Andrew Dupuis1, Yong Chen1, Rasim Boyacioglu1, John Stairs2, Michael Hansen2, Kelvin Chow3, and Mark A Griswold1
1Case Western Reserve University, Cleveland, OH, United States, 2Microsoft, Redmond, WA, United States, 3Siemens Medical Solutions USA, Inc., Chicago, IL, United States
Synopsis
Reconstruction
of 3D Magnetic Resonance Fingerprinting acquisitions is computationally
demanding, resulting in long processing times. GPU parallelization of
the reconstruction’s NUFFT, pattern matching, and coil combination steps
improves performance, but traditionally requires high-performance
computers at the scanner. We propose an online reconstruction on a
remote GPU-accelerated Kubernetes cluster. This allows many scanners or
sites to share easily upgradeable and manageable computing resources.
Additional calibration measurements, such as B1 maps, can also be
transferred to allow inline B1 inhomogeneity correction. We also
demonstrate that 3D-MRF reconstruction is robust with raw data
compression that can be used to reduce site-to-cloud bandwidth
requirements.
Purpose
Without
adequate parallelization, 3D-MRF image reconstruction is currently too
computationally intensive to be useful as a routine clinical tool. GPU
implementations of the 3D-MRF reconstruction’s NUFFT, pattern matching,
and coil combination steps substantially improve this performance
deficit [1], but require high-performance computers with substantial
amounts of GPU memory. Dedicated computational resources are substantial
investments with limited lifespans that quickly become cost-prohibitive
to install and maintain across a healthcare system.
To resolve
these speed and logistical concerns, we have implemented a
GPU-accelerated Gadgetron reconstruction [2,3,4] that is capable of
running remotely within a Kubernetes cluster hosted on Azure [5,6].
Kubernetes enables container-based deployment of Gadgetron as a managed
appliance [8], while the cluster architecture allows for rapid resource
scaling and load balancing, with scanners at multiple sites sharing a
reconstruction “service” endpoint. This approach enables us to scale our
3D-MRF research to multiple sites without additional hardware
deployments. Our implementation also supports B1 mapping for online B1
correction, and demonstrates that 3D-MRF raw data can be compressed for
use on low-bandwidth networks without introducing additional lag or
reducing image quality.Methods
An
ISMRM/NIST system phantom and a healthy volunteer (45 year old, male)
were imaged using a prototype FISP 3D-MRF sequence with a 250x250x120mm3
field of view, a 1x1x2mm3 spatial resolution and a factor of 2
interleaved partition undersampling on a 3T scanner (MAGNETOM Vida,
Siemens Healthcare, Erlangen, Germany). The total acquisition time was
~5.5 min. B1 maps were acquired first, with reconstruction performed by
the standard Siemens processing. B1 maps were automatically transferred
to the Kubernetes cluster for use in the B1-corrected reconstruction
[7].
Acquired data was sent from the scanner to the remote
Gadgetron cluster using the Framework for Image Reconstruction
Environment (FIRE) prototype with an SSH tunnel to a load-balancing jump
node managing cluster ingress. SNR-constrained data compression was
tested to evaluate possible reduction of necessary network bandwidth
without substantial image degradation [9]. Data was then reconstructed
by a Gadgetron appliance, with reconstructed maps returned via the same
SSH tunnel.The network infrastructure and gadgets used within our
reconstruction pipeline is shown in Figure 1.
Primary
considerations for our remote reconstruction implementation were
reconstruction quality, B1 mapping support, processing speed, and
network bandwidth. Bandwidth was measured via Azure’s Kubernetes metrics
interface. Processing speed was measured by timing the delay
acquisition completion and image availability on the scanner’s host
interface - which also reflects the delay between acquisition and the
availability of the DICOM reconstructed images.
Results
In
Figure 2, previously validated offline reconstructions in
MATLAB(R2021a, The MathWorks, Natick/MA, USA) [1] were compared to the
proposed Gadgetron implementation both visually and via comparison of
the reconstructed T1 and T2 values, and our results suggest a good match
between the two approaches.
In Figure 3, the impact of the
implemented online B1 correction method is demonstrated for in-vivo
acquisitions, with inclusion of B1 mapping data in the online
reconstruction yielding visible correction in T2 maps, with no change in
reconstruction time.
Figure 4 demonstrates the differences in T1
and T2 maps caused by SNR-constrained data compression.With compression
disabled, phantom reconstructions finished in 52 seconds with mean
network throughput of 138Mbps, versus 54 seconds and 70Mbps at 1% SNR
compression error tolerance. Versus the maps generated with uncompressed
data, compression introduced 0.00±0.09% error in T1 values and
0.00±0.04% error in T2 values.
In-vivo, online reconstructions
without compression finished in 55 seconds with mean bandwidth of
212Mbps, compared to 56 seconds and throughput of 75Mbps with 1% SNR
compression error tolerance. The increased bandwidth for in-vivo can be
attributed to the use of additional coils. Versus the maps generated
with uncompressed data, compression introduced 0.01±0.2% error in T1
values and 0.02±1.17% error in T2 values.
Figure 5 demonstrates
the pipeline’s ability to reconstruct 32-channel head coil acquisitions
without substantially increased delay. Compared to the 20-channel head
coil, with mean in vivo reconstruction delay of 55 seconds and mean
network throughput of 212Mbps, the 32-channel coil acquisition’s
reconstruction delay was 1m33 seconds with mean throughput of 368Mbps
without compression, 98Mbps with 1% compression error tolerance.
Discussion
Reconstruction
of 3D-MRF can be performed remotely on a GPU-accelerated Kubernetes
cluster with 1-1.5 minute between acquisition completion and
availability of quantitative maps at the scanner. Reconstruction is
accelerated by GPU implementations of the NUFFT, pattern matching, and
coil combination steps, and image quality is improved through online B1
correction. The proposed pipeline simplifies deployment to new scanners
by eliminating the need for on-site computing resources and unified
cloud computational resource management streamlines reconstruction
software updates. Finally, 3D-MRF seems to be extremely insensitive to
SNR-based data compression, implying that sites with relatively slow
(sub-100Mbps) internet connections should be able to make use of
cloud-based reconstructions without compromising image quality.
Therefore, this work demonstrates that cloud reconstruction of 3D-MRF
datasets is not only feasible, but may also improve processing time and
deployability of the technique in research and clinical workflows.Acknowledgements
This work was supported by Siemens Healthineers and Microsoft Corporation.References
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