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Introduction

  1. Top of page
  2. Introduction
  3. Methods and Procedures
  4. Results
  5. Discussion
  6. Conclusion
  7. Acknowledgments
  8. Disclosure
  9. REFERENCES

Although body composition (BC) data acquisition and ad hoc analysis are both popular and important, selecting an appropriate method or technique for accurate and/or precise assessment of individuals and/or groups remains a challenging task within various sectors of public health. Since 1950s and 1960s, with the pioneer work of Keys and Brozek (1), Forbes et al. (2), Siri (3), Brozek et al. (4), Behnke (5), and Durnin and Rahaman (6), BC almost became a scientific discipline profiling itself with the development of many methods, techniques, and equipments. Popular approaches have been criticized over the years because they are subject to measurement errors and/or violation of basic assumptions underlying their use such as hydrodensitometry (7,8,9,10,11) or anthropometry, e.g., skinfolds (12,13,14,15,16) and the universally accepted new method of choice, the dual-energy X-ray absorptiometry (DXA) (7,17,18,19,20,21).

Curiously, after reviewing the literature of DXA application, one cannot avoid obtaining a very controversial impression of this new method. On the other hand, we find an important number of validation and application studies (too many to refer to all) that support the DXA technique as convenient, as the criterion for % fat for lean body mass (LBM), and as a criterion for bone mineral content (BMC) (8,10,11,22,23). A number of authors as mentioned in Provyn et al. (21) suggest DXA as the gold standard for validation of other techniques essential for the measurement of BC (24,25,26). In addition to the violation of basic assumptions as referred to earlier, one needs to repeat and underline that DXA, hydrodensitometry, anthropometry, air, gas, and water displacement methods, bioelectrical impedance are all indirect in vivo techniques for measuring BC. Validation or even cross-validation in between indirect methods cannot guarantee both accuracy and reality precision. Perfect correlations and low coefficients of variation allow for good predictions and assumptions only (18,21). Possibly the greatest problems with accuracy/precision in DXA are found with fat and lean tissue estimates (27), with its projected areal bone density (17,20,28) and with the basic confusion between overall BC terminology, e.g., fat, adipose tissue (AT), fat-free mass (FFM), LBM, lean and AT free mass (ATFM), bone mineral density (BMD), surface and volume density, BMC, ash weight, actual mineral content, and BMC, with or without soft tissue (ST) covering (12,21,29).

These issues give rise to concern, but the accuracy of absorptiometry can be affected by the choice of calibrating materials. As a consequence, both absolute and relative values can differ substantially between manufacturers, between instruments and the ad hoc software used (10,27). Despite the multitude of DXA validation studies and despite the related controversy of its measuring quality, it is being reaffirmed that there have been comparatively few validation experiments of accuracy, and precision of either bone or BC measurements by cadaver and/or carcass analysis. More of these validations against direct values are necessary before we can be confident about the accuracy of absorptiometry (27). A review of the state of the art of carcass studies related to DXA (28) reveals validation attempts with rhesus monkeys (30), mice (31,32), piglets (9,33,34,35,36,37), pigs (38,39,40), pig hindlegs (21), chickens (41), and with dogs and cats (42). The majority of these validation studies were based on chemical analysis and only a few on direct dissection comparison. Almost all studies indicated perfect correlations for all variables with DXA, but approximately half of the results of the various variables were found to be significantly different (P < 0.001 and P < 0.05). In approximately a third of these studies, DXA was suggested to be valid and accurate for all its variables, whereas two studies indicated significant differences and/or erroneous data at all levels and for all variables. However, two important statements resulting from these studies are retained: (i) dissection and direct comparison combined with bone ashing are considered the most accurate and direct validation technique (9) and (ii) further research with direct dissection and ashing is needed (27), in particular, with focus on the influence of abdominal and thoracic organs associated with dispersed gas/air pockets and internal panniculus adiposus (21). Because BC measurements by DXA are increasingly used in clinical practice and because dissection is the best possible direct measure, no study has been giving clarity yet about the content and meaning of “lean” as produced by DXA, different intratissue combinations, e.g., skin, muscle, viscera, and bone will be related to the DXA-lean variable. The exact knowledge of what is the content of the meaning of “lean” as measured by DXA is mandatory. In order to crossvalidate this study, we will compare fan-beam data, with both dissection and computed tomography (CT) scanning data.

