Keywords
Neuroimaging, seasonal variation, MRI QA, MRI, reproducibility
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Cheng CP and Halchenko YO. A new virtue of phantom MRI data: explaining variance in human participant data [version 1; peer review: 1 approved, 2 approved with reservations, 1 not approved]. F1000Research 2020, 9:1131 (https://doi.org/10.12688/f1000research.24544.1)
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Research Article
A new virtue of phantom MRI data: explaining variance in human participant data
[version 1; peer review: 1 approved, 2 approved with reservations, 1 not approved]
PUBLISHED 14 Sep 2020
OPEN PEER REVIEW
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This article is included in the INCF gateway.
Abstract
Corresponding authors:
Christopher P. Cheng, Yaroslav O. Halchenko
Competing interests:
No competing interests were disclosed.
Grant information:
YOH was supported by NIH-NIBIB [P41EB019936]. CPC was supported by Dartmouth College’s Sophomore Research Scholarship. The datasets from Dartmouth College’s Psychological and Brain Sciences Department Chang and Haxby Labs were acquired as part of the work supported by NIH [R01MH116026, R56MH080716, and R01MH116026]. Gobbini’s lab’s data acquisition was supported by NSF [1835200].
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Copyright:
© 2020 Cheng CP and Halchenko YO.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Cheng CP and Halchenko YO. A new virtue of phantom MRI data: explaining variance in human participant data [version 1; peer review: 1 approved, 2 approved with reservations, 1 not approved]. F1000Research 2020, 9:1131 (https://doi.org/10.12688/f1000research.24544.1)
First published: 14 Sep 2020, 9:1131 (https://doi.org/10.12688/f1000research.24544.1)
Latest published: 14 Sep 2020, 9:1131 (https://doi.org/10.12688/f1000research.24544.1)
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Introduction
Magnetic resonance imaging (MRI) is an important data acquisition technology used to unravel the mysteries of the brain, and it is very complex. The exact constitution of MRI signals is not entirely known since it could also potentially be affected by factors such as temperature and humidity variations across seasons1. Notably, a study by Meyer et al. noted that seasonal variations may not even correspond to the four seasons, indicating a complicated relationship between environmental factors and brain functions2; thus, whether or not this variance in MRI is due to scanner effects or biological causes is unclear. This unexplained variance in MRI data hinders the statistical power of MRI studies and affects their reproducibility.
MRI quality assurance (QA) metrics are indicators of the condition of the scanner at the time of a given scan, and are used for quality control in MRI centers3. In cases of significant deviation from the norm, MRI personnel look into resolving underlying hardware or software issues. Otherwise, QA results are not used for anything else, and not shared alongside large shared datasets, such as Human Connectome Project (HCP)4 or the ABCD study where data is acquired across different scanners and potentially affected by scanner idiosyncrasies. It is typically unknown how seasonal and operational factors affect different types of scanning (on phantom and real subjects). We hypothesize that there may be a relationship between the QA metrics of a scanner (obtained on a phantom) and the characteristics of the MRI scans (on human participants), which affect the consecutive data analysis results and drawn conclusions.
The purpose of this study was to evaluate if phantom data could be used as a useful proxy for overall scanner operational characteristics that can help explain variance in real human subject data acquired using the same MRI scanner on dates nearby phantom QA scans. The first stage of this study analyzed the influence of phantom scanning parameters on signal-to-noise ratio (SNR), which is known to be a fundamental QA metric for MRI. The second stage of this study used SNRs from stage one from phantom data to model morphometric measures obtained using human MRI data.
Methods
Ethical statement
This study was approved by the Dartmouth Committee for the Protection of Human Subjects (CPHS 31408). Data collection in the individual studies was approved by the same committee (CPHS 17763, 28486, 29780, 21200 and 30389). All participants gave written informed consent for participation and re-analysis of data.
Human participants
We used the data from 206 participants (261 scans) collected from October 30, 2017 to August 28, 2018 who participated in five studies17–20 of three labs at the Dartmouth Brain Imaging Center (DBIC). Participants ranged from 18–64 years of age. There were 78 male and 128 female participants. The MRI scanner used was the 3.0 Tesla Siemens MAGNETOM Prisma whole-body MRI system from Siemens Medical Solutions. Human participant and phantom data were collected using a 32-channel head coil.
