Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Material and Methods
Patient Population
Image Analysis
Results
Discussion
References
All Topics
Biology
Nutrition

Influence of tube potential on CT body composition analysis

Profile image of Lena MartinLena Martin

2018, Nutrition (Burbank, Los Angeles County, Calif.)

https://doi.org/10.1016/J.NUT.2017.12.016
visibility

…

description

19 pages

link

1 file

Abstract

Our purpose was to investigate whether tube potential in contrast-enhanced computed tomography (CT) affects body composition analysis. Images from dual-source, dual-energy CT from the abdomen with intravenous contrast media administration were used. A total of 17 patients (11 women, mean age 52) with a mean body mass index of 20.8 kg/cm were included. Simultaneously acquired images with a tube voltage of 80 kV and 140 kV were compared. Body composition was analyzed on a single slice at the L3 level. Parameters evaluated included muscle and fat attenuation (Hounsfield units [HU]), skeletal muscle index (cm/m), muscle area (cm), and steatotic muscle area (cm). Significant differences between 80 kV and 140 kV series were compared using the paired Student's t test. Tube potential affected muscle attenuation with an average difference of 17% between 80 kV and 140 kV series (48 HU versus 41 HU, P < 0.01), fat attenuation (-84 HU versus -69 HU, P < 0.01), skeletal muscle index of...

Related papers

Using Body Mass Index and Bioelectric Impedance Analysis to Assess the Need for Positive Oral Contrast Agents Before Abdominopelvic CT
ismail ali

AJR. American journal of roentgenology, 2018

The objective of this study was to determine whether body fat percentage, measured using a portable handheld bioelectric impedance analysis (BIA) device, and body mass index (BMI, weight in kilograms divided by the square of height in meters) can estimate the amount of intraabdominal and intrapelvic fat and thereby predict the need for oral contrast material before abdominopelvic CT. A prospective, institutional review board-approved study consisting of 101 patients who presented to the emergency department of a level I trauma center was conducted between June 1, 2016, and July 19, 2016. A medical student calculated patients' BMI and obtained body fat measurements from a handheld BIA device. Three fellowship-trained and board-certified radiologists who were blinded to the collected data then assigned a score of 1-5 on the basis of the amount of intraabdominal and intrapelvic fat seen on CT images. A McNemar test was used to compare overall sensitivity and specificity of this met...

View PDFchevron_right
Skeletal muscle fat quantification by dual-energy computed tomography in comparison with 3T MR imaging
Jin Yamamura

European Radiology, 2021

Objectives To quantify the proportion of fat within the skeletal muscle as a measure of muscle quality using dual-energy CT (DECT) and to validate this methodology with MRI. Methods Twenty-one patients with abdominal contrast-enhanced DECT scans (100 kV/Sn 150 kV) underwent abdominal 3-T MRI. The fat fraction (DECT-FF), determined by material decomposition, and HU values on virtual non-contrast-enhanced (VNC) DECT images were measured in 126 regions of interest (≥ 6 cm 2) within the posterior paraspinal muscle. For validation, the MR-based fat fraction (MR-FF) was assessed by chemical shift relaxometry. Patients were categorized into groups of high or low skeletal muscle mean radiation attenuation (SMRA) and classified as either sarcopenic or non-sarcopenic, according to the skeletal muscle index (SMI) and cutoff values from non-contrast-enhanced single-energy CT. Spearman's and intraclass correlation, Bland-Altman analysis, and mixed linear models were employed. Results The correlation was excellent between DECT-FF and MR-FF (r = 0.91), DECT VNC HU and MR-FF (r =-0.90), and DECT-FF and DECT VNC HU (r = − 0.98). Intraclass correlation between DECT-FF and MR-FF was good (r = 0.83 [95% CI 0.71-0.90]), with a mean difference of-0.15% (SD 3.32 [95% CI 6.35 to − 6.66]). Categorization using the SMRA yielded an eightfold difference in DECT VNC HU values between both groups (5 HU [95% CI 23-11], 42 HU [95% CI 33-56], p = 0.05). No significant relationship between DECT-FF and SMI-based classifications was observed. Conclusions Fat quantification within the skeletal muscle using DECT is both feasible and reliable. DECT muscle analysis offers a new approach to determine muscle quality, which is important for the diagnosis and therapeutic monitoring of sarcopenia, as a comorbidity associated with poor clinical outcome. Key Points • Dual-energy CT (DECT) material decomposition and virtual non-contrast-enhanced DECT HU values assess muscle fat reliably. • Virtual non-contrast-enhanced dual-energy CT HU values allow to differentiate between high and low native skeletal muscle mean radiation attenuation in contrast-enhanced DECT scans. • Measuring muscle fat by dual-energy computed tomography is a new approach for the determination of muscle quality, an important parameter for the diagnostic confirmation of sarcopenia as a comorbidity associated with poor clinical outcome.

