Magnetic resonance imaging (MRI) has detected changes in pancreas volume and other characteristics in type 1 and type 2 diabetes. However, differences in MRI technology and approaches across locations currently limit the incorporation of pancreas imaging into multisite trials. The purpose of this study was to develop a standardized MRI protocol for pancreas imaging and to define the reproducibility of these measurements. Calibrated phantoms with known MRI properties were imaged at five sites with differing MRI hardware and software to develop a harmonized MRI imaging protocol. Subsequently, five healthy volunteers underwent MRI at four sites using the harmonized protocol to assess pancreas size, shape, apparent diffusion coefficient (ADC), longitudinal relaxation time (T1), magnetization transfer ratio (MTR), and pancreas and hepatic fat fraction. Following harmonization, pancreas size, surface area to volume ratio, diffusion, and longitudinal relaxation time were reproducible, with coefficients of variation less than 10%. In contrast, non-standardized image processing led to greater variation in MRI measurements. By using a standardized MRI image acquisition and processing protocol, quantitative MRI of the pancreas performed at multiple locations can be incorporated into clinical trials comparing pancreas imaging measures and metabolic state in individuals with type 1 or type 2 diabetes.
MRI plays an important role in the diagnosis and monitoring of a number of pancreatic disorders including chronic [[
Most clinical MRI data of the pancreas is analyzed by visual inspection to identify features, such as mass lesions or edema, that are indicative of disease presence and severity. Radiomics offers a complementary approach, in which numerical features, some of which are directly related to physical or physiological properties of the imaged tissue, are extracted from the MRI data and evaluated mathematically. The advantage of this quantitative approach is that it can be sensitive to nuanced differences in images that are hard or impossible to detect visually. The clinical utility of quantitative imaging measures has been demonstrated across a number of diseases and organs of interest [[
While a variety of approaches have shown promise for pancreas imaging at single research sites, they have not been validated in multisite studies. Imaging of the brain [[
The Multicenter Assessment of the Pancreas in Type 1 Diabetes (MAP-T1D) study is an international consortium of diabetes and medical imaging centers using MRI to investigate the pancreas in individuals with type 1 diabetes. The MAP-T1D team consists of five academic centers with different MRI hardware and software; including the University of Texas at Austin, subsequently referred to as Austin; Vanderbilt University Medical Center, subsequently referred to as Nashville; Barbara Davis Center for Diabetes and University of Colorado School of Medicine, subsequently referred to as Denver; University of Chicago, subsequently referred to as Chicago; St Vincent's Institute and Hospital and the University of Melbourne, subsequently referred to as Melbourne. Two centers (Austin and Denver) were equipped with 3T Skyra scanners (Siemens, Erlangen, Germany). Scans performed in Melbourne were acquired using a 3T Prisma scanner (Siemens, Erlangen, Germany). Two centers (Nashville and Chicago) acquired images on 3T Ingenia scanners (Philips, Best, Netherlands). All Siemens scanners employed VE11C software. Of the Philips scanners, Vanderbilt employed R5.5.0.1 while Chicago employed R.5.6.1. All sites employed torso coil arrays.
The MRI protocol consisted of a three-plane localizer followed by a series of axial scans spanning the pancreas. Anatomical scans included fat-suppressed 3D T1-weighted gradient echo and T2-weighted fast spin-echo images with and without fat saturation. Diffusion-weighted MRI (DWI) was acquired in a single direction with spin-echo EPI readout and b-values of 0, 50, 200, and 800. For T1 mapping, a B1 field map was acquired to correct for transmit inhomogeneity followed by five spoiled gradient echo images with equally spaced flip angles spanning 4° to 20°. A gradient echo image with and without a magnetization transfer sensitive prepulse was acquired for calculation of MTR. A 3D quantitative 6-point Dixon acquisition was collected at sites which had the requisite software on their scanner (Austin, Nashville, Chicago, Melbourne). Imaging parameters are summarized in Table 1. With the exception of the B1 map, slice thickness was 4 mm. Total time for this protocol was approximately 16 minutes of acquisition time, translating to approximately 35 minutes of total scan time.
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Table 1 MRI protocol acquisition parameters.
