for an intensity-based matching with the SAR image. The results of the matching process are ground control points, which are used for the estimation of translation parameters followed by a subpixel translation of the optical image. The proposed image registration method is tested for two pairs of TerraSAR-X and QuickBird images and one pair of TerraSAR-X andWorldView-2 images of a suburban area. The results show that with the proposed method the geometric accuracy of optical images can be enhanced.

Over the last years several research studies have shown the high geometric accuracy of high resolution radar satellites like TerraSARX. Due to this fact, the impact of high resolution SAR images for image registration has increased. An aim of high accuracy image registration is the improvement of the absolute geometric accuracy of optical images by using SAR images as references. High accuracy image registration is required for different remote sensing applications and is an on-going research topic. The registration of images acquired by different sensor types, like optical and SAR images, is a challenging task. In our work, a novel approach is proposed, which is a combination of the classical feature-based and intensity-based registration approaches. In the first step of the method, spatial features, here roundabouts, are detected in the optical image. In the second step, the detected features are used to generate SAR like roundabout templates. In the third step, the templates are matched with the corresponding parts of the SAR image by using an intensitybased matching process. The proposed method is tested for a pair of TerraSAR-X and QuickBird images and a pair of TerraSAR-X and WorldView-2 images of a suburban area. The results show that the proposed method offers an alternative approach compared to the common optical and SAR images registration methods and it can be used for the geometric accuracy improvement of optical images.

Background: The multi-contrast assessment of the carotid artery wall has become an important diagnostic tool for the characterization of atherosclerotic plaque and vessel wall thickening. For providing the required T1-, T2-, and proton density weighted contrast, multi-slice turbo spin echo (TSE) techniques are normally applied. The straightforward extension of the TSE techniques to volumetric imaging of large sections of the carotid arteries is limited by the resulting long acquisition times. Where the acquisition of a T1-weighted contrast can be accelerated by applying a T1-weighted fast gradient echo technique, acceleration of the T2-weighted contrast is not as straightforward., Methods: In this work, the combination of a T2 preparation and a conventional fast gradient echo technique (T2P-3DGE) was evaluated for rapid acquisition of a T2-weighted image contrast. Acquisition parameters were optimized in an initial in vitro study in direct comparison to the conventional T2-weighted TSE (T2W-3DTSE) technique. Subsequently, the T2P-3DGE technique was evaluated in vivo., Results: In direct comparison, the T2P-3DGE sequence provided similar T2 contrast as the respective T2W-3DTSE sequence. After correction of an observed intensity offset, most likely caused by the additional T1-weighting of the T2P-3DGE sequence, no significant difference between the two T2-weighted sequences were observed in phantom data. The good correlation of the image contrast between the two sequences was confirmed in the initial in-vivo study, proving a potential reduction of the scan time for T2P-3DGE to 25% of the respective T2W-3DTSE technique., Conclusion: The in vitro as well as the in vivo results clearly indicate the potential of the T2P-3DGE technique for providing similar T2 image contrast as in the conventional techniques. Thereby, the acquisition times could be substantially reduced to about 25% of the respective 3D-TSE technique.

Background: The dual-bolus protocol enables accurate quantification of myocardial blood flow (MBF) by first-pass perfusion cardiovascular magnetic resonance (CMR). However, despite the advantages and increasing demand for the dual-bolus method for accurate quantification of MBF, thus far, it has not been widely used in the field of quantitative perfusion CMR. The main reasons for this are that the setup for the dual-bolus method is complex and requires a state-of-the-art injector and there is also a lack of post processing software. As a solution to one of these problems, we have devised a universal dual-bolus injection scheme for use in a clinical setting. The purpose of this study is to show the setup and feasibility of the universal dual-bolus injection scheme., Methods: The universal dual-bolus injection scheme was tested using multiple combinations of different contrast agents, contrast agent dose, power injectors, perfusion sequences, and CMR scanners. This included 3 different contrast agents (Gd-DO3A-butrol, Gd-DTPA and Gd-DOTA), 4 different doses (0.025 mmol/kg, 0.05 mmol/kg, 0.075 mmol/kg and 0.1 mmol/kg), 2 different types of injectors (with and without "pause" function), 5 different sequences (turbo field echo (TFE), balanced TFE, k-space and time (k-t) accelerated TFE, k-t accelerated balanced TFE, turbo fast low-angle shot) and 3 different CMR scanners from 2 different manufacturers. The relation between the time width of dilute contrast agent bolus curve and cardiac output was obtained to determine the optimal predefined pause duration between dilute and neat contrast agent injection., Results: 161 dual-bolus perfusion scans were performed. Three non-injector-related technical errors were observed (1.9%). No injector-related errors were observed. The dual-bolus scheme worked well in all the combinations of parameters if the optimal predefined pause was used. Linear regression analysis showed that the optimal duration for the predefined pause is 25s to separate the dilute and neat contrast agent bolus curves if 0.1 mmol/kg dose of Gd-DO3A-butrol is used., Conclusion: The universal dual-bolus injection scheme does not require sophisticated double-head power injector function and is a feasible technique to obtain reasonable arterial input function curves for absolute MBF quantification.