Phantom
The DBIC collects MRI QA data weekly (typically each Monday) on an agar phantom. For the purposes of this study we did not use DBIC QA estimates but carried out QA using MRIQC BIDS-App7.
QA data, converted to a BIDS dataset (including original DICOM data under sourcedata/), is available as a ///dbic/QA DataLad dataset8. Subject “qa” within that dataset contains data for the agar phantom used in weekly QA scans. QA scans contain a single T1 weighted anatomical image (192×256×256 matrix at 0.90×0.94×0.94mm) and two functional T2* weighted echo planar imaging (EPI) scans (80×80×30 matrix at 3.00×3.00×3.99mm with 200 volumes acquired with time of repetition (TR) of two seconds; not used in this study).
Data preparation
All data at the DBIC is collected following the ReproIn convention on organizing and naming scanning protocols9. To guarantee that the data would not contain variance caused by different conversion software versions through time, data from all phantom and human subjects was reconverted from raw DICOMs into BIDS datasets using consistent versions of ReproIn/HeuDiConv and dcm2niix10. All phantom QA data was re-converted using ReproIn/HeuDiConv with dcm2niix (v1.0.20171215 (OpenJPEG build) GCC6.3.0), and human data from different studies re-converted using ReproIn/HeuDiConv (v0.5.3) with dcm2niix (v1.0.20181125 GCC6.3.0). HeuDiConv is programmed to automatically extract many acquisition and scanner operation parameters from DICOMs and place them alongside neuroimaging files in the BIDS dataset. For the purposes of this study, a subset of those parameters was selected as variables of interest for analysis based on prior knowledge regarding which variables could potentially affect the collected data (see Table 1). Furthermore, we added seasonal effects by using NumPy 1.18.4 inserting sine and cosine waves into the model with a period of one year to roughly estimate the four seasons; arguably, this was a very simplistic model due to our data’s short duration of under two years. Our data’s limited time range precluded us from using more elaborate seasonal models, and as noted in the introduction, seasonal effects may not exactly correspond to the four seasons. Still, we felt a simplistic representation of seasonal effects could help indicate the possibility of further investigation.
Table 1. Quality assurance metrics of interest in this study, categorically divided by interest.
We used MRIQC (v0.14.2)7 on both the QA phantom and the human data from October 30, 2017 to August 28, 2018. MRIQC provided us with proxy measures of scanner operation characteristics, such as total SNR for anatomicals. Figure 1 presents a correlation structure between all variables of interest for phantom MRI data visualized using Seaborn 0.10.1. DataLad11 with the datalad-container extension12 was used for version control of all digital objects (data, code, singularity containerized environments), and all code and shareable data were made available on GitHub (see Data availability22 and Code availability21) with containers and data available from the ///con/nuisance DataLad dataset. At the moment we have concentrated on analysis of anatomical data, so only T1w images from phantom and human participants were used.
Figure 1. Pearson correlations between different variables on phantom MRI phantom data.
Refer to Table 1 for explanation of abbreviated variables.
We used DataLad to run a modified version of the simple_workflow container and script13, which extracts certain segmentation statistics of the brain from real human MRI data. These include metrics relating to the accumbens area, amygdala, caudate, hippocampus, pallidum, putamen, thalamus proper, cerebrospinal fluid, and the gray and white matter in the brain. The original simple_workflow container is fully reproducible (frozen to the state of NeuroDebian as of 20170410 using nd_freeze) and uses FSL 5.0.9-3~nd80+1.
The free open source software facilitating our data preparation was Pandas 1.0.4.
Data modeling
Ordinary least squares (OLS) regression, as implemented in StatsModels Python package (v 0.9.0)15, was used to model target variables of interest. As part of the modeling, certain interdependent variables were orthogonalized to account for possible covariance (in the order presented in Figure 1; seasonal variation data was orthogonalized last) using NumPy 1.18.4. The extent to which an independent variable affects the dependent variable was assessed using a t-test for single-valued variables, and using an F-test for arrayed values (such as patient position in the scanner). Subsequently, the explanatory power of the scanning parameters and characteristics on the phantom QA metric (snr_total) as the dependent variable was evaluated.