MRI Scan 3D T1-w image T2-w image (fat sat) T2-w image (no fat sat) Diffusion-weighted B1 Map Multi Flip Angle Spoiled GRE (T1 Mapping) Magnetization Transfer 3D Quantitative DIXON Orientation Axial Axial Axial Axial Axial Axial Axial Axial Acquisition Matrix 256 x 208 256 x 208 256 x 208 128 x 104 128 x 104 128 x 104 128 x 104 160 x 132 Field of View [mm] 384 x 312 384 x 312 384 x 312 384 x 312 384 x 312 384 x 312 384 x 312 450 x 372 In Plane Resolution [mm] 1.5 x 1.5 1.5 x 1.5 1.5 x 1.5 3 x 3 3 x 3 3 x 3 3 x 3 1.4 x 1.4 Number of Slices 48 48 48 40 24 48 48 64 Slice Thickness/Slice Gap [mm] 4/0 4/0 4/0 4/0.8 8/0 4/0 4/0 4/0 TR [ms] 4.04 750 386 7600 14310 4.6 2000 9 TE [ms] 1.29; 2.52 105 105 48 2.06 1.96 3.58 1.05; 2.46; 3.69; 4.92; 6.15; 7.38 Flip Angle [degrees] 10 100 100 90 8 20; 16; 12; 8; 4 25 4 Fat Suppression 2 point Dixon SPAIR None SPAIR None None None 6 point Dixon Miscellaneous b-values: 0, 50, 200, 800 5 flip angles 'MTC' off & 'MTC' on Motion Compensation 1 breath-hold 2 breath-holds 1 breath-hold Respiratory Gated 2 breath-holds 1 breath-hold None 1 breath-hold Acquisition Time [m:s] 0:12 0:39 0:19 4:08 0:29 0:10 each; 5 acquisitions 2:12 each; 2 acquisitions 0:13
Synthetic imaging phantoms for standardizing MRI parameters were constructed at a single center (Austin) and shipped to each site for imaging (Fig 1A). These MRI phantoms were used to standardize measurements of volume, apparent diffusion coefficient (ADC), T1, MTR, and fat fraction. For volume standardization, a pancreas phantom was generated from an abdominal MRI of a 39-year-old male with no known pancreas pathology. A 3D volume of the pancreas was extracted from the abdominal MRI by freehand tracing the organ's borders using Osirix software (Pixmeo, Bernex, Switzerland). This volume was 3D printed using PLA thermoplastic and embedded in agar in a 1L Nalgene jar. For diffusion imaging, the phantom consisted of a 50 mL tube filled with deionized water chilled to 0°C and immobilized in a 1L Nalgene jar filled with ice water, as previously described [[
MAP: Fig 1 Schematic of MRI data acquisition.A) Example MRI of synthetic phantoms with calibrated properties that were shipped to five different sites for imaging. The phantom (upper left) consisted of three components. The leftmost bottle contained phantoms with canola oil, bovine serum albumin, and gadolinium-doped gelatin to validate fat fraction, MTR, and T1 measurements, respectively. Example fat fraction, MTR, and T1 maps are shown on the bottom row. The middle bottle contained a 3D printed pancreas created from an MRI of a normal volunteer pancreas and subsequently embedded in agar for imaging (middle row). The rightmost bottle contained deionized water chilled to 0°C to validate diffusion-weighted MRI measurements. B) Five healthy volunteers traveled to four sites in the US (Austin, Chicago, Denver, and Nashville) for an MRI of the pancreas using the harmonized acquisition protocol. C) MAP-T1D study logo.
Five healthy volunteers with no known pancreas pathology or diabetes traveled to four MAP-T1D locations in the US (Austin, Chicago, Denver, Nashville) to undergo MRI using the standardized, harmonized imaging protocol (Fig 1B). The volunteers were not imaged at Melbourne because of travel distance. Characteristics of the volunteers were: four males and one female with an average age of 37 years old (min 31, max 44) with an average BMI of 26.4 ± 2.9 kg/m
Images of the phantoms and human volunteers were analyzed using the same methodology. The pancreas was outlined on each slice of the fat-suppressed T2-weighted image by an experienced radiologist (M.A.H.). Repeat scans of the same individual were blinded and presented to the radiologist non-consecutively to minimize bias. Pancreas outlining was performed using the Medical Image Processing, Analysis, and Visualization (MIPAV) application (https://mipav.cit.nih.gov/). The T1-weighted image was consulted to guide delineation of the pancreas border. Pancreas volume was calculated by multiplying the sum of the pancreas area on each slice by the distance between slices [[
ADC maps were calculated from the diffusion-weighted images acquired with b values of 200 and 800 s/mm
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Graph: Fig 2 Flow chart of MRI data acquisition and processing.
