Diffusion-weighted MRI is a functional imaging method that allows a qualitative and quantitative assessment of tissue diffusivity. Historically, the use of DW-MRI has been described for brain investigations,1–4 but the examination of abdominal organs is an emerging application of DW-MRI in veterinary medicine. In human medicine, DW-MRI is routinely used to examine the liver and enables the detection of focal lesions and diffuse hepatic diseases.5 The ADC is derived from DW-MRI images and represents the quantification of the random motion of water molecules in biological tissues. It has been shown to be an adequate method for predicting liver fibrosis and inflammation and is used to help differentiate between benign and malignant focal lesions.6 However, the ADC of the liver is dependent on patient-specific characteristics, including the HFF. An association between lower ADC and higher HFF has been found in some studies5,7,8 in human medicine as well as in an investigation with mice used to study nonalcoholic steatohepatitis.9 Another study of human patients10 did not find an association between HFF and the ADC.
Hepatic fat accumulation with consequent increased HFF develops in domestic cats secondary to obesity11 and pathological conditions such as diabetes mellitus12 and hepatic lipidosis.13 Overweight status and obesity problems are becoming more common in cats,14 and hepatic lipidosis is the most common hepatobiliary disorder in adult cats.13,15 Obesity and hepatic lipidosis are also associated with each other: obese cats have a significantly higher liver fat concentration than lean cats,16 and those that are overweight and obese are at risk for the development of hepatic lipidosis. Increased HFF is clinically difficult to quantify and its clinical importance may vary. Noninvasive techniques to evaluate hepatic fat content in clinical practice are usually limited to ultrasonography and CT, but both modalities lack specificity and allow only semiquantitative evaluation of fat content.17 Assessment of hepatic steatosis for clinical care requires grading of disease severity as well as its diagnosis. In human patients, hepatic steatosis is a pathogenic, potentially reversible condition and there is an urgent need in both clinical and research arenas to detect its presence and assess its severity.18 The same needs are present in veterinary medicine. A substantial number of studies, mostly in people, show that MRI allows a noninvasive, accurate, reproducible, precise, and reader-independent quantification of HFF regardless of the degree of hepatic lipidosis.17,19–26 A recently commercially released multiple-echo GRE pulse sequence enables accurate and consistent measurement of HFF.27
To the authors' knowledge, no study has investigated the association of the hepatic ADC and HFF in cats. The purpose of the study reported here was to determine the changes in mean ADC and HFF during body weight gain in adult cats by use of MRI; specifically, we aimed to investigate whether increasing HFF has an effect on ADC of the liver in these experiments. This effect could influence the interpretation of DW-MRI results for hepatic parenchyma in cats.
Materials and Methods
The prospective, experimental study was approved by the Cantonal Veterinary Office of Zurich (license number, ZH118–16) in accordance with the Animal Welfare Act of Switzerland and as a part of a larger concurrent study. Cats underwent MRI examinations at 2 time points: at the start of the study before dietary intervention (time 0) and 40 weeks after the start of dietary intervention (time 1).
Animals
Twelve purpose-bred adult neutered male short-hair cats were enrolled in the study. Median age of cats at the beginning of the study was 77 months (range, 75 to 78 months). At time 0, all cats underwent a clinical examination, body condition score assessment, and body weight measurement prior to MRI. On the basis of results of a physical examination, hematologic assessment, and serum biochemical tests, all cats were deemed to be in good health except that 2 cats had mildly high renal values (ie, urea concentration in one cat, urea and creatinine concentrations in the other; chronic kidney disease stage 2 according to International Renal Interest Society guidelines). Ten cats were assigned American Society of Anesthesiologists physical classification status I, and the 2 cats with mildly high renal values were assigned American Society of Anesthesiologists physical classification status II. All cats had a normal body condition score (5/9) at time 0. After the initial MRI examination, the cats were fed a commercial dry fooda ad libitum for 16 weeks or until they were deemed overweight (body condition score, 7/9). Subsequently, the amount of food provided was adjusted to maintain this body weight.
At time 1, the physical examination, body weight measurement, and hematologic and serum biochemical tests were repeated for all cats prior to MRI. The 10 cats with American Society of Anesthesiologists physical classification status I had this status confirmed. The 2 cats with American Society of Anesthesiologists physical classification status II were excluded from the remainder of the study for causes not related to the study.
