Real-time diaphragm navigation using reflected power measurements from a multiple channel transmit RF coil on a human 7T
Aaron T Hess1, Christopher T Rodgers1, and Matthew D Robson1

1OCMR, University of Oxford, Oxford, United Kingdom


The reflected power of transmit RF coils is influenced by the position of the diaphragm. In this work the diaphragm position is measured in real-time for every RF pulse with a hybrid approach. The set of reflection coefficients are transformed into a diaphragm position using a series of MR diaphragm navigators at the start of the pulse sequence in a learning cycle. We demonstrate high quality respiratory gated data based on gating via this mechanism using standard SAR monitoring hardware with a real-time lag of 23ms and temporal resolution of 4.5ms.


The electrical characteristics of RF coils are affected by the objects they couple to. This has been used to detect respiratory and cardiac motions (1,2). We hypothesize that using vendor provided parallel transmit (PTX) SAR monitoring hardware (Siemens 7T) the reflected power can be monitored on every RF pulse and we can relate this measurement to motion using MR images. This strategy uses an MR image based learning interval to provide a hybrid method to quantify in real-time the diaphragm position at a 4.5ms (image TR) temporal resolution.


The Siemens 7T PTX local SAR monitor samples the forward and reflected power of all RF pulses on each transmit channel via directional couplers that are then digitized by the imager. A programme was written on the image reconstruction computer to calculate the reflection coefficients (Sii) at the end of each RF pulse on all eight transmit channels. A change in Sii occurs when the diaphragm changes position, this Sii change is transformed into a quantitative diaphragm position using a series of MR images to measure the diaphragm position during an initial learning interval.

To assess the method two experiments were setup on a human 7T scanner (Siemens). The first, to assess the accuracy of the diaphragm position, and the second to evaluate whether this operates effectively within a real-time cardiac cine acquisition. Five healthy volunteers were scanned according to our institutions ethical guidelines. A sagittal gradient echo (GRE) acquisition placed over the right hemi-diaphragm with a TR/TE of 4.5/1.9ms, image matrix of 304x156, resolution of 1.0 (HF) x 1.9mm (AP), and flip angle ~2°. The image was repeated 513 times every 321ms for 2min 45s. The eight complex Sii were calculated from the central 10μs of each RF pulse. The diaphragm edge was measured in each image. The first 20s of measurements were used as a learning cycle by performing a linear regression between the set of Sii and the diaphragm edge. In the regression the complex Sii was separated into real and imaginary components. One image, every 8s, was used as an absolute position reference to remove effects of systematic drift in Sii.

In the second experiment a pulse sequence was constructed to include a diaphragm image GRE navigator (3) that runs repeatedly for 20s at the start of the sequence with three dummy excitation pulses between each navigator. After this the navigator was repeated every 8s to measure and account for systematic drift in Sii (as illustrated figure 1). The navigator duration was 66ms, resolution of 1.4mm (HF) x 15mm (AP), slice thickness of 15mm, flip angle ~2°. The position of the diaphragm was determined using the method above and used with a 5mm window to prospectively respiratory gate the acquisition. A non-gated acquisition was acquired for comparison. A retrospectively ECG gated cine was acquired. Flip angle 12°, FOV 320x300, resolution 1.4x1.4x8mm3, TR/TE 5.8/3.06 ms, bandwidth of 744Hz/pix, and a temporal resolution of 52ms. A Kalman filter was implemented in both experiments to increase the confidence when an offset is applied.


For experiment 1: figures 2 and 3 show representative traces of the diaphragm position measured with the Sii method compared to that measured in the images for subjects 3 and 2 respectively, figure 4 lists the mean ± standard deviation of the difference between the Sii diaphragm position and that measured in the images without filtering. The last column of the table shows the accumulated system drift over the scan.

For experiment 2: figure 4 shows a single cardiac cine frame for non-diaphragm-gated and diaphragm-gated cine acquisitions. There was a delay of 23ms due to processing and networking constraints between measurement of the diaphragm position and updating the RF and gradient waveforms.

Discussion and Conclusion

For all subjects except the outlier subject 2, the mean±SD of the difference between the image and Sii prediction was -0.1±1.2mm. For subject 2, who’s trace is shown in figure 3, both the image and Sii measures indicate a large unusual motion, perhaps coughing at the end of the scan.

This method poses a number of benefits over conventional respiratory monitoring, i) it requires no additional hardware as it uses the existing RF coil and SAR monitoring hardware, ii) the pulse sequence maintains a steady state, iii) diaphragm positions are known with a real-time lag of 23ms

In conclusion, real-time diaphragm navigation has been demonstrated using vendor provided PTX local SAR monitoring hardware complemented by a position learning phase then infrequent navigator images.


Both MD Tisdall and AJW van der Kouwe contributed code for the diaphragm navigator. Stefan Neubauer and Elizabeth Tunnicliffe for guidance. The Medical Research Council for funding.


1. Buikman, D., Helzel, T., Röschmann, P. The RF coil as a sensitive motion detector for magnetic resonance imaging. Magn. Reson. Imaging. 1988;6:281–289.

2. Kudielka GP, Hardy CJ, Vuissoz P, Felblinger J, Brau A. “Utilization of the Receive Coil for Cardiovascular and Respiratory Motion Representation”. ISMRM 2015 pp 705.

3. Hess AT, van der Kouwe AJW, Tisdall MD, Neubauer S, and Robson MD. “2D Diaphragm Navigation with Rapid Gradient Echo Images: Validation at 3T and Application at 7T”, ISMRM 2015, pp 2565.


Figure 1: Timing schematic for diaphragm navigated pulse sequence, with a 20 s learning cycle consisting of 264 repeated GRE navigators, each of 66 ms duration. The diaphragm position is calculated and provided to pulse sequence for every imaging RF pulse.

Figure 2: Trace of diaphragm position measured for every RF pulse using the coil reflection and measured with GRE images acquired with those RF pulses. No filtering is performed for this plot. The difference between these measures is plotted for both drift corrected data and non-drift corrected data.

Figure 3: Trace from subject two showing a large system drift which is tracked by the 8 s updates, at the end a large change is shown in both the reflection and image based measures, possibly the subject was coughing or moving.

Figure 4 / Table 1: Mean and standard deviation (SD) of the difference between diaphragm predition with coil reflection to that measured in the images. Total drift correction applied over the time cource of the scan from all 8s updates

Figure 5: Two retro gated cine cardiac short axis images, one acquired with no diaphragm-gating and the second is diaphragm-gated with a 5 mm acceptance window at end expiration.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)