Kristina M. Pelkola1,2, Tess E. Wallace1,2, Peter J. Morriss1,2, Monet E. Dugan1,2, Camilo Jaimes1,2, Onur Afacan1,2, and Simon K. Warfield1,2
1Radiology, Boston Children's Hospital, Boston, MA, United States, 2Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, United States
Synopsis
Magnetic resonance imaging
(MRI) has become an increasingly popular imaging exam for pediatric patients
due to its high resolution, multiplanar capabilities, and lack of ionizing
radiation [1]. MRI is highly beneficial by providing detailed imaging to assist
in the diagnosis and treatment plan for patients, but before a sequence becomes
part of an established clinical protocol, there are many trials performed and
data collected. Novel sequences, such as the motion robust imaging technique
MPnRAGE [2], are emerging to help alleviate clinical imaging dilemmas, like
motion artifacts, but this data needs to be examined and understood to make
advancements.
Background
Before a sequence is
employed for patient care purposes, it's considered
a work in progress (WIP) and is investigated. With the data collected, scientists can make
improvements and ultimately create an established sequence impacting patient care. In pediatric MRI, WIP sequences help support advancements like increasing image quality, decreasing scan duration, and eliminating motion artifacts. Diagnostic pediatric MRI exams
are challenging to achieve due to patient fear and anxiety which causes motion. Repeated exams are costly and delay the diagnosis and treatment. Due to these challenges, anesthesia is administered, but also incurs motion artifacts, increased cost, schedule delays, and risks of
adverse events. There is a growing need to evaluate new
concepts that may be helpful in reducing these issues while preserving diagnostic image quality. The evaluation of novel imaging strategies
benefits from both qualitative assessment by expert radiologists, and from
quantitative assessment of image quality measures. The use of objective image
quality measures can allow for rapid assessment of different image acquisition
parameters and different reconstruction algorithms, enabling the evaluation of
a much broader range of imaging strategies with less time and cost than could
be carried out relying on expert evaluation alone. In this work, we
investigated the image quality measures to compare two 3D high-resolution T1
structural imaging sequences, an established sequence, Cartesian
MPRAGE, and a WIP sequence, radial MPnRAGE, which can alleviate motion artifacts. Method
A cohort of subjects undergoing routine clinical MRI was recruited, consented with an appropriate institutional review
board (IRB) protocol, and acquired the established MPRAGE and WIP MPnRAGE. At exam completion, images were electronically
transferred into FlyWheel software, a cloud based computationally enabled
research DICOM service, where each sequence was assessed using MRIQC, a
previously described and validated software application which provides image
quality metrics (IQMs) for structural T1and T2 weighted sequences [6]. When
visually comparing the images of the MPRAGE and MPnRAGE sequence (Fig.1), it is
observed that the images offer similar contrast and spatial resolution. Image
quality metrics (IQMs) of signal to noise ratio (Dietrich’s) (SNRd) and
contrast to noise ratio (CNR) were measured
with MRIQC [6]. SNRd is defined by MRIQC based on Dietrich’s description of the
mean of the signal in a region of interest, divided by an estimate of image noise
computed from an automatically determined region of interest in the background [6].
This routinely employed quantification can assess imaging hardware, protocols,
and acquisition sequences [6][7]. CNR is a measurement of the image contrast to
the background noise. This is calculated by measuring the mean signal intensity
of two regions of interest in an image, subtracting them and normalizing by a
pooled measure of the standard deviation which reflects noise power. The
ability to identify subtle features in images improves as the SNR and CNR
increase. A visualization of the data that allows for assessment of similarities
or differences between imaging techniques is the Bland-Altman plot. To display
the findings, the SNRd and CNR data collected were each imported into a Bland
Altman plot respectively. “The Bland Altman plot assesses a bias and estimates an agreement interval, within which 95% of the
differences of the second method, compared to the first one, fall” [3,9]. We
created Bland-Altman plots for the SNRd and CNR of the MPRAGE and a particular
setting of the WIP MPnRAGE for 15 subjects. The bias, defined as
the average of the differences between the two data sets, and the standard
deviation of the differences were determined and used to calculate the lower
level of agreement (LOA) and upper LOA. The lower and upper LOAs were
determined utilizing the equations in Figure 2. These calculations were then plotted displaying the difference and mean of SNRd and CNR between the
established MPRAGE and novel MPnRAGE. Results
Data was collected
from 15 participants, who received both the established Cartesian MPRAGE
sequence and the novel radial MPnRAGE sequence. Previously stated, in Figure
1 the images from the MPRAGE and MPnRAGE look similar in contrast. Visual
differences can be altered by display settings, so it is valuable to make
quantitative assessments. The Bland Altman plots help display the SNRd and CNR
in the images (Figures 3 and 4). The data displayed offers insight into mean
differences of SNRd and indicate an elevated variance in the background of the work-in-progress
sequence. This finding through quantitative measurements then suggests the need
for further investigation to identify the source of the elevated signal in the
background, which may arise from aspects of the image reconstruction pipeline
or suggest other changes in acquisition parameters be made to further increase
image quality. Non-cartesian reconstructions may exhibit reconstruction artifacts
differently to Cartesian acquisitions and may warrant different assessment
strategies. Conclusion
With the rising demand for increased image quality in pediatric MRI imaging, it is imperative to
continue exploring new imaging techniques and compare them to the gold
standards of today. The availability of quantitative measurements can aid in
understanding and optimizing imaging parameters. Plots such as the Bland Altman
are easy to review and conclude where improvements could be made. With these
plots, improvements can be put forth to increase the image quality of the next
cohort. Acknowledgements
This research was
supported in part by the
following
grant: NIH-2R01EB019483-05A1. References
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