The automatic segmentation method performs as well as the manual segmentation for GFR estimation and reduces the segmentation time from several hours to 45 s. Magn Reson Med 79:1696-1707, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Purpose To introduce a respiratory-gated high-spatiotemporal-resolution dynamic-contrast-enhanced MRI technique and a high-temporal-resolution aortic input function (HTR-AIF) estimation method for glomerular filtration rate (GFR) assessment in children. Methods A high-spatiotemporal-resolution DCE-MRI method with view-shared reconstruction was modified to incorporate respiratory-gating, and an AIF estimation method that uses a fraction of the k-space data from each respiratory period was developed (HTR-AIF). The method was validated using realistic digital phantom simulations and demonstrated on clinical subjects. The GFR estimates using HTR-AIF were compared to estimates obtained by using an AIF derived directly from the view-shared images. Results Digital phantom simulations showed that using the HTR-AIF technique gives more accurate AIF estimates (RMSE = 0.0932) compared to the existing estimation method (RMSE = 0.2059) that used view-sharing (VS). For simulated GFR > 27 ml/min, GFR estimation error was between 32% and 17% using view-shared AIF, whereas estimation error was less than 10% using HTR-AIF. In all clinical subjects, the HTR-AIF method resulted in higher GFR estimations than the view-shared method. Conclusion The HTR-AIF method improves the accuracy of both the AIF and GFR estimates derived from the respiratory-gated acquisitions, and makes GFR estimation feasible in free-breathing pediatric subjects.

1 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2017;46:303-311.

This paper describes a new data acquisition (DAQ) program for a breast dedicated high-resolution positron emission tomography (PET) camera employing 4608 position-sensitive avalanche photodiodes (PSAPDs). The DAQ program is designed to be highly scalable to match the needs of evolving implementation as the system is built up from 1 to 4608 PSAPDs.The program features real time data quality monitoring capabilities. Energy and flood histograms of each PSAPD are monitored in real time, as well as the charge histogram for each channel. This allows the user to detect any hardware problems and configuration errors prior to the completion of experiment.In order to view flood histograms collected data needs to be corrected for pedestals. The program employs a pedestal estimation algorithm by fitting a Landau function to the histograms. It is also possible to use this algorithm to detect changes in pedestal values during data acquisition.A circular buffer supports real time data monitoring. Changes in the display settings, like photopeak windowing, require refilling the histograms. In order to do this as fast as possible, a number of events are stored in a circular buffer with data split into separate groups, one for each PSAPD. When a histogram of a certain PSAPD needs to be updated, the buffer pulls out the data of that specific PSAPD. This approach reduces the time it takes to update a histogram as opposed to reading data from disk or waiting for new events. It also prevents unnecessary data transfer between different parts of the program.Using one of the data acquisition boards developed, we tested the throughput capabilities of the program. The maximum throughput is limited by the hardware to 64Mbits/s. This throughput corresponds to roughly 228000 events per second.Due to processing overhead, real time online monitoring ability of the program is limited to 120000 events per second. During the test none of the events were dropped due to processing and plotting tasks.

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