Next, a segmentation statistic - gray brain matter on human participant data - was modeled using a proxy QA measure from phantom data (snr_total) and a scanner characteristic of the human participant scanning session (IOPD), demographics (age, gender), and seasonal effects. Gray brain matter was chosen because either gray or white brain matter were deemed likely to yield a statistically significant relationship. Because phantom QA data was acquired typically only each Monday, its value was interpolated in time to obtain values for the dates of human participants scanning. After modeling gray brain matter, other structures (such as white matter, cerebrospinal fluid, and subcortical regions) were analyzed. Our reasoning was that if gray brain matter yielded a significant result, then other brain segmentation statistics could also yield significant results, which could subsequently be investigated.
The free open source software facilitating the visualization of our model was Matplotlib 3.2.1.
Results
Statistical significance of variables
We found that we could describe the total SNR of phantom data well with just a limited set of scanner operational characteristics. The R2 value of the model shown in Figure 2 was 0.533. Multiple variables (day time of acquisition, subject position, and SAR) were statistically significant and all survived false discovery rate (FDR) correction, as shown in Table 2.
Figure 2. Model of total signal-to-noise ratio using scanning parameters from phantom data.
R2 value was 0.533, indicating scanning parameters have explanatory power.
Table 2. Independent variables’ original and corrected p-values from the model in Figure 2.
FDR, false discovery rate.
Given that certain scanning parameters and a QA metric were determined to have significant explanatory power in the phantom scans, we decided to proceed to model human participants’ gray brain matter volume using participants' basic demographic data, variables we found significant from phantom data (IOPD), and total SNR. Gray (or white) brain matter was deemed likely to be affected as they are two large “structures”.
After correction, the phantom’s total SNR was found to be a statistically significant (p=0.002 and FDR-corrected=0.003) predictor of gray brain matter, as shown in Table 3. Other scanning parameters and subject characteristics found significant were subject age, sex, weight, as well as seasonal variation in data. Note that the orthogonalization carried out for the model in Table 3 was carried out in the order shown from top-to-bottom.
Table 3. Independent variables’ original and FDR-corrected p-values from the model for gray brain matter volume (Figure 3).
FDR, false discovery rate; SNR, signal-to-noise ratio.
After modeling gray brain matter, we modeled the other structures (background, subcortical regions, etc.), and assessed statistical significance for two independent variables of interest in each model: phantom’s total SNR, and seasonal variables. Results in Table 4 show no statistically significant results after FDR correction across structures except phantom total SNR in gray brain matter; notably, seasonal variables in gray brain matter were not significant.
Table 4. Brain segmentation statistics results where bolded values are statistically significant.
FDR, false discovery rate; SNR, signal-to-noise ratio; CSF, cerebrospinal fluid.
Model selection: investigation of QA metric vs seasonal effects
Given that scanning characteristics, a QA metric and seasonal effects seem to have a statistically significant effect on gray brain matter volume estimates (see Table 3), we decided to compare the fit of the model with and without those independent variables. We did so by removing one of either the QA metric or seasonal effects from the list of independent variables in the model for gray brain matter and observing the fit of the resulting model. From the initial R2 value of 0.242 (Akaike Information Criterion, AIC = 7044; Bayesian Information Criterion, BIC = 7091) with both QA metric and seasonal effects shown in Figure 3, removing the QA metric dropped the model’s R2 value to 0.208 (AIC = 7054, BIC = 7097), and removing seasonal effects resulted in an R2 value of 0.207 (AIC = 7052, BIC = 7092), thus showing that the QA metric and seasonal effects both have similar effects on the fit of the model. However, after removing both the QA metric and seasonal effects, the R2 value drops to 0.185 (AIC = 7058, BIC = 7093), which suggests that they are complementary in terms of explanatory power.
Figure 3. Model of gray brain matter using patient age, patient position in scanner, patient sex, patient weight, total signal-to-noise ratio, and seasonal variation.
R2 value was 0.242. The full fit plot (in red) shows the plot of all independent variables, whereas the partial fit plot (in pink) shows the plot of only the statistically significant variables. The black plot shows the actual fit of the real data.