Objectives: This study sought to evaluate the prevalence of subclinical myocardial infarctions with cardiovascular magnetic resonance imaging (CMRI) in patients with patent foramen ovale (PFO) after cryptogenic cerebral ischemic events., Background: A thrombotic mass passing a PFO may embolize in cerebral but also in coronary arteries, resulting in both cerebral and myocardial ischemic events. CMRI with late gadolinium enhancement (LGE) analysis is the most sensitive imaging technique to detect small myocardial infarctions., Methods: PFO patients (n = 74) with a first cryptogenic cerebral ischemic event without a clinical history for myocardial infarction underwent CMRI and coronary angiography. Right and left ventricular volumes and ejection fractions were measured by CMRI. LGE imaging was performed after administration of gadolinium-diethylenetriaminepentaacetic acid. The presence of atrial septal aneurysm (ASA) was evaluated by transesophageal echocardiography., Results: LGE was detected in 8 of 74 (10.8%) patients. LGE pattern was transmural or subendocardial. Patients with LGE and those without did not differ in cardiovascular risk factors, type of ischemic event, presence of ASA, right and left ventricular volumes, and ejection fractions. LGE volume was small and comprised only 7.9 +/- 2.4% of left ventricular muscle mass. Coronary artery disease was ruled out in 7 of 8 patients with LGE. There was a trend towards a larger PFO size in patients with LGE compared with patients without LGE (13.2 +/- 4.1 mm vs. 16.0 +/- 2.8 mm, p = 0.06)., Conclusions: Subclinical myocardial infarctions determined in CMRI were observed in 10.8% of patients with PFO after a first cryptogenic cerebral ischemic event. Our results strengthen the pathophysiologic role of a PFO with paradoxical embolism in patients with cryptogenic cerebral ischemic events.

Cardiac magnetic resonance imaging (CMR) has become an indispensible imaging technique for the diagnosis and treatment of patients with cardiovascular diseases. Technical advances in the past have rendered CMR unique in the evaluation of cardiovascular anatomy, physiology, and pathophysiology due to its unique ability to produce high resolution tomographic images of the human heart and vessels in any arbitrary orientation, with soft tissue contrast that is superior to competing imaging modalities without the use of ionizing radiation. CMR imaging is the gold standard for assessing left and right ventricular function and for detecting myocardial tissue abnormalities like edema, infarction, or scars. For prognostic reasons abnormal structure and dysfunction of the heart, and the detection of myocardial ischemia and/ /or myocardial scars are the main targets for CMR imaging. In this review we briefly describe the prognostic significance of several CMR imaging techniques and special CMR parameters in patients with coronary artery disease (CAD), with cardiomyopathies, and with chronic heart failure. Myocardial ischemia proved to be a strong predictor of an adverse outcome in patients with CAD. Microvascular obstruction in acute myocardial infarction is a new and independent parameter of negative left ventricular remodeling and a worse prognosis. Myocardial scars in patients with CAD and unrecognized myocardial infarction heralds a negative outcome. Scar in patients with dilated or hypertrophic cardiomyopathy are a strong predictor of both life-threatening ventricular tachyarrhythmias and prognosis. CMR imaging may improve the assessment of inter- and intraventricular dyssynchrony and provide prognostic information by detecting myocardial scars.

