Potential acceleration performance of a 256-channel whole-brain receive array at 7 T

PURPOSE: Assess the potential gain in acceleration performance of a 256-channel versus 32-channel receive coil array at 7 T in combination with a 2D CAIPIRINHA sequence for 3D data sets. METHODS: A 256-channel receive setup was simulated by placing 2 small 16-channel high-density receive arrays at 2 × 8 different locations on the head of healthy participants. Multiple consecutive measurements were performed and coil sensitivity maps were combined to form a complete 256-channel data set. This setup was compared with a standard 32-channel head coil, in terms of SNR, noise correlation, and acceleration performance (g-factor). RESULTS: In the periphery of the brain, the receive SNR was on average a factor 1.5 higher (ranging up to a factor 2.7 higher) than the 32-channel coil; in the center of the brain the SNR was comparable or lower, depending on the size of the region of interest, with a factor 1.0 on average (ranging from 0.7 up to a factor of 1.6). The average noise correlation between coil elements was 3% for the 256-channel coil, and 5% for the 32-channel coil. At acceptable g-factors (< 2), the achievable acceleration factor using SENSE and 2D CAIPIRINHA was 24 and 28, respectively, versus 9 and 12 for the 32-channel coil. CONCLUSION: The receive performance of the simulated 256 channel array was better than the 32-channel reference. Combined with 2D CAIPIRINHA, a peak acceleration factor of 28 was assessed, showing great potential for high-density receive arrays

Potential acceleration performance of a 256-channel whole-brain receive array at 7 T

PURPOSE: Assess the potential gain in acceleration performance of a 256-channel versus 32-channel receive coil array at 7 T in combination with a 2D CAIPIRINHA sequence for 3D data sets. METHODS: A 256-channel receive setup was simulated by placing 2 small 16-channel high-density receive arrays at 2 × 8 different locations on the head of healthy participants. Multiple consecutive measurements were performed and coil sensitivity maps were combined to form a complete 256-channel data set. This setup was compared with a standard 32-channel head coil, in terms of SNR, noise correlation, and acceleration performance (g-factor). RESULTS: In the periphery of the brain, the receive SNR was on average a factor 1.5 higher (ranging up to a factor 2.7 higher) than the 32-channel coil; in the center of the brain the SNR was comparable or lower, depending on the size of the region of interest, with a factor 1.0 on average (ranging from 0.7 up to a factor of 1.6). The average noise correlation between coil elements was 3% for the 256-channel coil, and 5% for the 32-channel coil. At acceptable g-factors (< 2), the achievable acceleration factor using SENSE and 2D CAIPIRINHA was 24 and 28, respectively, versus 9 and 12 for the 32-channel coil. CONCLUSION: The receive performance of the simulated 256 channel array was better than the 32-channel reference. Combined with 2D CAIPIRINHA, a peak acceleration factor of 28 was assessed, showing great potential for high-density receive arrays

On the magnetic field dependence of deuterium metabolic imaging

\u3cp\u3eDeuterium metabolic imaging (DMI) is a novel MR-based method to spatially map metabolism of deuterated substrates such as [6,6'-\u3csup\u3e2\u3c/sup\u3eH\u3csub\u3e2\u3c/sub\u3e]-glucose in vivo. Compared with traditional \u3csup\u3e13\u3c/sup\u3eC-MR-based metabolic studies, the MR sensitivity of DMI is high due to the larger \u3csup\u3e2\u3c/sup\u3eH magnetic moment and favorable T\u3csub\u3e1\u3c/sub\u3e and T\u3csub\u3e2\u3c/sub\u3e relaxation times. Here, the magnetic field dependence of DMI sensitivity and transmit efficiency is studied on phantoms and rat brain postmortem at 4, 9.4 and 11.7 T. The sensitivity and spectral resolution on human brain in vivo are investigated at 4 and 7 T before and after an oral dose of [6,6'-\u3csup\u3e2\u3c/sup\u3eH\u3csub\u3e2\u3c/sub\u3e]-glucose. For small animal surface coils (Ø 30 mm), the experimentally measured sensitivity and transmit efficiency scale with the magnetic field to a power of +1.75 and −0.30, respectively. These are in excellent agreement with theoretical predictions made from the principle of reciprocity for a coil noise-dominant regime. For larger human surface coils (Ø 80 mm), the sensitivity scales as a +1.65 power. The spectral resolution increases linearly due to near-constant linewidths. With optimal multireceiver arrays the acquisition of DMI at a nominal 1 mL spatial resolution is feasible at 7 T.\u3c/p\u3