Discussion
Our results have indicated that the QA metric of the phantom data can be useful beyond routine monitoring of MRI scanner health. Specifically, our use case demonstrates the viability of using a QA metric to predict variance in estimates derived from human MRI scan data. Our results show also that the following scanning parameters: “patient” (in this case, phantom) position in scanner, day time of acquisition, and specific absorption rate; were statistically significant predictors of the phantom QA metric: total SNR. In turn, the phantom total SNR ratio was a statistically significant predictor of gray brain matter volume, even in the presence of actual data parameters relating to the patient such as age, sex, and weight. It seems that effects depend on the scale of data; in our data sample the uncorrected phantom QA metric provided an explanation for coarse “structures” (such as all of the gray brain matter) but failed to significantly explain any subcortical structure estimate.
Furthermore, we provide further support for the idea that seasonal variations affect human data. The initial R2 value of the QA metric-human data model was 0.242; yet, by removing seasonal effects from the model, the R2 value dropped to 0.207, suggesting that including seasonal effect data is useful for attributing variance in MRI data. It should be noted that the effect of the proxy scanner health metric seems to have a similar magnitude as that of seasonal variation effects, given that the removal of the QA metric from the model also results in a similar decline in the R2 value to 0.208. However, we determined that this effect of seasonal variation is distinct from that from the QA metric, as can be seen by the fact that after removing both the QA metric and seasonal variation from the model, the R2 value declines further to 0.185. This result indicates that both the QA metric and seasonal variations may be important variables to account for when seeking to explain variance in MRI data, given that using the MRI scanner’s phantom QA metric was not sufficient to account for all seasonal variance (at least when gray brain matter was the dependent variable). This result weakly supports the idea that seasonal variation in human data is caused by biological, rather than scanner, effects due to the fact that seasonal variation was not significant for phantom data (i.e. scanner-only) but was significant for gray brain matter in human data.
To supplement our findings on seasonal variation effects, it should be noted that our seasonal model was very simplistic and was composed of sine and cosine waves. Given that we noticed that it affected our models to some extent, we anticipate developing a more sophisticated model of seasonal effects for a more accurate model.
An unsurprising observation we made is that positioning of the patient (or phantom) in the scanner accounted for some variance. In the case of the phantom, its significance did not pass the significance threshold after FDR correction, but was very significant for human participants. Meanwhile, patient weight was a very statistically significant predictor for gray matter volume (Table 2); conversely, the size of the phantom remained constant while SNR was affected by position in the scanner. However, prior studies have indicated that brain size strongly correlates with patient height and thus weight16. This result suggests that it is still beneficial to add patient position in the scanner into the models to account for position-specific variance in addition to patient size (weight).
One interesting negative side-effect of establishing a fully reproducible pipeline, as we have done, is that we cannot share even highly compressed derivatives of the human data, such as morphometric estimates, with the subjects’ participation dates. This information could potentially be used to cross-reference with datasets where such anonymized MRI data is fully shared, albeit with their dates stripped, and thereby used to violate the confidentiality of these subjects’ data. Unfortunately, to our knowledge, no large public datasets are accompanied with phantom QA data scans from the participating sites, which made it impossible to reuse publicly available datasets.
Our use of ReproIn for “turnkey” collection of MRI data into BIDS datasets at DBIC was a highly beneficial methodology shown in our approach. An example of such a dataset is the phantom QA dataset we used in our study. The standardized structure of our dataset collection, from filenames to data format, facilitated our establishment of “meta-datasets” comprising data from multiple studies.
Future directions
Our investigation has used phantom and human data for the period from October 30, 2017 to August 28, 2018. We are going to compare the model’s predictions on additional data (from other studies and later dates) with actual data to check the generalizability of our established models on future data and feed new data into the model to make it more robust.
As mentioned in the Methods/Phantom section, we will consider using DBIC QA estimates such as T2* weighted EPI scans instead of MRIQC estimates to evaluate the significance of other sources of QA metrics in reducing variance.
In this study we used only anatomical (T1 weighted) data. We will investigate temporal SNR, which is a QA metric only available for functional data. Functional QA metrics are an interesting area of investigation in the future, as there is some notion of functional connectivity in resting state data, and statistical estimates from GLM on task data. Functional phantom QA and other scanner characteristics could provide explanatory power to analyses.
The software we used to derive morphometric estimates of the brain could have been affected by the software used, and an investigation into the effects of conversion software (e.g., FSL) and their versions on morphometric estimates could yield valuable insights.