Aims: Clinical parameters are weak predictors of outcome in patients with idiopathic dilated cardiomyopathy (IDC). We assessed the prognostic value of cardiac magnetic resonance (CMR) parameters in addition to conventional clinical and electrocardiographic characteristics., Methods and Results: One hundred and forty-one IDC patients were studied. QRS and QTc intervals were measured in 12-lead surface electrocardiogram. Patients were followed for median 1339 days, including 483 patient-years. The primary endpoint-cardiac death or sudden death-occurred in 25 (18%) patients, including 16 patients with cardiac death, 3 patients with sudden cardiac death (SCD), and 6 patients with ICD shock. Late gadolinium enhancement (LGE) was detected in 36 patients (26%). Kaplan-Meier survival analysis displayed QRS >110 ms (P = 0.010), the presence of LGE (P = 0.037), and diabetes mellitus (P < 0.001) as significant parameters for a worse outcome. Multivariable analysis revealed cardiac index (P < 0.001), right ventricular end-diastolic volume index (RVEDVI) (P = 0.006) derived from CMR imaging, the presence of diabetes mellitus (P = 0.006), and QRS >110 ms (P = 0.045) as significant predictors for the primary endpoint., Conclusion: Cardiac index and RVEDVI derived from CMR imaging in addition to QRS duration >110 ms from conventional surface ECG and diabetes mellitus provide prognostic impact for cardiac death and SCD in patients with IDC.

Background: In patients with percutaneous device implantation for closure of patent foramen ovale (PFO), a 10% incidence of new or worsened aortic regurgitation within 12 months has been reported with echocardiography. Cardiac magnetic resonance imaging is a powerful noninvasive tool to quantify volume and fraction of valve insufficiencies. We studied the acute and long-term impact of percutaneous device implantation for PFO closure on valve insufficiencies in cardiac magnetic resonance imaging., Methods and Results: Sequential cardiac magnetic resonance imaging studies were performed in 102 patients with cryptogenic ischemic events. Cardiac magnetic resonance imaging was performed before PFO closure, the day after device implantation, and at 12 months of follow-up. There was no difference in volumetric and hemodynamic parameters before PFO closure compared with 12 months of follow-up. With a cutoff for relevant regurgitation fraction of 5%, there were no statistically significant differences in regurgitation fraction of the semilunar and atrioventricular valves. The median fraction of aortic valve insufficiency was 3.9% (interquartile range [IQR] 2.0% to 5.1%) before PFO closure, 5.4% (IQR 4.1% to 5.9%) after device implantation, and 4.3% (IQR 3.3% to 6.0%) at 12 months of follow-up. The size and type of the occluder had no impact on aortic valve insufficiency. Median regurgitation fraction for the pulmonary valve was 3.6% (IQR 2.4% to 6.7%) before intervention, 7.3% (IQR 5.1% to 8.2%) after occluder implantation and 5.8% (IQR 4.8% to 7.4%) at 12 months of follow-up. Values for the mitral valve were 3.1% (IQR 1.4% to 6.0%), 5.5% (IQR 3.5% to 7.3%), and 3.8% (IQR 1.5% to 7.9%) and for the tricuspid valve were 5.4% (IQR 0.1% to 8.8%), 5.8% (IQR 1.4% to 9.2%), and 6.0% (IQR 1.1% to 8.4%), respectively., Conclusions: Percutaneous PFO closure with device implantation has no impact on valve insufficiencies as determined by cardiac magnetic resonance imaging.

Objective: To evaluate the diagnostic impact of magnetic resonance imaging (MRI) first-pass perfusion using steady-state, free-precession (SSFP) sequences with parallel imaging (SENSE) for detection of coronary stenoses., Design: Prospective observational study., Setting: University hospital, cardiac MRI and catheterisation laboratories., Patients and Methods: 228 patients were examined with coronary angiography and MRI (1.5 T Intera CV). A three-slice, short-axis SSFP perfusion scan with a saturation prepulse was performed during infusion of adenosine and at rest followed by myocardial scar (late enhancement) imaging. Gadolinium-DTPA was given at 0.1 mmol/kg body weight. Perfusion images were visually assessed. Analysis for myocardial hypoperfusion was done according to patient group and according to vessel., Results: Sensitivity, specificity and accuracy of MRI first-pass perfusion for detection of a coronary artery stenosis (>50% luminal narrowing) in the total patient group were 93.0%, 85.7%, 91.2% and for a significant lesion (>70% luminal narrowing) 96.1%, 72.0%, 88.2%, respectively. Based on 536 coronary artery territories without myocardial scar, the sensitivity of MRI perfusion analysis for detection of a significant lesion was for the left anterior descending artery 91.4%, for the circumflex artery 81.6% and for the right coronary artery 65.1% (p<0.001)., Conclusions: MRI first-pass perfusion analysis using an SSFP sequence with three myocardial slices was a highly accurate diagnostic method for detection of coronary artery stenoses. This MRI technique can be included in daily practice and has the potential to guide the indication for invasive coronary angiography.