The reproducibility of each radiomic measure was quantified for phantoms across five MRI scanners and for healthy volunteers across four MRI scanners. For phantom studies, the accuracy of volume and T1 measures were calculated as percent difference between the reference and measured value. For measures without reference values, the mean coefficient of variation across MRI scanners was calculated. Friedman's test was used to assess the difference in each measure across MRI scanners, with post-hoc Wilcoxon rank sum testing to assess differences between each pair of scanners. The difference between scans performed on different individuals on the same scanner (inter-individual variation) was compared with the difference between scans on the same individual using different scanners (inter-assay variation). Power analysis was performed by calculating mean and standard deviation of both inter-individual and inter-assay variation for each MRI measure and calculating the sample size required to achieve a prescribed statistical power. To put our findings in perspective, we also performed power analysis on similar measurements performed on control individuals at a single site (n = 79), an extension of work previously reported [[
To standardize and harmonize imaging across the five centers with different MRI technology and software, synthetic imaging phantoms with known MRI properties were created and shipped to each site for imaging (Fig 1A). Pancreas phantom volume measurements at each of the five sites were similar to the true volume, with a maximum and average difference of 2% from the true value (Table 2). The average difference between T1 measurements at each site and the true values was 6%, with a maximum difference of 13% (Table 2). Of note, one site (Melbourne) had the lowest T1 measurement for each phantom, suggesting there may be bias in T1 mapping on this scanner. The ADC, MTR, and fat fraction measurements were compared across sites, resulting in coefficients of variation of 4.3%, 8.9%, and 0.4%, respectively, across the five sites. We did not detect any temporal changes in these phantoms over the course of these studies. These reproducibility measurements indicate that the acquisition and processing protocol was harmonized across the five sites.
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Table 2 MRI reproducibility results.
Phantom MRI Measure MRI Scanner Location Nashville Austin Denver Chicago Melbourne Ref. Value: 89.0 Volume [ml] 90.8 87.4 91 91.2 89.5 MTR 0.383 0.378 0.384 0.317 0.326 ADC [mm2/s] 0.0014 0.0014 0.0013 0.0013 0.0015 Ref. Value: 500 T1 [ms] (vial 1) 468 500 494 454 447 Ref. Value: 1000 T1 [ms] (vial 2) 948 1038 1020 1141 897 Ref. Value: 1250 T1 [ms] (vial 3) 1284 1250 1256 1375 1092 Ref. Value: 1500 T1 [ms] (vial 4) 1570 1550 1600 1625 1354 Fat Fraction 96.7 95.9 N/A 96.2 95.6 Volunteer 1 Pancreas Volume [ml] 81.297 59.616 60.768 67.914 PVI [ml/kg] 1.0862 0.79655 0.77619 0.90742 MTR 0.37469 0.44421 0.44516 0.24861 ADC [mm2/s] 0.0011888 0.0010299 0.0011715 0.0012272 T1 [ms] 958.61 890.77 866.19 1043.3 Pancreatic Fat Fraction 0.1472 0.13584 N/A 0.18757 Hepatic Fat Fraction 0.0422 0.0296 N/A 0.0312 Surface area/volume [cm-1] 0.1017 0.10107 0.10482 0.095063 Volunteer 2 Pancreas Volume [ml] 73.602 74.214 87.93 73.134 PVI [ml/kg] 1.081765 1.09808 1.294076 1.07488 MTR 0.41664 0.30807 0.31042 0.055648 ADC [mm2/s] 0.0012611 0.0012671 0.0014773 0.0014198 T1 [ms] 1108.1 1236.4 1311.6 1055.5 Pancreatic Fat Fraction 0.020894 N/A N/A 0.060248 Hepatic Fat Fraction 0.0465 N/A N/A 0.024 Surface area/volume [cm-1] 0.1118 0.10107 0.10595 0.11043 Volunteer 3 Pancreas Volume [ml] 112.75 122.29 127.75 112.9 PVI [ml/kg] 1.1893 1.29617 1.30389 1.1852 MTR 0.40765 0.28954 0.34684 N/A ADC [mm2/s] 0.0012716 0.001267 0.0011184 0.0010943 T1 [ms] 1168.1 874.06 818.81 989.93 Pancreatic Fat Fraction 0.13 0.13369 N/A 0.12877 Hepatic Fat Fraction 0.0394 0.0293 N/A 0.0185 Surface area/volume [cm-1] 0.087008 0.086092 0.075841 0.095138 Volunteer 4 Pancreas Volume [ml] 57.177 60.381 63.351 70.839 PVI [ml/kg] 0.7781 0.80677 0.84645 0.97 MTR 0.249 0.32639 0.3998 0.085408 ADC [mm2/s] 0.0013227 0.0014217 0.0011917 0.0011398 T1 [ms] 901.82 897.35 868.17 918.05 Pancreatic Fat Fraction 0.077119 0.078579 N/A 0.13843 Hepatic Fat Fraction 0.0298 0.0163 N/A 0.0269 Surface area/volume [cm-1] 0.11663 0.11427 0.11303 0.11338 Volunteer 5 Pancreas Volume [ml] 96.57 116.41 103.18 100.39 PVI [ml/kg] 0.9721 1.19367 1.04345 1.0294 MTR 0.39885 0.40804 0.39628 0.089641 ADC [mm2/s] 0.0010012 0.00094109 0.0008776 0.0010638 T1 [ms] 676.91 839.37 830.25 707.06 Pancreatic Fat Fraction 0.23798 0.2386 N/A 0.20345 Hepatic Fat Fraction 0.1381 0.1111 N/A 0.1686 Surface area/volume [cm-1] 0.075479 0.083718 0.070601 0.081018
