Alan B McMillan1, Lloyd Estowski2, Ty A Cashen2, R. Marc Lebel3, Xinzeng Wang4, Ersin Bayram4, and Ali Pirasteh1
1Radiology, University of Wisconsin Madison, Madison, WI, United States, 2Global MR Applications & Workflow, GE Healthcare, Waukesha, WI, United States, 3Global MR Applications & Workflow, GE Healthcare, Calgary, AB, Canada, 4Global MR Applications & Workflow, GE Healthcare, Houston, TX, United States
Synopsis
We present a single-shot fast-spin-echo pulse-sequence (SSFSE) with short-tau inversion recovery (STIR) and deep-learning
(DL) image reconstruction that provides whole-body images in less than 3
minutes, achieving uniform fat suppression, detailed delineation of the anatomy,
and favorable signal-to-noise (SNR). This was achieved through the reduction of
echo-train-length by increasing the acceleration to a factor of 4x, hence substantially
improving image quality through blurring reduction in the phase encoding
direction, and ameliorating the subsequent SNR reductions by utilization of a DL
reconstruction algorithm. This whole-body MRI technique can be used in the
setting of various pathologies for both pediatrics and adults.
INTRODUCTION
There is an increasing clinical
demand for whole-body (WB) MRI for a variety of purposes, including diagnosis/staging/follow-up
of malignancies, as well as screening of high-risk patients [1]; one common example is detection
of osseous lesions in the setting of breast cancer, renal cell carcinoma, and
multiple myeloma. Utilization of WB-MRI acquisition techniques such as
single-shot fast spin-echo (SSFSE) are necessary to improve patient throughput
and reduce motion artifacts. However, single-shot techniques are subject to
some challenges; for example, the possible induction of high RF power
deposition exhibits objectionable blurring in the phase encoding direction due
to vastly extended echo train lengths (ETLs). These effects can be mitigated to
some extent by the use of variable flip angle schemes [2, 3],
but still limit the overall performance and achievable ETL. The use of parallel
acceleration techniques can reduce the ETL by a factor of 2x or
more, however with at the cost of substantially-reduced signal-to-noise (SNR).
Recently, deep learning-based reconstruction approaches have demonstrated
promise to recover image SNR. Therefore, the purpose of this work is to study
highly-accelerated SSFSE for WB-MRI using a deep learning-based reconstruction.METHODS
Imaging studies were performed on
a 1.5T MRI scanner (Artist, GE Healthcare, Waukesha, USA), using a whole-body
coil suite (head/neck unit, AIR anterior array, posterior array; GE Healthcare).
A SSFSE-STIR pulse sequence was optimized in the coronal plane with the
following parameters: TR = 571 msec, TE = 96 msec, TI = 150 msec, ETL = 107 matrix
= 288 x 224 (frequency x phase), voxel
size = 1.5 x 2 x 4 mm, bandwidth = 83.3 kHz, craniocaudal coverage per station
= 44 cm, oversampling = 1.2, tailored RF pulse, scan time per station = 24 sec.
Imaging was repeated with increasing acceleration (ARC = 4) to reduce blurring;
TR was adjusted to 476 msec, and ETL decreased to 82, yielding a scan time of
20 sec per station. Three stations were scanned to cover the anatomy from skull
vertex to thighs. Images were reconstructed using both the conventional as well
as a prototype deep-learning (DL Recon, GE Healthcare) reconstruction
algorithms. Images at different stations were fused to generate a single set of
whole-body images. RESULTS
An increase in image acquisition
acceleration (from 2x to 4x) resulted in markedly reduced blurring, evidenced
by a favorable delineation of the vasculature, nerves, and the contour of
various organs, such as the spine, liver, spleen and kidneys in an example case
(Figures 1-3). However, this improvement was observed at the cost of increased
image noise and a less favorable SNR using conventional accelerated
reconstructions. The implementation of the DL Recon algorithm achieved a
substantially appreciable reduction in noise level without the loss of fine
detail in the image; in fact, some structures, such as the liver vasculature,
are better evaluated after the implementation of the DL reconstruction
algorithm.DISCUSSION/CONCLUSIONS
Development of a rapid
whole-body MRI pulse sequence with robust fat suppression and the ability for
tumor detection is an area that has been challenging due to the limitations of
the conventional pulse sequences and image reconstruction algorithms; often SNR
and image detail are compromised by limits on image acquisition time. The
presented SSFSE-STIR acquisition with DL reconstruction shows great promise, as
it provides whole-body images in less than 3 minutes, with uniform fat
suppression, detailed delineation of the anatomy, and favorable SNR. The
reduction of ETL, enabled by increasing the acceleration to a factor of 4x, greatly
improves image quality by reducing the blurring in the phase encoding direction
for which SNR reductions due to acceleration are ameliorated by the DL
reconstruction algorithm. This sequence can be used as an excellent complement
to PET in the setting of PET/MRI, or in whole-body MRI without PET in the
setting of various pathologies, such as cancer or systemic inflammatory
conditions. The rapid nature of this acquisition is highly applicable to both pediatric
and adult patients.Acknowledgements
No acknowledgement found.References
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