Objectives: We studied the value of cardiac magnetic resonance imaging (CMRI) before and after closure of patent foramen ovale (PFO) in patients with cryptogenic ischemic events., Background: Cardiac magnetic resonance imaging is a powerful noninvasive tool for detailed assessment of cardiac anatomy and function. The relevance of CMRI compared with transesophageal echocardiography (TEE) in patients undergoing transcatheter PFO closure has not been evaluated so far., Methods: Contrast-enhanced CMRI and TEE were performed in 75 patients before and after PFO closure. Twelve months after PFO closure, both imaging techniques were repeated in 61 patients with contrast application. To determine provokable atrial right-to-left shunting in CMRI, we applied a contrast-enhanced perfusion imaging technique. Detection of atrial septal aneurysm (ASA) was achieved by means of a high-resolution cine imaging technique., Results: Before PFO closure, ASA was seen with CMRI in 28 of 75 cases (37.3%), compared with 47 of 75 (62.7%) cases using TEE. There were a total of 211 CMRI studies with a corresponding TEE performed in 75 patients. No shunt was present in 107 of 211 studies with both techniques. Contrast-enhanced right-to-left shunting was detected by CMRI in 48 of 72 (66.6%) cases with moderate or severe shunts seen with TEE, but only in 6 of 32 (18.8%) studies with mild shunts with TEE. Anomalous venous returns were excluded in all patients. In two patients, coronary anomalies were seen., Conclusions: The present CMRI technique is inferior to TEE in detection of contrast-enhanced right-to-left shunting and identification of ASA.

Background: In some patients suffering from dilated cardiomyopathy (DCM) magnetic resonance imaging (MRI) shows late gadolinium enhancement with variable distribution. Myocardial biopsies in DCM reveal a chronic myocardial inflammatory process in almost 50% and myocardial persistence of adenoviral or enteroviral genome in about 15% of the patients., Aims: We prospectively investigated whether the pattern of late gadolinium enhancement correlates with myocardial biopsy findings., Methods and Results: 42 patients with DCM and 42 control subjects underwent contrast MRI. In the DCM group, endomyocardial biopsies were performed and evaluated for inflammation and viral genome. None of the control subjects showed late gadolinium enhancement whereas in 29 DCM patients (69%) gadolinium enhancement was detectable (p<0.001). 21 of the DCM patients (50%) showed midwall septal enhancement, 7 patients (17%) showed a patchy distribution of hyperenhancement and 1 patient (2%) showed enhancement typical for ischemic heart disease. In myocardial biopsy analysis, 2 patients (5%) showed persistence of viral genome, 18 patients (43%) showed inflammation and in 22 patients (52%) neither virus nor inflammation was detected. The pattern of late gadolinium enhancement and myocardial biopsy findings were not significantly correlated (p = 0.854)., Conclusion: MRI as a non-invasive technique cannot replace myocardial biopsy for the differential diagnosis of DCM.

Purpose: To assess the geometry and area of the left ventricular outflow tract (LVOT) in non-stenotic and stenotic aortic valves and to determine the aortic valve area (AVA) in non-stenotic valves by magnetic resonance imaging (MRI) using a modified continuity equation., Methods: Twenty patients (10 male, mean age 54.8 +/- 15 years) without known aortic valve disease and 10 patients (7 male, mean age 65.1 +/- 14 years) with moderate to severe aortic stenosis were included in this study. MRI was performed using a 1.5 T scanner (Philips Intera CV). AVA was assessed by planimetry on high quality SSFP cine sequences and used as reference standard. LVOT area was defined by calculating a circular area using the LVOT diameter from the 3 chamber view (3CV) and by planimetry. Peak flow velocity was assessed in the LVOT and the proximal aorta. AVA was calculated by a modified Gorlin equation, the continuity equation and a modified continuity equation using the planimetric LVOT area., Results: Planimetric AVA ranged from 2.9 to 6.4 cm2 in patients with non-stenotic and from 0.3 to 1.3 cm2 with stenotic valves, LVOT area from 3.4 to 6.1 cm2 and from 2.6 to 6.5 cm2, respectively. The LVOT area based on the LVOT diameter derived from the 3CV was significantly underestimated in comparison to planimetry in non-stenotic and stenotic aortic valves (3.3 +/- 0.7 vs. 4.7 +/- 1.0 cm2, p < 0.0001; mean difference 1.1 +/- 0.12 cm2, CI 0.86-1.36 and 3.7 +/- 1.2 vs. 4.7 +/- 1.5 cm2, p < 0.05; mean difference 1.0 +/- 1.0 cm2, CI 0.24-1.71). The Gorlin formula showed a poor agreement with planimetry, whereas continuity equation and the modified continuity equation revealed a very good agreement. Planimetry of the LVOT displayed an elliptic shape of the LVOT in all patients with the minimum diameter perpendicular to the 3CV, which was the reason for the above mentioned underestimation., Conclusion: The LVOT area calculated from the 3CV-LVOT diameter underestimates the LVOT area compared to planimetry due to an elliptic shape of the LVOT in patients with non-stenotic as well as with stenotic aortic valves. The modified Gorlin equation proved to be less useful to assess AVA in non-stenotic valves, whereas the continuity equation and a modified continuity equation displayed a very good agreement with planimetric area measurements.

