Optimal Chest Compression Point During Pediatric Resuscitation: Implications for Pediatric Resuscitation Practice by CT Scans.

OBJECTIVES: Current European guidelines for pediatric cardiopulmonary resuscitation (CPR) recommend the lower half of the sternum as the chest compression point (CP). In this study, we have used thoracic CT scans to evaluate recommended and optimal CP in relation to cardiac anatomy and structure. DESIGN: Analysis of routinely acquired thoracic CT scans acquired from 2000 to 2020. SETTING: Single-center pediatric department in a German University Hospital. PATIENTS: Imaging data were obtained from 290 patients of 3-16 years old. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We measured and analyzed 14 thoracic metrics in each thoracic CT scan. In 44 of 290 (15.2%) scans, the recommended CP did not match the level of the cardiac ventricles. Anatomically, the optimal CP was one rib or one vertebral body lower than the recommended CP, that is, the optimal CP was more caudal to the level of the body of the sternum in 67 of 290 (23.1%) scans. The recommended compression depth appeared reasonable in children younger than 12 years old. At 12 years old or older, the maximum compression depth of 6 cm is less than or equal to one-third of the thoracic depth. CONCLUSIONS: In this study of thoracic CT scans in children 3-16 years old, we have found that optimal CP for CPR appears to be more caudal than the recommended CP. Therefore, it seems reasonable to prefer to use the lower part of the sternum for CPR chest compressions. At 12 years old or older, a compression depth similar to that used in adults-6 cm limit-may be chosen.

Hybrid dynamic bright and black blood angiography by non-contrast-enhanced vessel selective saturation angiography.

The integrity of vessel walls and changes in blood flow are involved in many diseases, and information about these anatomical and physiological conditions is important for a diagnosis. There are several different angiography methods that can be used to generate images for diagnostic purposes, but often using different imaging techniques and MR sequences. The purpose of this study was to develop a method that allows time-resolved, vessel-selective simultaneous bright and black blood imaging by vesselselective blood saturation. Measurements in six volunteers were performed to evaluate the time-resolved bright blood angiography and the significance of the generated black blood contrast. It was shown that this method can be used to generate a black blood contrast with a sufficient signal difference to the surrounding gray matter in addition to the time-resolved and vessel-selective bright blood contrast. Using post-processing methods, whole brain angiograms can be calculated from the acquired data.

AI supported detection of cerebral multiple sclerosis lesions decreases radiologic reporting times.

PURPOSE: Multiple Sclerosis (MS) is a common autoimmune disease of the central nervous system. MRI plays a crucial role in diagnosing as well as in disease and treatment monitoring. Therefore, evaluation of cerebral MRI of MS patients is part of daily clinical routine. A growing number of companies offer commercial software to support the reporting with automated lesion detection. Aim of this study was to evaluate the effect of such a software with AI supported lesion detection to the radiologic reporting. METHOD: Four radiologist each counted MS-lesions in MRI examinations of 50 patients separated by the locations periventricular, cortical/juxtacortical, infrantentorial and unspecific white matter. After at least six weeks they repeated the evaluation, this time using the AI based software mdbrain for lesion detection. In both settings the required time was documented. Further the radiologists evaluated follow-up MRI of 50 MS-patients concerning new and enlarging lesions in the same manner. RESULTS: To determine the lesion-load the average reporting time decreased from 286.85 sec to 196.34 sec (p > 0.001). For the evaluation of the follow-up images the reporting time dropped from 196.17 sec to 120.87 sec (p < 0.001). The interrater reliabilities showed no significant differences for the determination of lesion-load (0.83 without vs. 0.8 with software support) and for the detection of new/enlarged lesions (0.92 without vs. 0.82 with software support). CONCLUSION: For the evaluation of MR images of MS patients, an AI-based support for image-interpretation can significantly decreases reporting times.

Automatic lesion detection at Multiple Sclerosis patients - Comparison of 2D- and 3D-FLAIR-datasets.