On the magnetic field dependence of deuterium metabolic imaging

Deuterium metabolic imaging (DMI) is a novel MR-based method to spatially map metabolism of deuterated substrates such as [6,6’-2H2]-glucose in vivo. Compared with traditional 13C-MR-based metabolic studies, the MR sensitivity of DMI is high due to the larger 2H magnetic moment and favorable T1 and T2 relaxation times. Here, the magnetic field dependence of DMI sensitivity and transmit efficiency is studied on phantoms and rat brain postmortem at 4, 9.4 and 11.7 T. The sensitivity and spectral resolution on human brain in vivo are investigated at 4 and 7 T before and after an oral dose of [6,6’-2H2]-glucose. For small animal surface coils (Ø 30 mm), the experimentally measured sensitivity and transmit efficiency scale with the magnetic field to a power of +1.75 and −0.30, respectively. These are in excellent agreement with theoretical predictions made from the principle of reciprocity for a coil noise-dominant regime. For larger human surface coils (Ø 80 mm), the sensitivity scales as a +1.65 power. The spectral resolution increases linearly due to near-constant linewidths. With optimal multireceiver arrays the acquisition of DMI at a nominal 1 mL spatial resolution is feasible at 7 T

On the magnetic field dependence of deuterium metabolic imaging

Deuterium metabolic imaging (DMI) is a novel MR-based method to spatially map metabolism of deuterated substrates such as [6,6'-2H2]-glucose in vivo. Compared with traditional 13C-MR-based metabolic studies, the MR sensitivity of DMI is high due to the larger 2H magnetic moment and favorable T1 and T2 relaxation times. Here, the magnetic field dependence of DMI sensitivity and transmit efficiency is studied on phantoms and rat brain postmortem at 4, 9.4 and 11.7 T. The sensitivity and spectral resolution on human brain in vivo are investigated at 4 and 7 T before and after an oral dose of [6,6'-2H2]-glucose. For small animal surface coils (Ø 30 mm), the experimentally measured sensitivity and transmit efficiency scale with the magnetic field to a power of +1.75 and −0.30, respectively. These are in excellent agreement with theoretical predictions made from the principle of reciprocity for a coil noise-dominant regime. For larger human surface coils (Ø 80 mm), the sensitivity scales as a +1.65 power. The spectral resolution increases linearly due to near-constant linewidths. With optimal multireceiver arrays the acquisition of DMI at a nominal 1 mL spatial resolution is feasible at 7 T

Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

Background: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. Methods: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. Results: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH2O. Conclusion: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes

The Prospective Dutch Colorectal Cancer (PLCRC) cohort: real-world data facilitating research and clinical care

Real-world data (RWD) sources are important to advance clinical oncology research and evaluate treatments in daily practice. Since 2013, the Prospective Dutch Colorectal Cancer (PLCRC) cohort, linked to the Netherlands Cancer Registry, serves as an infrastructure for scientific research collecting additional patient-reported outcomes (PRO) and biospecimens. Here we report on cohort developments and investigate to what extent PLCRC reflects the “real-world”. Clinical and demographic characteristics of PLCRC participants were compared with the general Dutch CRC population (n = 74,692, Dutch-ref). To study representativeness, standardized differences between PLCRC and Dutch-ref were calculated, and logistic regression models were evaluated on their ability to distinguish cohort participants from the Dutch-ref (AU-ROC 0.5 = preferred, implying participation independent of patient characteristics). Stratified analyses by stage and time-period (2013–2016 and 2017–Aug 2019) were performed to study the evolution towards RWD. In August 2019, 5744 patients were enrolled. Enrollment increased steeply, from 129 participants (1 hospital) in 2013 to 2136 (50 of 75 Dutch hospitals) in 2018. Low AU-ROC (0.65, 95% CI: 0.64–0.65) indicates limited ability to distinguish cohort participants from the Dutch-ref. Characteristics that remained imbalanced in the period 2017–Aug’19 compared with the Dutch-ref were age (65.0 years in PLCRC, 69.3 in the Dutch-ref) and tumor stage (40% stage-III in PLCRC, 30% in the Dutch-ref). PLCRC approaches to represent the Dutch CRC population and will ultimately meet the current demand for high-quality RWD. Efforts are ongoing to improve multidisciplinary recruitment which will further enhance PLCRC’s representativeness and its contribution to a learning healthcare system