The present study examined the association of myocardial perfusion reserve index (MPRI) in cardiovascular magnetic resonance (CMR) with coronary microvascular dysfunction (CMD) and serum levels of markers of inflammation or endothelial activation. Twelve patients with typical angina pectoris without coronary artery disease were enrolled in this study, and CMR perfusion was analyzed using a steady-state-free-precession sequence with 3 short axis slices per heartbeat during first pass of 0.025 mmol Gadolinium-DTPA/kg body weight. The upslope of myocardial signal intensity curves was used to calculate MPRI. CMD was assessed by intracoronary Doppler flow measurement and biplane angiography. Both MPRI and CMD were assessed during endothelium-independent stimulation with intravenous adenosine and during endothelium-dependent stimulation with intracoronary infusion of acetylcholine. Serum values of soluble CD40 ligand (sCD40L), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), soluble intercellular adhesion molecule-1 (sICAM-1), and C-reactive protein (CRP) were measured. Impaired MPRI correlated significantly with a decrease in coronary blood flow reserve after both endothelium-dependent (p = 0.033) and endothelium-independent (p = 0.022) stimulation. Serum levels above the median of all normal ranged biomarkers sCD40L, TNF-alpha, IL-6, sICAM-1 and CRP were associated with an impaired MPRI for stimulation with adenosine as well as acetylcholine. In multivariable analyses, sCD40L (p < 0.001) and TNF-alpha (p = 0.011) were significantly associated with a decrease in MPRI on adenosine, as were TNF-alpha (p = 0.016) and sICAM-1 (p = 0.022) for a decrease in MPRI on acetylcholine. MPRI on adenosine significantly correlated with MPRI on acetylcholine (p < 0.001). Therefore, the present study demonstrates safety and feasibility of an intracoronary infusion of acetylcholine during CMR perfusion analysis, thus allowing direct assessment of endothelial dependent vasomotor function at the myocardial level by CMR. Furthermore, we show that an impaired myocardial perfusion reserved in CMR is associated with established biomarkers of early atherosclerosis and significantly correlated with CMD. CMR combined with adenosine could be proposed as a non-invasive tool to evaluate CMD.

Aims: Because of its high spatial resolution and tissue contrast, magnetic resonance imaging (MRI) was used to assess cardiac structure and function in a large population of patients with acute myocardial infarction (AMI)., Methods and Results: One hundred and ten patients were studied by MRI 6.1 +/- 2.2 days after AMI. Infarct size (IS), persistent microvascular obstruction (PMO), left and right ventricular (LV/RV) volumes, and functions were measured. The same MRI measurements were repeated in 89 patients after a mean follow-up period of 225 +/- 92 days. IS was 11.9 +/- 7.3% of total LV muscle mass. PMO was detected in 51/110 (46.4%) patients and comprised 15.6 +/- 8.5% of IS and 2.8 +/- 2.3% of LV muscle mass. Papillary muscle infarct was seen in 26%, RV infarction in 16%, pericarditis in 40%, and pericardial effusion in 66% of the patients. During follow-up, there were 16 major adverse cardiac events (MACE) including seven deaths. IS, PMO, and amount of transmural infarction were predictive for LV adverse remodelling defined as > 20% increase in LV end-diastolic volume. Multivariable analysis revealed LV end-diastolic volume, LV ejection fraction, and PMO as significant predictors for the occurrence of MACE., Conclusion: MRI is a highly sensitive and reliable tool to detect morphologic and functional sequelae of AMI providing baseline MRI parameters with relevant predictive power for LV adverse remodelling and occurrence of MACE.