BACKGROUND: Multiple Sclerosis (MS) is a common autoimmune inflammatory disease of the central nervous system (CNS). Magnetic Resonance Imaging (MRI) allows a sensitive assessment of the CNS and is established for diagnostic, prognostic and (therapy-) monitoring purposes. Especially lesion counting in T2- or Fluid Attenuated Inversion Recovery (FLAIR)-weighted images plays a decisive role in clinical routine. Software-packages allowing an automatic evaluation of image data are increasingly established aiming a faster and improved workflow. These programs allow e.g. the counting, spatial attribution and volumetry of MS-lesions in FLAIR-weighted images. Research has shown that 3D-FLAIR-sequences are superior to 2D-FLAIR-sequences in visual evaluation of lesion burden in MS. An influence on the automatic analysis is expectable but not yet systematically studied. This work will therefore investigate the influence of 2D- and 3D datasets on the results of an automatic assessment. MATERIAL AND METHODS: In this prospective study, 80 Multiple Sclerosis patients underwent a clinically indicated routine MRI examination. The clinical routine protocol already including a 3D-FLAIR sequence was adapted by an additional 2D-FLAIR sequence also conform to the 2021 MAGNIMS-CMSCNAIMS consensus recommendations. To obtain a quantitative analysis for assessment of amount, dissemination and volume of the lesions, the acquired MR images were post-processed using the CE-certified Software mdbrain (mediaire, Berlin, Germany). The resulting data were statistically analysed using the paired t-test for normally distributed data and the Wilcoxon-signed-rank-test for not normally distributed data respectively. Demographic data and data such as the subtype, duration, severity and therapy of the disease were collected, pseudonymized and evaluated. RESULTS: There is a significant difference concerning the total number and lesion volume with more lesions being detected (2D: 29.7, +/- 20.22 sd; 3D: 40.1 +/- 31.67 sd; p < 0.0001) but lower total volume (2D: 6.24 +/- 6.11 sd; 3D: 5.39 +/- 6.37 sd; p < 0.0001) when using the 3D- sequence. Especially significantly more small lesions in the unspecific white matter and infratentorial region were detected by using the 3D-FLAIR sequence (p < 0.0001) compared to the 2D-FLAIR image. Main reason for the lower total volume in the 3D-FLAIR sequence was the calculated volume for periventricular lesions which was significantly beneath the calculated volume from the 2D-FLAIR sequence (p < 0.0001). CONCLUSION: Automatic lesion counting and volumetry is feasible with both 2D- and 3D-weightend FLAIR images. Still, it leads to partly significant differences even between two sequences that both are conform to the 2021 MAGNIMS-CMSCNAIMS consensus recommendations. This study contributes valuable insights into the impact of using different input data from the same patient for automated MS lesion evaluation.

Detection of Spinal Cord Multiple Sclerosis Lesions Using a 3D-PSIR Sequence at 1.5 T.

PURPOSE: Multiple sclerosis (MS) is a prevalent autoimmune inflammatory disease. Besides cerebral manifestations, an affection of the spinal cord is typical; however, imaging of the spinal cord is difficult due to its anatomy. The aim of this study was to assess the diagnostic value of a 3D PSIR pulse sequencing at a 1.5 T magnetic field strength for both the cervical and thoracic spinal cord. METHODS: Phase sensitive inversion recovery (PSIR), short tau inversion recovery (STIR) and T2-weighted (T2-w) images of the spinal cord of 50 patients were separately evaluated by three radiologists concerning the number and location of MS lesions. Furthermore, lesion to cord contrast ratios were determined for the cervical and thoracic spinal cord. RESULTS: Of the lesions 54.81% were located in the cervical spinal cord, 42.26% in the thoracic spinal cord and 2.93% in the conus medullaris. The PSIR images showed a higher sensitivity for lesion detection in the cervical and thoracic spinal cord (77.10% and 72.61%, respectively) compared to the STIR images (58.63% and 59.10%, respectively) and the T2-w images (59.95% and 59.52%, respectively). The average lesion to cord contrast ratio was significantly higher in the PSIR images compared to the STIR images (p < 0.001) and the T2-w images (p < 0.001). CONCLUSION: Evaluation of the spinal cord with a 3D PSIR sequence at a magnetic field strength of 1.5 T is feasible with a high sensitivity for the detection of spinal MS lesions for the cervical as well as the thoracic segments. In combination with other pulse sequences it might become a valuable addition in an advanced imaging protocol.