To quantify the reproducibility of quantitative MRI techniques in the pancreas, we sent five volunteers to each of the four MAP-T1D sites in the US (Fig 1B). Multiparametric MRI measurements of the pancreas from these five volunteers are shown in Table 2 and graphically in Fig 3. The average coefficient of variation across five individuals in pancreas volume and PVI were similar at 9.5% and 9.8%, respectively. The ratio of pancreas surface area to volume displayed the lowest coefficient of variation at 5.3%. For the voxel-wise MRI measures, the coefficient of variation was 8.4% for ADC measurements, 9.5% for T1, 39.3% for MTR, 26.5% for pancreatic fat fraction, and 30.2% for hepatic fat fraction. We did not detect a statistically significant difference in any measure across the MRI scanners or between any pair of scanners. For each MRI measure, the difference in measurements between individuals (inter-individual variation) was greater than the difference in measurements made on the same individual on different scanners (inter-assay variation). Dot plots in Fig 3 indicate the distribution of PVI, ADC, and surface area to volume ratio calculated for 79 control subjects at a single site, an extension of our previously published study [[
Graph: Fig 3 Quantitative MRI measures for 5 individuals scanned on four different MRI centers.Values for each MRI measurement of the pancreas are displayed for: A) pancreas volume index (PVI), B) surface area to volume ratio, C) longitudinal relaxation time (T1), D) apparent diffusion coefficient (ADC), E) pancreatic fat fraction, and F) hepatic fat fraction. Note that fat fraction was not measured in Denver due to a lack of the requisite software. For the graphs of PVI, surface area to volume ratio, and ADC, the distribution of values calculated in healthy volunteers at a single site study (updated from a previously published study [[
Power analysis was performed to estimate the minimum number of subjects required to power clinical trials using MRI measures. Table 3 displays the number of subjects required to detect both differences between two independent samples (e.g., controls versus individuals with diabetes) as well as longitudinal differences in the same individual. Sample sizes are provided to detect 5%, 10%, or 20% changes in each MRI measure at 80% and 90% power. As there was greater inter-individual variation than inter-assay variation for each measure, detecting changes in a single individual, as in a longitudinal study, requires smaller sample sizes. The size measures (volume and PVI) require similar sample sizes to detect changes in an individual, but PVI has added power to detect differences between groups as it accounts for the correlation between body size and pancreas size. The surface area to volume ratio had the highest power for discriminating two groups or detecting changes in an individual. Of the voxel-based measures, ADC and T1 measurements have similar statistical power to pancreas size measures. Pancreatic and hepatic fat fraction have moderate ability to detect changes in an individual, but large inter-individual differences in fat content across individuals limits the ability to detect differences in fat fraction between groups. MTR requires large sample sizes to detect changes in the pancreas. To provide context to these multisite results, we performed derived power measures/indices using a previously acquired dataset of control individuals (n = 79) who underwent longitudinal pancreas MRI on a single MRI scanner [[
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Table 3 Projected number of study participants required for future clinical trial (multisite).