Methods and Procedures

  1. Top of page
  2. Introduction
  3. Methods and Procedures
  4. Results
  5. Discussion
  6. Conclusion
  7. Acknowledgments
  8. Disclosure
  9. REFERENCES

Twelve 6- to 18-month-old “Belgian Native” pigs were prepared for human consumption and were acquired within 2-day intervals, immediately after electroshock slaughter (six females and six castrated males, mean weight ± s.d., 39.509 ± 4.335 kg). Special permission was obtained from the Belgian Directorate General of Public Health, Safety of the Food Chain and Environment, for the transport of the carcasses and for the nonremoval of abdominal and thoracic content, which is a normal procedure in consumption matters. The carcasses were exsanguinated and decapitated between the atlas and the occipital bone. To minimize further dissection error, front and hindlegs were disarticulated distal from humeri and femora, e.g., on elbow and knee levels, respectively. The mean weight ± s.d. of the remaining carcass plus viscera was 33.051 ± 3.324 kg (whole carcass weights being taken with a digital hang scale (KERN-HUS-150K50; Kern & Sohn GmBH, Balingen, Germany) accurate to 50 g). The composition of the carcasses was studied in the following order.

DXA

A QDR 4500A upgraded to Discovery Hologic DXA device (Hologic, Waltham, MA) utilizes a constant X-ray source producing fan-beam dual-energy radiation with effective dose equivalents (EDE) of 5 µSv (e.g., to situate this low radiation in terms of example: a one-way transatlantic flight produces ±80 µSv EDE and a spinal radiograph ∼700 µSv EDE) (27).

The estimations of fat and lean mass are based on extrapolation of the ratio of ST attenuation of two X-ray energies in nonbone-containing pixels. The two X-ray energies are produced by a tungsten stationary anode X-ray tube pulsed alternately as 70 kilovolts (peak) (kVp) and 140 kVp. The software (for Windows-XP version 12.4.3) performs calculations of the differential attenuations of the two photon energies and presents data for each carcass of percentage of fat, fat mass (g), lean mass (g), bone mineral mass (g), BMD (g/cm2), and total weight. According to the manufacturer, a coefficient of variation for human BMD of 0.5% can be expected during repeated measurements.

To determine the reliability of DXA measurements, each pig carcass was scanned three times consecutively without (×2) and with (×1) repositioning. From these data, the coefficient of variation for the different tissue types was calculated.

The DXA equipment was calibrated daily with a spine phantom (supplied by the manufacturers) to assess stability of the measurements, but also calibrated weekly using a step phantom to allow for correction of sources of error related to, e.g., skin thickness.

Computed tomographic scanning

Whole-body scans of the pigs were taken with a CT scanner (type Philips Brilliance BZC 16; Koninklijke Philips Electronics, Eindhoven, the Netherlands) using the following settings: 120 kVp, 200 mAs, pitch 0.641, slice collimation 64 × 0.625 mm, reconstructed slice width 0.75 mm and using the Brilliance V2.3.0.16060 software. Tissues (AT, ST, and bone (B)) were classified based on Hounsfield units (HU), and their respective volumes were calculated using a maximum likelihood Gaussian mixture estimator implemented in MATLAB (The MathWorks, Natick, MA). The following optimal classification scale was employed to determine each tissue: AT: −180..−7 HU; ST: −6..+142 HU and B: +143..+3010 HU (43,44). Tissue volumes were multiplied by their reference densities with AT = 0.923 g/cm3, ST = 1.04 g/cm3, and B = 1.72 g/cm3 to obtain tissue weight estimates.

Dissection, bone ashing, chemical fat and hydration analysis

After the DXA measurements, the carcasses were dissected into their various components as expressed on the tissue-level system: skin, muscle, AT, viscera, and bones (45). Muscle included tendon, blood vessels, and nerves belonging to the ad hoc muscle. The subcutaneous, intramuscular (mostly intratendon), and intravisceral ATs were combined as one tissue. Again, blood vessels and nerves within AT were attributed to AT. Bones were carefully scraped, ligaments were added with muscle tendons to muscle tissue, and cartilage remained part of the bone tissue.