Conclusions
We showed that the scanning parameters and QA metric of phantom data are useful for more than just QA. To maximize the statistical power of MRI studies, we propose using scanning parameters and a QA metric, total SNR, from an MRI scanner’s phantom data to reduce the unexplained variance that exists in MRI data.
Furthermore, we have found that our simple representation of seasonal variation can help explain gray brain matter volume in human MRI data, and deserves further investigation to determine if this effect is truly of a biological origin, as our results weakly suggest. The incorporation of seasonal variables can also help reduce variance in MRI data.
Data availability
The human participants’ data used in this study cannot be shared, either in its anonymized form or in the form of derivative estimates of the brain structure volumes, due to the nature of the study which relies on the dates of scanning. As outlined in HIPAA, “The following identifiers of the individual or of relatives, employers, or household members of the individual must be removed to achieve the “safe harbor” method of de-identification: […](C) All elements of dates (except year) for dates directly related to the individual, including […] admission date, discharge date.” To apply for access to the human participant data, you may contact either of the authors (cheng1928c@gmail.com and yoh@dartmouth.edu) and request a Data User Agreement (DUA) to fill out; upon approval, you will be granted access subject to the conditions of the DUA.
Harvard Database: Phantom MRI (Quality Assurance) Data From October 2017 to August 2018 at DBIC. https://doi.org/10.7910/DVN/PFH4FL22.
This project contains the following underlying data:
- JSON files contain quality assurance metrics to be used as independent variables (AcquisitionTime, SAR, TxRefAmp, & ImageOrientationPatientDICOM) and as the dependent variable (snr_total) for logistic regression analysis
- NII.GZ files that contain a compressed version of an MRI scan in the NIfTI-1 Data Format, and is the basis from which the quality assurance metrics of the JSON file were extracted. These nii.gz files were obtained from the original DICOM files using the HeuDiConv conversion program, which you may use to validate the JSON file’s metrics.
Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).
Code availability
Code available from: https://github.com/proj-nuisance/nuisance
Archived code at time of publication: https://doi.org/10.5281/zenodo.386544121.
License: Apache License 2.0
The aforementioned git repositories are also DataLad datasets that provide the complete computing environments used in the study (via git-annex), and contain full history of the analyses recorded in git commits history.
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CITE
how to cite this article
Cheng CP and Halchenko YO. A new virtue of phantom MRI data: explaining variance in human participant data [version 1; peer review: 1 approved, 2 approved with reservations, 1 not approved]. F1000Research 2020, 9:1131 (https://doi.org/10.12688/f1000research.24544.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
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ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations
A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
Version 1
VERSION 1
PUBLISHED 14 Sep 2020
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8
How to cite this report:
Sommer J. Reviewer Report For: A new virtue of phantom MRI data: explaining variance in human participant data [version 1; peer review: 1 approved, 2 approved with reservations, 1 not approved]. F1000Research 2020, 9:1131 (https://doi.org/10.5256/f1000research.27077.r75970)
The direct URL for this report is:
https://f1000research.com/articles/9-1131/v1#referee-response-75970
https://f1000research.com/articles/9-1131/v1#referee-response-75970
NOTE: it is important to ensure the information in square brackets after the title is included in this citation.
Reviewer Report 04 Jan 2021
Jens Sommer,
Center for Mind, Brain and Behavior, Marburg and Justus-Liebig University Giessen, Philipps-University Marburg, Marburg, Germany; Core Facility Brainimaging, Philipps-University Marburg, Marburg, Germany; Psychiatry Department, Philipps-University Marburg, Marburg, Germany
Approved with Reservations
VIEWS 8
The article describes in a very reasonable way how to estimate previously unexplained variance of MRI studies using data from the regular QA procedure of the radiology department. As the QA procedure is only run once a week the authors
... Continue reading
CITE
HOW TO CITE THIS REPORT
Sommer J. Reviewer Report For: A new virtue of phantom MRI data: explaining variance in human participant data [version 1; peer review: 1 approved, 2 approved with reservations, 1 not approved]. F1000Research 2020, 9:1131 (https://doi.org/10.5256/f1000research.27077.r75970)
The direct URL for this report is:
https://f1000research.com/articles/9-1131/v1#referee-response-75970
https://f1000research.com/articles/9-1131/v1#referee-response-75970
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
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15
How to cite this report:
Duchesne S. Reviewer Report For: A new virtue of phantom MRI data: explaining variance in human participant data [version 1; peer review: 1 approved, 2 approved with reservations, 1 not approved]. F1000Research 2020, 9:1131 (https://doi.org/10.5256/f1000research.27077.r75971)
The direct URL for this report is:
https://f1000research.com/articles/9-1131/v1#referee-response-75971
https://f1000research.com/articles/9-1131/v1#referee-response-75971
NOTE: it is important to ensure the information in square brackets after the title is included in this citation.