Two Independent Groups MRI Measure 80% Power 90% Power 5% Difference 10% Difference 20% Difference 5% Difference 10% Difference 20% Difference Volume [ml] 930 234 60 1244 312 80 PVI [ml/kg] 362 92 24 484 122 32 ADC [mm2/s] 504 128 34 674 170 44 T1 [ms] 380 96 26 508 128 34 MTR 2244 562 142 3004 752 190 Pancreatic Fat Fraction 1630 408 104 2180 546 138 Hepatic Fat Fraction 9434 2360 850 12,630 3158 1138 Surface area/volume [cm-1] 284 72 20 380 96 26 Within Subject Variation MRI Measure 80% Power 90% Power 5% Difference 10% Difference 20% Difference 5% Difference 10% Difference 20% Difference Volume [ml] 28 9 4 36 11 4 PVI [ml/kg] 30 9 4 39 11 5 ADC [mm2/s] 33 10 4 44 13 5 T1 [ms] 31 9 4 41 12 5 MTR 589 149 39 787 198 51 Pancreatic Fat Fraction 64 17 6 84 23 7 Hepatic Fat Fraction 149 39 15 199 51 20 Surface area/volume [cm-1] 10 4 3 13 5 3
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Table 4 Projected number of study participants required for future clinical trial (single site).
Two Independent Groups MRI Measure 80% Power 90% Power 5% Difference 10% Difference 20% Difference 5% Difference 10% Difference 20% Difference Volume [ml] 2142 538 136 2868 718 182 PVI [ml/kg] 768 198 50 1026 264 68 ADC [mm2/s] 258 128 16 346 170 20 MTR 722 182 46 964 242 60 Within Subject Variation MRI Measure 80% Power 90% Power 5% Difference 10% Difference 20% Difference 5% Difference 10% Difference 20% Difference Volume [ml] 40 12 5 53 15 5 PVI [ml/kg] 36 11 4 48 14 5 ADC [mm2/s] 18 10 3 23 13 3 MTR 128 16 6 170 21 7
To examine the impact of different image acquisition and processing, we performed image processing using both a standardized and non-standardized protocol. To generate the non-standardized image T1 map, B1 field correction was not performed. To simulate non-standardized diffusion processing, we changed the b-values employed to generate ADC maps from 200 and 800 in the standardized protocol to 0 and 800 in the non-standardized protocol. The results of this analysis for subjects imaged at two different sites (Austin and Chicago) are shown in Fig 4. Representative non-standardized T1 and ADC maps are shown in the top row of Fig 4A. In contrast, images acquired and processed using the standardized MAP-T1D protocol at two different sites (Fig 4A, middle and bottom row) display similar values for both T1 and ADC. For each individual, the mean pancreas T1 value calculated using the standardized protocol at two sites was more reproducible than when a non-standardized protocol was employed at one site (Fig 4B). Similarly, the mean pancreatic ADC value calculated using the standardized protocol at two sites displayed better agreement than when a non-standardized protocol was employed (Fig 4C). We calculated the variation induced by using a non-standardized MRI protocol to estimate sample sizes required if MRI protocols are not standardized. A non-standardized MRI protocol with 80% power would require a sample size 22-fold greater to detect a 10% change in ADC and 10-fold greater to detect a 10% change in T1.