Seven expert prosectors and anatomists worked simultaneously and each dissected particle was collected under cling film and kept in color-labeled, continuously covered plastic containers (12 × 10 × 10 cm) of known weight in order to minimize or eliminate evaporation (21,46). Full container mass was measured during the dissection by two researchers using Mettler-Toledo digital scales (Excellence XS precision balance model 40025; Mettler-Toledo GmBH, Greifensee, Switzerland) accurate to 0.01 g. Once a bone was fully prepared, the same procedure was followed but completed with its hydrostatic weight while placed in a wire cradle suspended to the same scale allowing for the volume-based bone density (g/cm3) calculation.

After the dissection and multiple weighing procedures, samples of all tissues of ∼100–150 g (min–max) were deep-frozen. Small parts were cut off and weighed in recipients of known weight before lyophilization overnight. With dried samples, the water content was measured after storing into metal cells, and fat (lipids) extracted with technical hexane using a Dionex accelerated solvent extractor. After the hexane evaporation of the extraction, total (final) lipid content was determined (weighed).

Part of the dissection protocol of the 12 porcine carcasses was the total defleshing of the skeleton, including the removal of extraosseous soft tendon and ligament tissue by scraping. Cartilage and intraosseous tissue (e.g., intervertebral discs) remained intact. The whole skeleton was diamond-cut into pieces in order to fit in the ashing furnace (type Nabertherm; Nabertherm, Lilienthal, Germany). After incineration, each sample was heated using a ramped temperature protocol of 2 h to 800 °C and ashed for 8 h, as determined by prior pilot work. Before weighing on the Mettler-Toledo precision scale (accurate to 0.01 g), the ash was cooled undercover and collected in a main container. The ashing of one full porcine skeleton took between 50 and 60 h.

Statistical analysis

Data are reported as mean (inline image) ± s.d. Normality of all variables was verified with a Kolmogorov–Smirnov test and all DXA, CT, and dissection data were (matrix) compared with Pearson correlation coefficients, whereas differences were verified with one-way ANOVA repeated measures. Reliability and consistency of these results were verified with intraclass correlation coefficients, and Bland–Altman (47) plots were used to access agreement of the direct carcass dissection data with the indirect DXA and CT estimates. All statistical tests were performed using SPSS 16.0 for Windows (SPSS, Chicago, IL), and P values of <0.05 indicated significant differences.

Results

  1. Top of page
  2. Introduction
  3. Methods and Procedures
  4. Results
  5. Discussion
  6. Conclusion
  7. Acknowledgments
  8. Disclosure
  9. REFERENCES

The purpose of this study is to compare directly and indirectly obtained data of masses and densities (e.g., of whole-body bone, adipose, and nonadipose tissue) using three different technique yield information on the ad hoc terminology used in the respective methodologies.

Table 1 shows an overview of terminology used per technique as applied and that are assumed to measure the same values.

Table 1.  Different terminologies assumed to measure a similar outcome
inline image

Although the basic assumption of equality of outcome and despite the different terminology used, knowledge of the ad hoc mass and density names will create a better understanding of the respective data acquisitions (e.g., Table 2).

Table 2.  Comparison between direct dissection data values with the corresponding DXA and CT values
inline image

Table 2 combines the data acquisition of all directly obtained measures and the complete set of indirect estimates made by DXA and CT. The purpose of this Table 2 is to evaluate the predictive quality of both DXA and CT, but also to evaluate precision and accuracy between direct and indirect values. For a good understanding and despite the significance of a correlation found, this study considers r ≥0.90 as a good, r ≥0.80 as a medium, and r ≥70 as an average (mediocre) indicator of prediction confirmed or rejected by the intraclass correlation coefficient. The ANOVA statistics are considered as an indicator of precision or accuracy. Significant differences are set at P < 0.05. If not significantly different with the dissection reference, one can assume an acceptable level of measurement precision. A nonsignificant result between DXA and CT indicates similarity between data only because DXA or CT is considered to be a reference in this study.