Anesthesia
Food was withheld from cats for 12 hours before anesthesia. Premedication consisted of ketamine (10 mg/kg), midazolam (0.1 mg/kg), and butorphanol (0.3 mg/kg) administered IM. After premedication, a catheter was aseptically placed in the left or right cephalic vein for administration of contrast medium, IV medication, and lactated Ringer solution (3 mL/kg/h). Oxygen was administered via face mask for 30 minutes prior to induction of anesthesia with alfaxalone (0.5 to 2 mg/kg, IV). The cats were then intubated with a cuffed endotracheal tube and mechanically ventilated with positive end-expiratory pressure in a pressure-controlled mode (5 to 11 cm H2O). The respiratory rate was adjusted to achieve the desired end-tidal partial pressure of CO2 (35 to 42 mm Hg [4.66 to 5.59 kPa]). Anesthesia was maintained with isoflurane in oxygen and air (1:1 ratio). Anesthesia was monitored with a multiparameter monitor that included spirometry, capnography, and an MRI-compatible wireless respiratory sensor, as well as vectorcardiography and pulse oximetry. The anesthesia variables were recorded automatically. Glycopyrrolate (10 μg/kg, IV) was administered if the pulse rate declined to < 100 beats/min for > 10 minutes, and this treatment procedure was repeated once during the anesthetic episode if needed.
MRI
A 3-T MRI scannerb with a phased-array anterior coilc was used for MRI examinations. All images were acquired in the transverse plane. Cats were positioned in dorsal recumbency, and the scanning included morphological imaging to exclude liver abnormalities.
A T2-weighted sequence (turbo spin echo; repetition time and echo time, 2,000 and 80 milliseconds, respectively; flip angle, 90°; field of view adapted to the animal; voxel size, 1.18 × 1.42 × 3.00 mm; slice gap, 0 mm; and slice thickness, 3 mm) was performed first. This was followed by a T1-weighted precontrast sequence (modified Dixon, gradient echo; repetition time, 3.7 milliseconds; echo time 1 and echo time 2, 1.21 and 2.4 milliseconds, respectively; flip angle, 10°; field of view, adapted to the animal; voxel size, 1.5 × 1.5 × 3.0 mm; slice gap, −1.5 mm; and slice thickness, 3 mm).
For fat quantification, multiple-echo GRE (multi-echo acquisition, multipeak modified Dixon sequence with T2* correction)d was then performed. The sequence settings used were as follows: breath hold, expiration; repetition time, 7.5 milliseconds; echo time 1 and delta echo time, 1.23 and 1.0 milliseconds, respectively; flip angle, 3°; field of view adapted to the animal; slice gap, −2 mm; slice thickness, 4 mm; acquired voxel size, 1.5 × 1.49 × 4 mm; and number of echoes, 6. The breath-hold technique was used for a maximum of 21.3 seconds. Controlled mechanical ventilation was discontinued to induce brief expiratory apnea and was continued immediately after the sequence.
Diffusion-weighted MRI (3b imaging performance sensitivity encoding) was performed with the following settings: repetition time, 1.8 seconds; echo time, 75 milliseconds; flip angle, 90°; field of view adapted to the animal; slice gap, 0.4 mm; slice thickness, 4 mm; and acquired voxel size, 1.96 × 1.83 × 4 mm. The 3 b values were 0, 400, and 800 s/mm2.
Finally, a T1-weighted postcontrast sequence was performed after manual injection of contrast mediume (0.3 mL/kg, IV) followed by 10 mL of saline (0.9% NaCl) solution. The settings were as follows: modified Dixon method, gradient echo; repetition time, 3.7 milliseconds; echo time 1 and echo time 2, 1.21 and 2.4 milliseconds, respectively; flip angle, 10°; field of view adapted to the animal; voxel size, 1.5 × 1.5 × 3.00 mm; slice gap, −1.5 mm; and slice thickness, 3 mm.
