A DWI-based hypoxia model shows robustness in an external prostatectomy cohort.

Introduction: Prostate cancer hypoxia is a negative prognostic biomarker. A promising MRI-based tool to assess hypoxia is the 'Consumption and Supply based Hypoxia' (CSH) model based on diffusion-weighted imaging (DWI). The aim of the study was to validate the association between the CSH hypoxia fraction (HFDWI) with pathological Grade Group (pGG) and pathological T-staging (pTstage) in an external prostatectomy cohort. Methods: Apparent diffusion coefficient (ADC) and fractional blood volume (fBV) maps were assessed from DWI data from 291 prostatectomies and combined by the CSH model. HFDWI was calculated for each lesion after median scaling of ADC and fBV to address differences in acquisition and analysis between centers. The absolute HFDWI values and the associations of HFDWI between pGG < 3 versus ≥ 3, and pTstage = 2 versus = 3 in the Netherlands Cancer Institute (NKI) cohort were compared to the obtained by original cohort (Oslo cohort). Statistical T- and Mann-Whitney tests (p<0.05) were performed. Pearson correlation was determined between HFDWI and individual pGG groups. Results: The HFDWI showed comparable absolute values and similar metric performance as in the original published cohort. Higher HFDWI values were observed for higher pGG (Oslo: 0.27; NKI: 0.24) compared to lower pGG (Oslo: 0.11; NKI: 0.17). Similar results were obtained for pTstage. Furthermore, HFDWI demonstrated a significant positive correlation with pGG groups 1-5 (ρ = 0.41, p<0.001). Conclusion: The CSH model exhibited sufficient robustness in the external cohort, suggesting a plausible reflection of true hypoxia and enabling the use of the HFDWI metric for further research into prostate cancer and hypoxia.

Validation of quantitative magnetic resonance imaging techniques in head and neck healthy structures involved in the salivary and swallowing function: Accuracy and repeatability.

Background and Purpose: Radiation-induced damage to the organs at risk (OARs) in head-and-neck cancer (HNC) patient can result in long-term complications. Quantitative magnetic resonance imaging (qMRI) techniques such as diffusion-weighted imaging (DWI), DIXON for fat fraction (FF) estimation and T2 mapping could potentially provide a spatial assessment of such damage. The goal of this study is to validate these qMRI techniques in terms of accuracy in phantoms and repeatability in-vivo across a broad selection of healthy OARs in the HN region. Materials and Methods: Scanning was performed at a 3 T diagnostic MRI scanner, including the calculation of apparent diffusion coefficient (ADC) from DWI, FF and T2 maps. Phantoms were scanned to estimate the qMRI techniques bias using Bland-Altman statistics. Twenty-six healthy subjects were scanned twice in a test-retest study to determine repeatability. Repeatability coefficients (RC) were calculated for the parotid, submandibular, sublingual and tubarial salivary glands, oral cavity, pharyngeal constrictor muscle and brainstem. Additionally, a linear mixed-effect model analysis was used to evaluate the effect of subject-specific characteristics on the qMRI values. Results: Bias was 0.009x10-3 mm2/s for ADC, -0.7 % for FF and -7.9 ms for T2. RCs ranged 0.11-0.25x10-3 mm2/s for ADC, 1.2-6.3 % for FF and 2.5-6.3 ms for T2. A significant positive linear relationship between age and the FF and T2 for some of the OARs was found. Conclusion: These qMRI techniques are feasible, accurate and repeatable, which is promising for treatment response monitoring and/or differentiating between healthy and unhealthy tissues due to radiation-induced damage in HNC patients.

A framework to integrate EEG-correlated fMRI and intracerebral recordings.

EEG-correlated functional MRI (EEG-fMRI) has been used to indicate brain regions associated with interictal epileptiform discharges (IEDs). This technique enables the delineation of the complete epileptiform network, including multifocal and deeply situated cortical areas. Before EEG-fMRI can be used as an additional diagnostic tool in the preoperative work-up, its added value should be assessed in relation to intracranial EEG recorded from depth electrodes (SEEG) or from the cortex (ECoG), currently the clinical standard. In this study, we propose a framework for the analysis of the SEEG data to investigate in a quantitative way whether EEG-fMRI reflects the same cortical areas as identified by the IEDs present in SEEG recordings. For that purpose, the data of both modalities were analyzed with a general linear model at the same time scale and within the same spatial domain. The IEDs were used as predictors in the model, yielding for EEG-fMRI the brain voxels that were related to the IEDs and, similarly for SEEG, the electrodes that were involved. Finally, the results of the regression analysis were projected on the anatomical MRI of the patients. To explore the usefulness of this quantitative approach, a sample of five patients was studied who both underwent EEG-fMRI and SEEG recordings. For clinical validation, the results of the SEEG analysis were compared to the standard visual review of IEDs in SEEG and to the identified seizure onset zone, the resected area, and outcome of surgery. SEEG analysis revealed a spatial pattern for the most frequent and dominant IEDs present in the data of all patients. The electrodes with the highest correlation values were in good concordance with the electrodes that showed maximal amplitude during those events in the SEEG recordings. These results indicate that the analysis of SEEG data at the time scale of EEG-fMRI, using the same type of regression model, is a promising way to validate EEG-fMRI data. In fact, the BOLD areas with a positive hemodynamic response function were closely related to the spatial pattern of IEDs in the SEEG recordings in four of the five patients. The areas of significant BOLD that were not located in the vicinity of depth electrodes, were mainly characterized by negative hemodynamic responses. Furthermore, the area with a positive hemodynamic response function overlapped with the resected area in three patients, while it was located at the edge of the resection area for one. To conclude, the results of this study encourage the application of EEG-fMRI to guide the implantation of depth electrodes as prerequisite for successful epilepsy surgery.

Automatic breath-to-breath analysis of nocturnal polysomnographic recordings.

Diagnosis of sleep-disordered breathing is based on the presence of an abnormal breathing pattern during sleep. In this study, an algorithm was developed for the offline breath-to-breath analysis of the nocturnal respiratory recordings. For that purpose, respiratory signals (nasal airway pressure, thoracic and abdominal movements) were divided into half waves using period amplitude analysis. Individual breaths were characterized by the parameters of the half waves (duration, amplitude, and slope). These values can be used to discriminate between normal and abnormal breaths. This algorithm was applied to six polysomnographic recordings to distinguish abnormal breathing events (apneas and hypopneas). The algorithm was robust for the identification of breaths (sensitivity = 96.8%, positive prediction value (PPV) = 99.5%). The detection of apneas and hypopneas was compared to the manual scoring of two experienced sleep technicians: sensitivity was, respectively, 89.2 and 88.9%, PPV was 54.1 and 59.3%. The classification of apneas into central, obstructive, or mixed was in concordance with the observers in 68% of the apneas. Although the algorithm tended to detect more hypopneas than the clinical standard, this study shows that the extraction of breath-to-breath parameters is useful for detection of abnormal respiratory events and provides a basis for further characterization of these events.