Table 2 confirms that for almost all ST comparisons, including total masses, a majority of good correlations (r ≥ 0.90), two medium correlations (r ≥ 0.80), and two average correlations (r ≥ 0.70), adiposity prediction expressed in % seems to be problematic for the CT. Despite the majority of good prognoses for prediction related to the dissection reference, we do find significant differences in accuracy for total masses (DXA), adiposity (g and %) (DXA and CT) for all nonadipose ST combinations (DXA and CT) and for all bony comparisons. Except for the ashing, there are indications of acceptable precision and comparability with DXA-BMC. Agreement and disagreement of DXA and CT data acquisition with the dissection reference of the compared tissues and tissue combinations are shown in Figures 1 and 2 with 12 Bland–Altman (47) plots. The solid lines indicate the mean difference deviation from zero and the individual variation between ±1.96 s.d. (dashed lines). The intraclass correlation coefficient and the Bland–Altman plots confirm the findings as shown in Table 2.

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Figure 1. Bland–Altman plots comparing adipose tissue (AT) and adipose tissue free mass (ATFM) by dissection to dual-energy X-ray absorptiometry (DXA) and computed tomography (CT) measures with assumed similar outcome. BMC, bone mineral content; DISS, dissection.

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image

Figure 2. Bland–Altman plots comparing different nonadipose tissue combinations by dissection to dual-energy X-ray absorptiometry (DXA) measures with assumed similar outcome. BMC, bone mineral content; BMD, bone mineral density; DISS, dissection.

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The dissection tissue masses were subdivided according to anatomic segmentation into upper limb, lower limb, and trunk (e.g., for skin, muscle, and bone). For AT, additional differentiation was made for subcutaneous (e.g., external) and visceral (e.g., internal) trunk AT. For each segment, the water content and the fat (e.g., lipid) content was determined for the respective tissues and presented as % of the studied mass per tissue in Table 3.

Table 3.  Water (lyophilization) and lipid (ether extraction) content of different tissues and relationship
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Discussion

  1. Top of page
  2. Introduction
  3. Methods and Procedures
  4. Results
  5. Discussion
  6. Conclusion
  7. Acknowledgments
  8. Disclosure
  9. REFERENCES

Given the basic reasoning that the measurement of whole-body adiposity (in g or %), or nonadipose tissue (in g) and density (in g/cm3) with different techniques using different equipment should produce similar, if not identical results on the same individuals, cannot be supported because of underlying assumptions, models, or approaches of the techniques and/or equipment are different (7,14,48).

This can be perfectly illustrated by the experiment on one single healthy male subject of which its % adiposity or fatness was measured with four different techniques on the same day, e.g., with an anthropometric formula, with bioelectrical impedance, with hydrostatic weighing including the calculation via the Siri (3) formula, and with whole-body potassium counting.

According to the original basic reasoning, the result of these four measures of adiposity should be the same. On the contrary, one notices with an anthropometric formula 18.5%, with bioelectrical impedance 13.3%, with Siri 9.6%, and with potassium counting 21.0% of adiposity was found. In other words, a maximum difference of 11.4% that surpasses the value of whole-body % adiposity based on hydrostatic weighing obtained density and ad hoc calculation with the Siri (3) formula that was considered as the reference method. The reality is that one is measuring different adiposity approaches with the same confusing terminology, e.g., % whole-body fat.

Body fat (BF) is defined as the ether-extractable constituent of body tissues (Table 3), and must be considered as a chemical component of the body. This is already known since Keys and Brozek (1). The interchangeable use of the terms BF and AT has led and is leading still to ambiguities and serious error. Among all DXA validation studies, only a few (9,32) have defined the meaning of its adiposity variables mentioning or précising as DXA fat and lean against chemical (CHEM fat and CHEM lean).

In the Anglo-Saxon literature, in particular, the term “fat” is commonly used quantitatively when referring to the degree of obesity of a body and with “fatness” qualitatively referring to the appearance of the body that results from the deposition of AT (29). Technically, “fat” may be defined biochemically as extractable lipid that consists of depot lipids such as the triglycerides and free fatty acids from AT and also so-called “essential” lipids such as structural phospholipids of cell membranes and nervous tissues, lipids of bone marrow, and a small moiety of other lipid-based compounds. Because of the confusion surrounding terms that are used both colloquially and technically, the terms “fat” and “fatness” should not be used. The term “fat” used in a biochemical sense should be replaced by the term “lipid,” and the term “fatness” will be replaced by “adiposity” when referring to the quantity of AT in the body. ATs are masses separable by gross dissection and includes not just lipid but also the nonlipid constituents of cells, such as water and protein, and of course, the bulk of the subcutaneous AT and tissue surrounding organs, viscera and variable amounts between muscles, e.g., the intramuscular AT.