Postprocessing and data analysis
Postprocessing of the multiple-echo GRE sequence was performed on the workstation of the previously described MRI unit, and DW-MRI postprocessing was performed on a dedicated extended workstation.f The HFF was evaluated qualitatively and quantitatively. Qualitative evaluation was performed by use of a color-coded map. Tissue with no fat content was displayed in dark violet, and a progressively increasing fat component was displayed in gradually brighter colors (from violet to blue, green, yellow, and orange; a fat component of approx 80% to 100% fat tissue was displayed in red). For the quantitative evaluation of HFF, 4 ROIs of similar size were manually drawn on the liver parenchyma by 1 investigator (FD) using an adjustable round cursor on the automatically generated fat fraction images. The ROIs were drawn in the right cranial, right caudal, middle, and left aspects of the liver parenchyma. On the basis of the DW-MRI, the software automatically generated an ADC map. Three ROIs of similar size were manually drawn on the liver parenchyma by the same investigator using an adjustable round cursor on the ADC map. The ROIs were drawn in the middle right, middle left, and cranial aspects of the liver parenchyma in areas with subjectively homogeneous signal intensity as described elsewhere.28 In all image processing, care was taken to avoid major blood vessels, the gallbladder, and obvious image artifacts when placing ROIs.
Statistical analysis
Statistical analyses were performed with a free software environment for statistical computingg and a related software package.h Normal distribution of the data was tested with the Shapiro-Wilk test. Descriptive statistics were calculated and numeric data were reported as the mean ± SD, median and range, or number and percentage where appropriate. For data collected with multiple ROIs, mean values were calculated for each cat. Significant differences in ADC, HFF, and body weight between time points were determined with the Wilcoxon test for paired samples. The relationship between ADC and HFF was analyzed with a generalized linear mixed-effects model. The relationship between the body weight and HFF was also analyzed with a generalized linear mixed-effects model. Values of P < 0.05 were considered significant.
Results
At time 0, 12 cats were evaluated, and at time 1, the 10 cats remaining in the study were evaluated. The results of physical examination, hematologic tests, and serum biochemical analysis for hepatic screening were unremarkable in all cats at both MRI time points. At time 0, median body weight was 4.52 kg (range, 3.6 to 5.3 kg), compared with 6.35 kg (range, 4.3 to 8.7 kg) at time 1; this difference was significant (P < 0.001).
The mean ± SD acquisition time was 99.76 ± 12.65 seconds for T2-weighted sequences, 10.85 ± 2.75 seconds for T1-weighted precontrast sequences, and 9.27 ± 2.37 seconds for T1-weighted postcontrast sequences. The mean acquisition time was 3 minutes 28 seconds ± 47 seconds for DW-MRI and was 14.3 ± 1.7 seconds for the multiple-echo GRE sequence.
Morphological images of the liver in all cats were considered unremarkable in T2-weighted and T1-weighted precontrast sequences and T1-weighted postcontrast sequences at both time points. All livers were homogeneously hyperintense to the epaxial musculature and hypointense to the spleen on T2-weighted images, homogeneously and mildly hyperintense to epaxial muscles and spleen on precontrast T1-weighted images, and homogenously hyperin-tense to epaxial muscles and spleen on postcontrast T1-weighted images.
Regarding the qualitative assessment of the livers with the color-coded map of the multiple-echo GRE sequence at time 0, the livers of 3 cats were almost completely dark violet (indicating the least amount of fat) with only traces of bright violet. The dark violet component was subjectively more extensive than the bright violet component in the other 9 cats. At time 1, the livers of 3 cats had dark and bright violet components with subjectively equal distribution. In the other 7 cats, the bright violet component was subjectively more extensive than the dark violet component. No cats had visible blue areas or brighter colors within the liver. An example of the qualitative changes in the liver of 1 cat from time 0 to time 1 is provided (Figure 1).
Color-coded maps derived from multiple-echo GRE sequences (transverse abdominal views) for 1 of 12 adult neutered male cats in a study to evaluate changes in mean hepatic ADC and HFF during body weight gain by use of MRI. Study cats had no evidence of hepatic abnormalities at the start of the study. A—The liver appears predominantly dark violet in the 77-month-old 4.1 −kg cat at time 0 (before dietary intervention at the start of the study). B—At time 1 (after 40 weeks of dietary intervention to achieve and maintain a body condition score of 7/9), the liver has an increased bright violet component, and increased subcutaneous and visceral fat tissue is evident in the 86-month-old 5.6-kg cat. Dorsal is at the top and the cat's right side is on the left in the images. The color scale bar on the right depicts an approximation of percentage fat, where the darkest violet represents no detectable fat and red represents approximately 80% to 100% fat. Bar = 1 cm.
