Validating the accuracy of real-time phase-contrast MRI and quantifying the effects of free breathing on cerebrospinal fluid dynamics.

BACKGROUND: Understanding of the cerebrospinal fluid (CSF) circulation is essential for physiological studies and clinical diagnosis. Real-time phase contrast sequences (RT-PC) can quantify beat-to-beat CSF flow signals. However, the detailed effects of free-breathing on CSF parameters are not fully understood. This study aims to validate RT-PC's accuracy by comparing it with the conventional phase-contrast sequence (CINE-PC) and quantify the effect of free-breathing on CSF parameters at the intracranial and extracranial levels using a time-domain multiparametric analysis method. METHODS: Thirty-six healthy participants underwent MRI in a 3T scanner for CSF oscillations quantification at the cervical spine (C2-C3) and Sylvian aqueduct, using CINE-PC and RT-PC. CINE-PC uses 32 velocity maps to represent dynamic CSF flow over an average cardiac cycle, while RT-PC continuously quantifies CSF flow over 45-seconds. Free-breathing signals were recorded from 25 participants. RT-PC signal was segmented into independent cardiac cycle flow curves (Qt) and reconstructed into an averaged Qt. To assess RT-PC's accuracy, parameters such as segmented area, flow amplitude, and stroke volume (SV) of the reconstructed Qt from RT-PC were compared with those derived from the averaged Qt generated by CINE-PC. The breathing signal was used to categorize the Qt into expiratory or inspiratory phases, enabling the reconstruction of two Qt for inspiration and expiration. The breathing effects on various CSF parameters can be quantified by comparing these two reconstructed Qt. RESULTS: RT-PC overestimated CSF area (82.7% at aqueduct, 11.5% at C2-C3) compared to CINE-PC. Stroke volumes for CINE-PC were 615 mm³ (aqueduct) and 43 mm³ (spinal), and 581 mm³ (aqueduct) and 46 mm³ (spinal) for RT-PC. During thoracic pressure increase, spinal CSF net flow, flow amplitude, SV, and cardiac period increased by 6.3%, 6.8%, 14%, and 6%, respectively. Breathing effects on net flow showed a significant phase difference compared to the other parameters. Aqueduct-CSF flows were more affected by breathing than spinal-CSF. CONCLUSIONS: RT-PC accurately quantifies CSF oscillations in real-time and eliminates the need for cardiac synchronization, enabling the quantification of the cardiac and breathing components of CSF flow. This study quantifies the impact of free-breathing on CSF parameters, offering valuable physiological references for understanding the effects of breathing on CSF dynamics.

Characterization of cardiac resynchronization therapy response through machine learning and personalized models.

INTRODUCTION: The characterization and selection of heart failure (HF) patients for cardiac resynchronization therapy (CRT) remain challenging, with around 30% non-responder rate despite following current guidelines. This study aims to propose a novel hybrid approach, integrating machine-learning and personalized models, to identify explainable phenogroups of HF patients and predict their CRT response. METHODS: The paper proposes the creation of a complete personalized model population based on preoperative CRT patient strain curves. Based on the parameters and features extracted from these personalized models, phenotypes of patients are identified thanks to a clustering algorithm and a random forest classification is provided. RESULTS: A close match was observed between the 162 experimental and simulated myocardial strain curves, with a mean RMSE of 4.48% (±1.08) for the 162 patients. Five phenogroups of personalized models were identified from the clustering, with response rates ranging from 52% to 94%. The classification results show a mean area under the curves (AUC) of 0.86 ± 0.06 and provided a feature importance analysis with 22 features selected. Results show both regional myocardial contractility (from 22.5% to 33.0%), tissue viability and electrical activation delays importance on CRT response for each HF patient (from 55.8 ms to 88.4 ms). DISCUSSION: The patient-specific model parameters' analysis provides an explainable interpretation of HF patient phenogroups in relation to physiological mechanisms that seem predictive of the CRT response. These novel combined approaches appear as promising tools to improve understanding of LV mechanical dyssynchrony for HF patient characterization and CRT selection.

Impact of Shunt Placement on CSF Dynamics.

BACKGROUND: CSF dynamics are disturbed in chronic hydrocephalus (NPH). We hypothesise that these alterations reflect a disturbance of intracranial compliance. The aim of our study is to investigate the variations in intracranial hydrodynamics in NPH after ventricular shunt surgery. PATIENTS AND METHOD: We included 14 patients with definite NPH. All patients improved after ventriculoperitoneal shunting. The patients underwent an analysis of intracranial haemodynamics by phase-contrast MRI (pcMRI) preoperatively, at 6 months postoperatively, and at 1 year postoperatively. We analysed the dynamics of intraventricular CSF at the level of the aqueduct of Sylvius (SVAQU) and CSF at the level of the high cervical subarachnoid spaces (SVCERV). We calculated the ratio between SVAQU and SVCERV, called CSFRATIO, which reflects the participation of intraventricular pulsatility in overall intracranial CSF pulsatility. RESULTS: SVAQU significantly (p = 0.003) decreased from 240 ± 114 µL/cc to 214 ± 157 µL/cc 6 months after shunt placement. Six months after shunt placement, SVCERV significantly (p = 0.007) decreased from 627 ± 229 µL/cc to 557 ± 234 µL/cc. Twelve months after shunt placement, SVCERV continued to significantly (p = 0.001) decrease to 496 ± 234 µL/cc. CSFRATIO was not changed by surgery. CONCLUSIONS: CSF dynamics are altered by shunt placement and might be a useful marker of the shunt's effectiveness-especially if pressure values start to rise again. The detection of changes in CSF dynamics would require a reference postoperative pcMRI measurement for each patient.