Reviewer Report 21 Dec 2020
Not Approved
VIEWS 15
This is a great study addressing the subject of image quality and its influence on brain morphometric measurements. While most investigators assume that these measurements are indeed near infaillible, work of this kind serves to improve our collective ability at
... Continue reading
CITE
HOW TO CITE THIS REPORT
Duchesne S. Reviewer Report For: A new virtue of phantom MRI data: explaining variance in human participant data [version 1; peer review: 1 approved, 2 approved with reservations, 1 not approved]. F1000Research 2020, 9:1131 (https://doi.org/10.5256/f1000research.27077.r75971)
The direct URL for this report is:
https://f1000research.com/articles/9-1131/v1#referee-response-75971
https://f1000research.com/articles/9-1131/v1#referee-response-75971
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
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12
How to cite this report:
Frederick B. Reviewer Report For: A new virtue of phantom MRI data: explaining variance in human participant data [version 1; peer review: 1 approved, 2 approved with reservations, 1 not approved]. F1000Research 2020, 9:1131 (https://doi.org/10.5256/f1000research.27077.r71693)
The direct URL for this report is:
https://f1000research.com/articles/9-1131/v1#referee-response-71693
https://f1000research.com/articles/9-1131/v1#referee-response-71693
NOTE: it is important to ensure the information in square brackets after the title is included in this citation.
Reviewer Report 22 Oct 2020
Approved
VIEWS 12
This article explores the relationship between various factors (SNR on a QA phantom, patient positioning, time of year, etc) and the results of quantitation of anatomic MRI images.
This is a solid piece of work, well executed ... Continue reading
This is a solid piece of work, well executed ... Continue reading
CITE
HOW TO CITE THIS REPORT
Frederick B. Reviewer Report For: A new virtue of phantom MRI data: explaining variance in human participant data [version 1; peer review: 1 approved, 2 approved with reservations, 1 not approved]. F1000Research 2020, 9:1131 (https://doi.org/10.5256/f1000research.27077.r71693)
The direct URL for this report is:
https://f1000research.com/articles/9-1131/v1#referee-response-71693
https://f1000research.com/articles/9-1131/v1#referee-response-71693
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
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14
How to cite this report:
Li X. Reviewer Report For: A new virtue of phantom MRI data: explaining variance in human participant data [version 1; peer review: 1 approved, 2 approved with reservations, 1 not approved]. F1000Research 2020, 9:1131 (https://doi.org/10.5256/f1000research.27077.r71690)
The direct URL for this report is:
https://f1000research.com/articles/9-1131/v1#referee-response-71690
https://f1000research.com/articles/9-1131/v1#referee-response-71690
NOTE: it is important to ensure the information in square brackets after the title is included in this citation.
Reviewer Report 06 Oct 2020
Xiangrui Li,
Department of Psychology, The Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA
Approved with Reservations
VIEWS 14
This is an interesting topic and has great practical application for the MRI studies.
My major concern is the conclusion about effect of variables, such as day time of acquisition, subject position, and SAR, on the brain tissue volume. ... Continue reading
My major concern is the conclusion about effect of variables, such as day time of acquisition, subject position, and SAR, on the brain tissue volume. ... Continue reading
CITE
HOW TO CITE THIS REPORT
Li X. Reviewer Report For: A new virtue of phantom MRI data: explaining variance in human participant data [version 1; peer review: 1 approved, 2 approved with reservations, 1 not approved]. F1000Research 2020, 9:1131 (https://doi.org/10.5256/f1000research.27077.r71690)
The direct URL for this report is:
https://f1000research.com/articles/9-1131/v1#referee-response-71690
https://f1000research.com/articles/9-1131/v1#referee-response-71690
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
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