Citation: American Journal of Veterinary Research 81, 10; 10.2460/ajvr.81.10.796
The mean ± SD HFF was 3.39 ± 0.89% at time 0, compared with 5.37 ± 1.92% at time 1 (P = 0.005). An example of the estimated HFF measurement for 1 ROI in 1 cat at both time points is shown (Figure 2). The mean ± SD hepatic ADC was 1.21 ± 0.08 × 10−3 mm2/s at time 0, compared with 1.01 ± 0.2 × 10−3 mm2/s at time 1 (P = 0.037). An image demonstrating placement of an ROI for ADC determination in 1 cat is provided (Figure 3). Changes over time for body weight, HFF, and ADC and the relationship between these 3 variables were summarized (Figure 4).
Representative multiple-echo GRE sequence images (transverse abdominal views) for 1 cat depicting ROI placement (white circle in the right cranial aspect of the hepatic parenchyma) for estimation of HFF at time 0 (A) and time 1 (B). A—Image obtained for the 75-month-old 4.6-kg cat at time 0. B—Image obtained for the 84-month-old 5.8-kg cat at time 1. Optically, no difference in signal intensity for HFF of the liver is visible. The estimated HFF in the ROI is 3.3% and 4.18% at time 0 and time 1, respectively, and increased subcutaneous and visceral fat tissue is visible at time 1. See Figure 1 for remainder of key.
Citation: American Journal of Veterinary Research 81, 10; 10.2460/ajvr.81.10.796
An ADC map (transverse abdominal view) of the liver of 1 cat depicting ROI placement in the right cranial aspect of the hepatic parenchyma at time 0 (A) and time 1 (B). A—Image obtained for the 75-month-old 4.7-kg cat at time 0 (HFF, 3.13%). B—Image obtained for the 84-month-old 6.5-kg cat at time 1 (HFF, 4.12%). Optically, no difference in signal intensity for ADC of the liver is visible. The ADC measured in the ROI is 1.19 × 10−3 mm2/s at time 0 and 0.9 × 10−3 mm2/s at time 1. Limited spatial resolution is apparent. GB = Gallbladder. See Figures 1 and 2 for remainder of key.
Citation: American Journal of Veterinary Research 81, 10; 10.2460/ajvr.81.10.796
Box-and-whisker plots of mean body weight (kg), HFF (%), and ADC (units × 10−3 mm2/s) for 12 adult neutered male cats at time 0 and for 10 cats that completed the study at time I. For each plot, the box represents the 25th to 75th percentiles, the solid horizontal line represents the median, and whiskers represent the range; dots represent the values for individual cats. BW = Body weight. T0 = Time 0. T1 = Time I. See Figure I for remainder of key.
Citation: American Journal of Veterinary Research 81, 10; 10.2460/ajvr.81.10.796
The mean ± SD ROI size for HFF measurements was 95.6 ± 3.1 mm2 (96.6 ± 3.4 mm2 at time 0 and 94.6 ± 3.8 mm2 at time 1), with no significant (P = 0.114) difference between the 2 time points. The median size of the ROI on the ADC maps was 51.4 ± 9.2 mm2 (50.1 ± 12.0 mm2 at time 0 and 52.8 ± 4.9 mm2 at time 1), with no significant (P = 0.314) difference between the 2 time points.
Results of analysis with generalized linear mixed-effects models indicated that hepatic ADC was negatively associated with body weight (P < 0.001) as well as negatively associated with HFF measured with the multiple-echo GRE sequences (P < 0.001). The latter model predicted a 0.29% decrease in ADC for every 1% increase in HFF when the other variables related to the cats were held constant.