These phenomena are known since 1950s and 1960s (4) and were reinforced in 1980s by extensive direct data acquisition (46,49).

Table 1 indicates other discrepancies, e.g., for the nonadipose terminology. ATFM is an anatomical concept and lays in the continuation of the AT vs. FM. DXA pretends to measure lean or LBM as opposed to FFM, which could be expected because manufacturers claim to measure chemical components.

In attempts to identify the physiologically relevant tissues, the concept of LBM was introduced more than half a century ago (50). This consists of the FFM plus the essential fat specification that has varied from 2 to 10% for the FFM. Because of its imprecise definition, this term also has led to much confusion in the literature and is often erroneously used as a synonym for FFM.

In addition to FFM and LBM, the anatomical concept of ATFM (Table 1) was proposed as a normalizing approach for interpopulation comparisons (51). With the confounding effect of the high variability of AT removed, the composition of the ATFM shows smaller deviations of its components and smaller differences between males and females than when body mass is used as a reference (52).

Conversion from FFM to LBM and/or to ATFM is susceptible to significant error because we are dealing with two totally different models.

If we look at the mean value level of the respective variables in Table 2, there cannot be any doubt that both DXA and CT are producing anatomical–morphological quantities, evidently at all adipose and nonadipose combinations. In addition, DXA and CT do not take into account the water content and lipid content variations (Table 3) of both its adipose and nonadipose constituents. Small variation of tissue hydration may explain important differences of ad hoc estimates (11,53,54).

Both in CT, DXA, and other newer technologies (55), body fat is calculated on the constancy assumption that 74.6% of LBM (e.g., lean or lean + BMC) is water (31). This assumed constancy of hydration, e.g., the observed ratio of total body water to FFM was confirmed in humans by Wang et al. (54). However, this assumption is subject to some questions that highlight the need for more research on the matter. Viewing tissue water content obtained by lyophilization in several human tissue studies, one can make two observations: (i) assuming a constant % of water in FFM may be jeopardized by the variable tissue water content within and between the tissues that compose FFM; and (ii) water content in AT is highly variable, e.g., ranging from ±17% to ±84% in humans (21).

This is confirmed in this study on animal corpses with % whole-body water content ranging from ±20 to ±50% (Table 3) repeating that the constancies claimed by DXA and CT cannot be maintained (e.g., with fluid ranging between ±50 and ±61% for skin, between ±39 and ±49% for bone, but little variability for muscle).

Because no total tissue lipid extraction was carried out because technical circumstances allowed sample fractionation only, lipid content is expressed as % of the measured sample mass. From sample masses being identical for hydration and lipid fractionation (Table 3), we learn that lipid content of tissues is variably related to its ad hoc fluid content, but if the extremities are considered separately, one notices an apparent constancy both in hydration and lipid fractionation. The fact that all trunk tissue data (e.g., in skin AT, muscle, and bone) deviate both, but nonsystematically in hydration and lipid content from the upper and lower extremities indicate the importance of the trunk as discriminating segment and the associated abdominal/metabolic syndrome theories. As Elowsson et al. (9) and Provyn et al. (21) were previously evaluating the accuracy of DXA with dissection in animals, both studies motivated the choice of using plain carcasses (decapitated pigs without abdominal and thoracic organs) or just hindlegs to minimize various errors. According to Elowsson et al. (9), with DXA this would marginally increase DXA's underestimation. This can no longer be supported; on the contrary, not measuring the internal trunk will just increase the error because of an assumption of segment constancy of hydration and ad hoc lipid fractionation. Wang et al. (54) examined in vitro and in vivo studies allowing a review and critical appraisal of the importance of hydration of FFM and confirming the findings of Provyn et al. (21). They conclude that, even though methodological limitations preclude a highly accurate analysis, adult mammals, including humans, share in common, a relatively constant hydration of FFM. The segmental data presented in Table 3 within a four-component dissection model dismisses the idea of constant hydration of FFM. In addition, the assumed ad hoc constancy of 0.73 cannot be retained.