MAP: Fig 4 Representative difference in quantitative MRI measurements induced by use of different image processing between sites.A) Representative maps of T1 relaxation time (left column) and ADC (right column) displayed in pseudo color over a T1-weighted image. The top row displays images acquired in Chicago and processed using a non-standardized image processing protocol, demonstrating differences in T1 and ADC values from the standardized protocol (middle row). Images acquired and processed using the standardized MAP-T1D protocol in Chicago on a Philips MRI scanner (middle row) and Austin on a Siemens MRI scanner (bottom row) display concordance for T1 and ADC. All sets of parametric maps are scaled identically for visualization. B) Mean pancreatic T1 values are more reproducible between two sites (Chicago and Austin) using the standardized image analysis protocol (red circles), than when using non-standardized image processing (blue squares). The line of identity indicates perfect agreement. C) Mean pancreatic ADC values are more reproducible between two sites (Chicago and Austin) when using the standardized image analysis protocol (red circles), than when using non-standardized image processing (blue squares).
This study is the first effort to generate a standardized pancreas MRI protocol for techniques that are being used for studying diabetes. When deployed to five different MRI centers, this standardized protocol demonstrated the reproducibility of quantitative imaging measures using both calibrated phantoms and healthy volunteers. Phantom measurements at five different sites demonstrated excellent accuracy to known standards and excellent reproducibility across sites. In pancreas scans in humans, MRI measurements of pancreas volume, ADC, T1, fat fraction, and surface area/volume ratio using a standardized protocol were reproducible across different scanner hardware and software. The harmonized image acquisition and processing tools developed in this study have been made available to anyone interested and can be deployed in multisite clinical trials incorporating pancreas imaging.
Previous MRI studies of the pancreas of individuals with diabetes have led to conflicting results, which likely stem, in part, from disparities in image acquisition and processing. For example, a meta-analysis of pancreatic volume and fat content found high heterogeneity between studies [[
We found high accuracy in volume and T1 measurements using phantoms standardized to known values. Of note, an ice water phantom similar to the one used in this study has previously calculated an ADC of 0.0011 mm
For in vivo reproducibility studies, MRI measures displayed marked differences among individuals with no known pancreas pathology. For example, one individual (denoted as Volunteer 5 in Table 2) had increased fat content in both the liver and pancreas. An association between fat accumulation the liver and pancreas has been previously reported [[
This study is subject to a number of limitations. MRI scanners from both Philips and Siemens were represented in the study sites, but GE scanners were not part of the current study. Additionally, all scans were performed at 3T. Several quantitative MRI parameters can be influenced by field strength, and thus translation of this protocol to 1.5T field strength will likely require adjustment of imaging parameters. Finally, a larger volunteer cohort would allow for a more precise evaluation of variability. Sending the same individual to each study site for scanning is both expensive and technically difficult, and thus, it is not commonly performed. Given the complexity of navigating volunteer travel and scheduling, we were limited to five individuals scanned at four different sites but showed good agreement in MRI measurements across a wide variety of MRI hardware and software. An additional limitation of this study is the use of a single reader to outline all pancreas images in this study. The use of multiple readers may lead to variation in MRI measurements of the pancreas [[
This study demonstrates that, when carefully controlled and standardized, quantitative MRI of the pancreas is highly reproducible across different MRI hardware and software at different geographic locations. Pancreas MRI can now be incorporated into multisite clinical trials of diabetes and other pancreatic diseases. In order to standardize acquisition and processing of MRI studies of the pancreas, we have made our image acquisition protocols and image processing code freely available for any users using Github (https://github.com/jvirostko/MAPT1D). It is our hope that this protocol will be adapted and modified by other groups in the diabetes and imaging community performing MRI of the pancreas. Efforts in employing a common standardized protocol will improve data quality and reporting and facilitate comparison of results across sites and, ultimately, multisite clinical trials. Furthermore, use of a common image acquisition standard will empower future applications of machine learning to studies of pancreas MRI.
The University of Chicago would like to thank the Kovler Diabetes Center research team including Mariko Pusinelli, Rabia Ali, and Cristy Miles, for their invaluable help with all aspects of study coordination. We sincerely thank all research participants who took part in this in this study.
By John Virostko; Richard C. Craddock; Jonathan M. Williams; Taylor M. Triolo; Melissa A. Hilmes; Hakmook Kang; Liping Du; Jordan J. Wright; Mara Kinney; Jeffrey H. Maki; Milica Medved; Michaela Waibel; Thomas W. H. Kay; Helen E. Thomas; Siri Atma W. Greeley; Andrea K. Steck; Daniel J. Moore and Alvin C. Powers
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