Discussion
Our study showed that HFF is among the factors that influence hepatic ADC measurement by MRI in cats. An increase in HFF measured with the multiple-echo GRE sequence following body weight gain was significantly associated with decreased ADC. As conditions related to increased HFF in cats are common, this association must be considered when DW-MRI of the liver is performed to avoid misinterpretation. Magnetic resonance imaging is increasingly used in veterinary medicine to assess liver disease because of its potential diagnostic value. To the authors' knowledge, DW-MRI of the liver in cats has only been reported once in a small group of healthy animals prior to the present study.28
In human medicine, studies6,29–32 have shown that determining the ADC is a useful method to help differentiate benign versus malignant liver lesions, predict the severity of hepatic fibrosis, and monitor the progress of treatment response. The ADC is a common index derived from DW-MRI sequences, and it quantifies the diffusion of water molecules in biological tissues.33 The sensitivity of the imaging sequence to water diffusion can be altered by the b-value determination. In the present study, b values of 0, 400, and 800 s/mm2 were chosen to obtain images with an acceptable spatial resolution on the basis of information available in the literature. Three b values were used instead of 2 to deliver a more accurate calculation of the ADC.33,34
In a clinical setting, caution should be used when comparing the hepatic ADC between patients because the value depends on many technical and patient-related factors. Technical factors that can influence the ADC are field strength, imaging platform, applied gradient strength and magnitude (b values), and type of coils; differences in equipment manufacturers, software, acquisition settings, and other factors can also have effects.35–37 In the present study, these bias factors were minimized because the MRI examinations performed before and after the dietary treatment included the same equipment and sequence parameters. Patient-related factors that can influence the ADC include age,38 motion from neighboring organs,39 and pathological processes such as hepatic steatosis.40 Considering that the same cats were evaluated twice in the present study, variation of factors related to the patients was also minimized, and it is reasonable to assume that our results indicated the change in ADC truly depends on the HFF and body weight gain, as these were the only other evaluated factors that significantly changed between the 2 time points.
Regarding technical considerations, we used a free-breathing technique in the present study because of the short acquisition time and the inability to obtain a triggered sequence in cats, considering the shallow respiration movements. In addition, the free-breathing method is recommended when performing DW-MRI for human patients.41
In veterinary medicine, DW-MRI sequences are still not routinely used for abdominal imaging. Despite equipment requirements and high costs, this technique is a promising noninvasive diagnostic tool for hepatic diseases. The technical advantages of DW-MRI include brief scanning times (a mean of 3 minutes 28 seconds in the present study) that make its use feasible in standard protocols for clinical examinations and the ability to perform evaluations without contrast medium administration. The major technical disadvantages of DW-MRI are the limited spatial resolution, sensitivity to artifacts, and need for general anesthesia. Literature on the use of DW-MRI applied for examination of the liver in cats is limited, and to the authors' knowledge, there are no reports that document the variability of ADC in this species.
Biopsy is currently accepted as the gold standard for determining high fat content in the liver and hepatic lipidosis.23 Liver biopsy has important limitations: it is an invasive technique that can cause pain, transient hypotension, and other complications such as bleeding, infections, bile leakage, pneumothorax, and hemothorax.42,43 Furthermore, it is not a suitable technique for follow-up evaluations. Fat accumulations can be heterogeneously distributed across the liver, so a biopsy sample may not be representative of the pathological processes, and information obtained in this manner could lead to an inaccurate estimation of hepatic fat content.43,44 The severity of hepatic lipidosis is histologically assessed by estimating the percentage of hepatocytes that contain fat droplets. Thus, interpretation of a hepatic biopsy sample is subjective and semiquantitative.45 As a result, alternativemethods to screen for and monitor increased HFF and hepatic lipidosis and to inform clinical decision-making are needed.23,43,44,46
The multiple-echo GRE sequence allows PDFF measurements for the estimation of HFF through a pixel-by-pixel reconstruction algorithm-based separation between water and fat and creates water-only, fat-only, in-phase, opposed-phase, fat fraction, and T2* images. In humans, PDFF measurements have been found to provide a reliable and reproducible estimation of HFF across different manufacturers and field strengths.27 Sequences performed with multiple-echo GRE techniques with fat and water separation yield HFF values that are highly correlated with the HFF estimated with other noninvasive and invasive techniques.47–50 Correlation between PDFF values and HFF values obtained by means of MRI-spectroscopy and histologic examination has been described in multiple publications (such as a 2018 report by Kang et al51). In particular, the correlation between HFF measured with PDFF techniques and other methods has been reported for dogs,24 geese,52 and rats.53
The primary advantages of the described multiple-echo GRE sequence are that it allows visualization and then assessment of the entire liver parenchyma and that images are produced during a single breath-holding episode. The short time of investigation, repeatability of the results, and lack of ionizing radiation make the described multiple-echo GRE sequence a suitable tool for monitoring disease and evaluating treatment efficacy. In addition to quantitative analysis of HFF through the use of ROIs, a color-coded map can be created to provide a visual display of the amount of fat in the liver and enable convenient comparison of images acquired in the same regions at various time points or comparison of findings between multiple-echo GRE images and other sequences. In the cats of the present study, increased HFF was apparent at time 1, even on visual comparison of the images with those obtained at time 0. The distribution of the various colors on these maps reflected the uniformity of fat accumulation in the present study. This could represent an important clinical application of the technique mainly in cats with higher HFF than in our study sample. For a more precise quantification of the HFF, multiple ROIs were drawn on the hepatic parenchyma for cats in the study reported here. The choice of the number, location, and size of the ROIs was made on the basis of human medical literature54 and a study24 of HFF quantification in healthy dogs.