The question whether the hydration status of FFM, LBM, or ATFM reflects physiologic regulatory mechanisms (56,57) cannot be answered, but it seems that trunk nonadipose tissues may affect hydration differently than the lean tissues of the extremities or vice versa (Table 3).

Regardless of the existing mechanisms and regardless of the hydration and lipid (fat) content of nonadipose tissue, this study has not been able to detect what the content is of the DXA nonadipose variables, e.g., “lean” and/or “lean + BMC.” We still do not know what DXA is exactly measuring under these ad hoc headings.

“Lean” compared with muscle tissue, with muscle plus skin tissue, and with muscle plus skin plus viscera (dissection and CT) resulted in equally high correlations (r values between 0.94 and 0.99) assuming a good prediction estimate but with systematic significant difference confirming its imprecision, “lean plus BMC” is certainly not measuring ATFM (e.g., skin + muscle + viscera + bone), although its high r = 0.99, but again with a significant difference (P < 0.001) indicating a lack of precision and accuracy. Contrary to Bloebaum et al. (58), but in agreement with Louis et al. (59), BMC seems a good estimate (r = 0.73) with no significant difference of its ash weight. The impression is given, however, that DXA nonadipose values are expressed as anatomical–morphological values combined with chemical elements. This study cannot confirm what the nonadipose component of DXA is measuring, but it does confirm that all the DXA components and the CT bone components are subject not only to measurement error but also to terminology error and violation of basic assumptions.

It is known since many decennia that density in its weight/volume quantification (g/cm3) can be considered as an additional and separate dimension of BC. The DXA-derived BMD, however, is a weight/surface quantification (g/cm2) and therefore not a true density, nor the density based on which indication of osteoporosis classifications were studied in the past (17,18,19,20,60).

In a pilot (dissection) study using porcine hindlegs in which DXA BMD was compared with bone covered with muscle, AT, and skin tissue, and compared with scraped bones only (21,28), it was found that DXA BMD underestimates true density with >40%. In this study (Table 2), under whole-body conditions, one notices a similar level of high underestimation of DXA but with a better correlation, e.g., r = 0.68 for the whole-body value against r = 0.39 for the hindleg study. The extensive work done by Bolotin (20) shows DXA-measured BMD methodology (in vivo) to be an intrinsically flawed and misleading indicator of bone mineral status and an erroneous gauge of relative fracture risk. The transfer of their findings to the in situ carcass situation of this study confirms that the DXA methodology cannot provide accurate, quantitatively precise, meaningful determinations of true bone densities and proper bone mass because of the contamination of independent ST, e.g., fluid and lipid content contributions.

Conclusion

  1. Top of page
  2. Introduction
  3. Methods and Procedures
  4. Results
  5. Discussion
  6. Conclusion
  7. Acknowledgments
  8. Disclosure
  9. REFERENCES

The majority of present consensual acceptance and understanding of the DXA estimate quality rests solely upon a number of well-established, multiconfirmed, in vivo and in situ significant high correlations. This is confirmed.

In terms of true “reality precision” measures, DXA produces inaccurate and misleading values at all levels of its output. Both the adipose and nonadipose components of DXA ignore the ad hoc lipid content, and the nonadipose variables do not take into account the true composing tissues. Lean and lean + BMC of DXA do not correspond to anatomical–morphological tissue combinations, nor to chemical values. It cannot be determined what DXA really measures. Total mass of BMC (vs. ash weight) are the single two variables with a close reality and nonsignificant difference output.

DXA and CT are based on a series of constancies within tissues, regardless of segments, hydration, and lipid content variability.

The hypothesis that DXA methodology provides accurate, precise, and relevant BC determinations is proven to be unwarranted and misplaced.

Acknowledgments

  1. Top of page
  2. Introduction
  3. Methods and Procedures
  4. Results
  5. Discussion
  6. Conclusion
  7. Acknowledgments
  8. Disclosure
  9. REFERENCES

We are grateful for the assistance of the prosectors during dissection, for the technical assistance of L. Sutton for the DXA calculations, and P. Clerinx for the CT calculations.

REFERENCES

  1. Top of page
  2. Introduction
  3. Methods and Procedures
  4. Results
  5. Discussion
  6. Conclusion
  7. Acknowledgments
  8. Disclosure
  9. REFERENCES