Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data.

PURPOSE: The combination of positron emission tomography (PET) and magnetic resonance (MR) tomography in a single device is anticipated to be the next step following PET/CT for future molecular imaging application. Compared to CT, the main advantages of MR are versatile soft tissue contrast and its capability to acquire functional information without ionizing radiation. However, MR is not capable of measuring a physical quantity that would allow a direct derivation of the attenuation values for high-energy photons. METHODS: To overcome this problem, we propose a fully automated approach that uses a dedicated T1-weighted MR sequence in combination with a customized image processing technique to derive attenuation maps for whole-body PET. The algorithm automatically identifies the outer contour of the body and the lungs using region-growing techniques in combination with an intensity analysis for automatic threshold estimation. No user interaction is required to generate the attenuation map. RESULTS: The accuracy of the proposed MR-based attenuation correction (AC) approach was evaluated in a clinical study using whole-body PET/CT and MR images of the same patients (n = 15). The segmentation of the body and lung contour (L-R directions) was evaluated via a four-point scale in comparison to the original MR image (mean values >3.8). PET images were reconstructed using elastically registered MR-based and CT-based (segmented and non-segmented) attenuation maps. The MR-based AC showed similar behaviour as CT-based AC and similar accuracy as offered by segmented CT-based AC. Standardized uptake value (SUV) comparisons with reference to CT-based AC using predefined attenuation coefficients showed the largest difference for bone lesions (mean value ± standard variation of SUV(max): -3.0% ± 3.9% for MR; -6.5% ± 4.1% for segmented CT). A blind comparison of PET images corrected with segmented MR-based, CT-based and segmented CT-based AC afforded identical lesion detectability, but slight differences in image quality were found. CONCLUSION: Our MR-based attenuation correction method offers similar correction accuracy as offered by segmented CT. According to the specialists involved in the blind study, these differences do not affect the diagnostic value of the PET images.

Automated Detection and Segmentation of Brain Metastases in Malignant Melanoma: Evaluation of a Dedicated Deep Learning Model.

BACKGROUND AND PURPOSE: Malignant melanoma is an aggressive skin cancer in which brain metastases are common. Our aim was to establish and evaluate a deep learning model for fully automated detection and segmentation of brain metastases in patients with malignant melanoma using clinical routine MR imaging. MATERIALS AND METHODS: Sixty-nine patients with melanoma with a total of 135 brain metastases at initial diagnosis and available multiparametric MR imaging datasets (T1-/T2-weighted, T1-weighted gadolinium contrast-enhanced, FLAIR) were included. A previously established deep learning model architecture (3D convolutional neural network; DeepMedic) simultaneously operating on the aforementioned MR images was trained on a cohort of 55 patients with 103 metastases using 5-fold cross-validation. The efficacy of the deep learning model was evaluated using an independent test set consisting of 14 patients with 32 metastases. Manual segmentations of metastases in a voxelwise manner (T1-weighted gadolinium contrast-enhanced imaging) performed by 2 radiologists in consensus served as the ground truth. RESULTS: After training, the deep learning model detected 28 of 32 brain metastases (mean volume, 1.0 [SD, 2.4] cm3) in the test cohort correctly (sensitivity of 88%), while false-positive findings of 0.71 per scan were observed. Compared with the ground truth, automated segmentations achieved a median Dice similarity coefficient of 0.75. CONCLUSIONS: Deep learning-based automated detection and segmentation of brain metastases in malignant melanoma yields high detection and segmentation accuracy with false-positive findings of <1 per scan.

Telephonic transmission of 12-lead electrocardiograms during acute myocardial infarction.

To test the feasibility of a small and simple system for telephonic transmission of 12-lead electrocardiograms (ECGs), 70 patients with acute coronary syndrome admitted to the cardiac care unit (CCU) were included in a feasibility study. The transmission system consisted of a belt with multiple electrodes, which was positioned around the chest. The ECG signal was sent to a call centre via a standard telephone line. In parallel, a standard 12-lead ECG was recorded on site. In a retrospective analysis, each lead of the transmitted ECG was compared with the on-site 12-lead ECG with regard to ST-segment changes and final diagnosis. In all 37 patients with acute ST-elevation myocardial infarction, the diagnosis was correctly established on the basis of telephone-transmitted ECGs. In 96% of limb and 88% of chest leads, ST elevations which were visible in standard ECGs were correctly displayed on telephonically transmitted ECGs. In the remaining 33 patients no false-positive diagnosis was made using transtelephonic ECG analysis. A control group of 31 patients without apparent heart disease showed high concordance between standard ECGs and telephonically transmitted ECGs. Telephonically transmitted 12-lead ECGs interpreted by a hospital-based internist/cardiologist might allow a rapid and accurate diagnosis of ST-elevation myocardial infarction and may increase diagnostic safety for the emergency staff during prehospital decision making and treatment of acute myocardial infarction.

N30 and the effect of explorative finger movements: a model of the contribution of the motor cortex to early somatosensory potentials.

OBJECTIVES: The source of the N30 potential in the median nerve somatosensory evoked potentials (SEP) has been previously attributed to a pre-central origin (motor cortex or the supplementary motor area, SMA) or a post-central located generator (somatosensory cortex). This attribution was made from results of lesion studies, the behavior of the potential under pathological conditions, and dipole source localization within spherical volume conductor models. METHODS: The present study applied dipole source localization and current density reconstruction within individual realistically shaped head models to median nerve SEPs obtained during explorative finger movements. RESULTS: The SEPs associated with movement of the stimulated hand showed a minor reduction of the N20 amplitude and a markedly reduced amplitude for the frontal N30 and parietal P27, exhibiting a residual frontal negativity around 25 ms. The brain-stem P14 remained unchanged. Mapping of the different SEPs (movement of the non-stimulated hand minus movement of the stimulated hand) showed a bipolar field pattern with a maximum around 30 ms post-stimulus. In eight out of ten normal subjects, both the N30 and the gN30 (subtraction data) sources resided within the pre-central gyrus, more medially than the post-centrally located N20. Two subjects, in contrast, showed rather post-centrally localized sources in this time range. A model of the cortical SEP sources is introduced, explaining the data with respect to previously described findings of dipole localization, and from lesion studies and the alterations seen in motor diseases. CONCLUSIONS: The results provide evidence for a pre-central N30 generator, predominantly tangentially oriented, located within the motor cortex, while no sources were detected elsewhere. It is suggested that the mechanisms underlying the 'gating' effect during explorative finger movements in the 30 ms time range predominantly arise in the motor cortex.

Wearable approach for continuous ECG--and activity patient-monitoring.

The paper describes an approach to monitor a person's ECG and activity continuously with functional clothing. A belt with integrated electronics has been developed and has proven long-term robustness of all electrical components. A low-power module measures the ECG signal as well as the acceleration (2-axis) and stores data continuously up to two days. A user test has been performed to evaluate the belt according to system performance at different daily-life activities like sleeping, walking and so on. System parameters are ECG-signal quality, system up-time, and ECG-signal coverage during a day.