Takayuki Masui1, Yudai Tokunaga1, Toshiyuki Hirano1, Masashi Sugimura1, Mitsuharu Miyoshi2, Tetsuya Wakayama2, Masako Sasaki1, and Haruo Isoda1
1Radiology, Seirei Hamamatsu General Hospital, Hamamatsu, Japan, 2Global MR Clincal Solutions and Research Collaborations, GE HealthCare, Hino, Japan
Synopsis
Keywords: Pancreas, Pancreas
Motivation: A clinical 3T MR system is preferably utilized due to higher image quality and better recognition of lesions and anatomy compared to 1.5T MR.
Goal(s): Our goal was to show improved abilities of 1.5T with deep learning reconstruction (DL) in evaluation of the pancreatobiliary regions comparing 3T without DL.
Approach: In 9 patients, image qualities and information of vasculatures and soft tissues of dynamic Gd-contrast images of pancreatobiliary regions on 1.5T with DL were compared to those previously obtained on 3T without DL.
Results: 1.5T systems with DL can provide competitive information to those with 3T systems without DL for the pancreatobiliary regions.
Impact: DL reconstruction can fully utilize a 1.5T MR,
providing competitive information to a 3T MR without DL for dynamic contrast
studies in the pancreatobiliary regions. This can enhance patient-throughput
without major socio-economic investment.
Introduction
Breath-hold dynamic
contrast MR study has provided information of characterization of the
pancreatobiliary lesions. Because of high SN, 3T systems might be preferably performed. Various features in
clinical application of deep learning reconstruction (DL) have been introduced.
Recent available DL can improve image quality with noise reduction and sharp edges of soft tissue and vasculatures(AIRTM Recon DL, GEHeathCare:GEHC)(1). Information with 1.5T
using DL might be competitive to those with previous obtained on 3T without DL.
Accordingly, the purpose was to assess efficiency of DL in dynamic contrast
study of pancreatobiliary regions for
the evaluation of soft tissues and vasculatures on 1.5T in comparison
with on 3T without DL.Materials and Methods
The study was approved by IRB and informed consent was obtained.
Population: Among patients undergoing previous 3T MR imaging
[ 6 in normal bore of 3T (GEHC) and 3 in wide bore (GEHC)] of pancreatobiliary
regions, 9 patients (5men, 4 women, mean 69years old) were selected, who
underwent dynamic contrast MR imaging with DL on 1.5T. Pathologies were
pancreatic cystic 7 including 5 IPMN, Autoimmune pancreatitis 1, status post
resection of pancreatic tumor 1, Among them, Polyp in the gall bladder 1, and
adenomyomatosis 1.
MR imaging: A 1.5T system (Explorer, GE HealthCare, Milwaukee, 16 channel
phased-array multicoil) was used in Breath-hold (BH) dynamic contrast imaging
covering the liver and pancreas in transverse plane using Turbo LAVA with ARC
and CS using spec IR for fat-sat. The parameters were as follows; ARC
factor 2x1, CS factor 1.1, TR 3.3ms, Matrix 256x164, thickness -3.4mm/-50%,
imaging time10seconds for one phase. Consecutive 2 phases of images were
obtained during one BH after injection of nonspecific Gd-chelate (Gadovist,
0.1mmol/kg body weight, injection rate 1mL/sec) followed by saline flash. With
Smart prep (GE HealthCare); five seconds after contrast arrival at the level of
the diaphragm and 60sec after triggering of the first phase, dual phase imaging
was obtained for arterial and portal phases. The data were postprocessed with DL
(1). Original images without DL were generated. Parameters for 3T were as
follows, ARC factor 2x1.25, CS factor 1.1, TR 3.3ms, Matrix 288x200, slice
thickness 3-3.4mm/-50%.
Evaluations: Three sets of MR images in precontrast, first phase
in the arterial phase, and 3nd phase as in the portal phase were selected and
evaluated. Subjective evaluations: Overall mage quality (IQ) and artifacts
(blurring & nose) were evaluated using five-point scale (1 non-diagnostic
or sever artifacts to 5 excellent quality or no blurring. With coronal
reformation, Edge of liver, pancreas, spleens and small and large bowels were
evaluated regarding sharpness. Conspicuity of the lesions in the pancreas was
also evaluated. Vasculatures: Partial MIP was applied with reviewer-dependent
slab thickness and orientation. Selective recognition of aorta and its branches
in 1st phase, Portal vein (PV), SMV, Branches of PV in portal phase were
evaluated. In subjective and objective evaluations, Wilcoxon ranked test was
used with Bonferroni correction.Results
IQ in 2nd and later phases was good and blurring
artifacts were less recognized on 1.5 T with DL and those on 3T (Fig 1-3) (IQ
4.3 vs 4.4, NS). Noise and artifacts were less on images with DL on 1.5T(4.5,
4.5). SI were not significantly different between images with and without DL,
but SD of SI was less on images with DL than those without DL. Objective SI
were highest on 3T among all images in pre, arterial and portal phases (Figs).
In MIP, recognition of arteries and portal veins and other veins best on 1.5T
with DL(Fig1-3). Recognitions of the cystic lesions were not significantly
different between those on 1.5T with DL and on 3T. Contrast of lesions against
the parenchyma was highest on 3T. Visual recognitions of lesions on 1.5T with
DL and on 3T were not significantly different.Discussion
Currently improvement of IQ and recognitions of vasculatures
including portal veins and arteries and soft tissues on dynamic contrast MR
imaging with DL on 1.5T could be made. DL can reduce noise and improve sharp
delineations of organs in coronal reformations. Information on 1.5T with DL may
be competitive to those previously obtained on 3T systems. Using both1.5T and
3T systems in imaging suites, expected equal performance of both MR systems can
give great advantages for patient-throughputs. Efficient investment might be
made with keeping 1.5T systems with DL instead of renewing 3T systems.
Limitations: Study population was limited. further studies in larger population
should be performed. Conclusion
Application of DL on 1.5T can improve IQ and recognitions of soft tissues and vasculatures, which may be competitive to those on 3T without DL. Acknowledgements
No acknowledgement found.References
1)Saleh M. et al. J Comput Assist Tomogr 2023 47 721