Similar to our results, a negative correlation between HFF and ADC has been found in human medicine.5,7,8 It is postulated that fat droplets inside hepatocytes and extracellular fat accumulation impede the movement of water, resulting in a lower ADC.5 In the cats of our study, it is likely that protons associated with intra- and extracellular fat reduced the interstitial space and thereby reduced diffusivity, similar to what has been described in human medicine. It has also been postulated in human medicine that inflammation and fibrosis may be additional causes for a lower ADC.5 In the present study, diseases affecting the liver in study cats were considered unlikely on the basis of the subjectively normal morphological images and the lack of serum biochemical abnormalities for hepatic variables at both time points.
The positive association between body weight gain and HFF was expected. The mean HFF in the cats of our study after weight gain was 5.37%. In human medicine, hepatic lipidosis is defined by a PDFF > 5.6% in adult patients55 and > 5% in children as assessed by quantitative fat-water separation MRI.56 To our knowledge, no corresponding data are available in feline medicine, but taking these data as reference, the mean HFF of the analyzed cats approximated the cutoffs for hepatic lipidosis in people. These cats, though, were generally healthy and had no other evidence of hepatic abnormalities, so it is possible the increased HFF could have indicated a preclinical stage of hepatic lipidosis.
The 2 primary limitations of the present study were the small sample size and limited observation period, both of which were chosen out of consideration for animal welfare. Moreover, PDFF and hepatic triglyceride content were not biochemically confirmed with liver biopsies. A histologic examination of the liver parenchyma would not have provided quantification of HFF but might have excluded concurrent diseases at the histologic level. Owing to the different spatial resolutions of the DW-MRI and multiple-echo GRE images, it was not possible to draw the ROIs in the same exact locations of the hepatic parenchyma. Thus, some degree of spatial mismatch between the locations evaluated with the 2 methods must be taken into account. Considering that the increase in HFF, although significant, was limited and achieved in a relatively short period of time, further studies conducted over a longer period in cats with higher body condition scores and higher HFFs, as well as investigations of animals with hepatic lipidosis, are needed to completely understand the association between ADC, HFF, and body weight in this species. We would expect an even more marked association between the ADC and HFF with longer observation periods. However, our results suggested that hepatic ADC is negatively associated with HFF following body weight gain in cats, and this association should be considered when interpreting DW-MRI images of the liver.
Acknowledgments
No third-party funding or support was received in connection with this study or the writing or publication of the manuscript. The authors declare that there were no conflicts of interest.
The authors thank Professor Paul Torgerson for assistance with the statistical analysis.
ABBREVIATIONS
| ADC | Apparent diffusion coefficient |
| DW-MRI | Diffusion-weighted MRI |
| GRE | Gradient-recalled echo |
| HFF | Hepatic fat fraction |
| PDFF | Proton density fat fraction |
| ROI | Region of interest |
Footnotes
Hill's Science Diet Adult Optimal Care, Hill's Pet Nutrition Inc, Topeka, Kan.
Ingenia 3.0T scanner, Philips AG Healthcare, Zurich, Switzerland.
dStream Torso coil solution, 32 channel, Philips AG Healthcare, Zurich, Switzerland.
mDIXON Quant, Philips AG Healthcare, Zurich, Switzerland.
Omniscan (Gadodiamid), GE Healthcare AG, Glattbrugg, Switzerland.
Intellispace Portal 2.6.3.5, Philips Medical Systems, Best, Netherlands.
R, version 3.2.3, R Foundation for Statistical Computing, Vienna, Austria.
RStudio software, version 1.1.456 for Mac OS, RStudio, Boston, Mass.
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