View PDFchevron_right
Effects of Contrast Phases on Automated Measurements of Muscle Quantity and Quality Using CT
Yousun Ko

Korean Journal of Radiology, 2021

Objective: Muscle quantity and quality can be measured with an automated system on CT. However, the effects of contrast phases on the muscle measurements have not been established, which we aimed to investigate in this study. Materials and Methods: Muscle quantity was measured according to the skeletal muscle area (SMA) measured by a convolutional neural network-based automated system at the L3 level in 89 subjects undergoing multiphasic abdominal CT comprising unenhanced phase, arterial phase, portal venous phase (PVP), or delayed phase imaging. Muscle quality was analyzed using the mean muscle density and the muscle quality map, which comprises normal and low-attenuation muscle areas (NAMA and LAMA, respectively) based on the muscle attenuation threshold. The SMA, mean muscle density, NAMA, and LAMA were compared between PVP and other phases using paired t tests. Bland-Altman analysis was used to evaluate the inter-phase variability between PVP and other phases. Based on the cutoffs for low muscle quantity and quality, the counts of individuals who scored lower than the cutoff values were compared between PVP and other phases. Results: All indices showed significant differences between PVP and other phases (p < 0.001 for all). The SMA, mean muscle density, and NAMA increased during the later phases, whereas LAMA decreased during the later phases. Bland-Altman analysis showed that the mean differences between PVP and other phases ranged-2.1 to 0.3 cm 2 for SMA,-12.0 to 2.6 cm 2 for NAMA, and-2.2 to 9.9 cm 2 for LAMA.The number of patients who were categorized as low muscle quantity did not significant differ between PVP and other phases (p ≥ 0.5), whereas the number of patients with low muscle quality significantly differed (p ≤ 0.002). Conclusion: SMA was less affected by the contrast phases. However, the muscle quality measurements changed with the contrast phases to greater extents and would require a standardization of the contrast phase for reliable measurement.

View PDFchevron_right
Comparison of CT and Dual-Energy DEXA Using a Modified Trunk Compartment in the Measurement of Abdominal Fat
Joseph Anderson

Endocrine, 2005

The quantification of abdominal fat is a marker of health risk. While dual-energy x-ray absorptiometry (DEXA) is easily applied, it measures overall fat, although abdominal fat may be a better indicator of health risk from obesity. We have evaluated whether a subcomponent of DEXA measurements correlates better with computed tomography (CT) for body fat than those traditionally used. Forty-seven healthy adults (22 M/ 25 F), aged 54.5 ± 15.8 yr (mean ± SD), with BMI of 27.1 ± 4.6 kg/m 2 participated in a cross-sectional study. Body fat was measured using abdominal CT and DEXA for total fat, trunk fat, and a modified trunk measurement that excludes the chest, termed "lower trunk," and compared. The coefficient of variation for DEXA measurements for trunk, lower trunk, and total body were 1.98, 3.12, and 0.85%, respectively. Mean DEXA for percentage fat ranged from 31.7% to 34.1% for trunk, lower trunk, and total body, compared to 54.2% for abdominal CT (p < 0.003 for each pairwise comparison). Lower trunk, whole trunk, and total body DEXA measurements were not different. Measurement of subcomponents of fat content by DEXA is not superior to whole body measurements and remains consistently lower than measurements by CT.

View PDFchevron_right
Quantification of liver and muscular fat using contrast-enhanced Dual Source Dual Energy Computed Tomography compared to an established multi-echo Dixon MRI sequence
Malte Bongers

European Journal of Radiology

Purpose: To investigate the feasibility of liver fat quantification in contrast-enhanced dual source dual energy computed tomography (DECT) using multi-echo Dixon magnetic resonance imaging (MRI) as reference standard. Method: Patients who underwent MRI of the liver including a multi-echo Dixon sequence for estimation of proton density fat fraction in 2017 as well as contrast-enhanced DECT imaging of the abdomen were included in this retrospective, monocentric IRB approved study. Furthermore, patients with a hepatic fat amount >5% who were examined in 2018 with MRI and DECT were included. The final study group consisted of 81 patients with 90 pairs of examinations. Analysis of parameter maps was performed manually using congruent regions of interest which were placed in the liver parenchyma, in the erector spinae muscles, and psoas major muscles. Results: Mean patient age was 61 ± 13 years. Median time between MRI and DECT was 48 days. MRI liver fat quantification resulted in a median of 3.8% (IQR: 2.2-8.2%) compared to 1.8% (IQR: 0-6.3%) in DECT (p < 0.001), with a Spearman correlation of 0.73. Bland-Altman analysis resulted in a systematic underestimation of liver fat in DECT, with a mean difference of −1.7%. Fat quantification in the erector spinae muscles (p = 0.257) and the psoas major muscles (p = 0.208) was not significantly different in DECT compared to MRI. Conclusions: Liver and muscular fat quantification in portal-venous phase DECT is feasible with good to excellent correlation compared to a multi-echo Dixon MRI sequence analysis. While there is an underestimation of the liver fat content in DECT, there are no significant differences between DECT and MRI fat quantification of the erector spinae and psoas major muscles.

View PDFchevron_right
Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies
Robin Strand

Scientific Reports

Computed Tomography (CT) allows detailed studies of body composition and its association with metabolic and cardiovascular disease. The purpose of this work was to develop and validate automated and manual image processing techniques for detailed and efficient analysis of body composition from CT data. The study comprised 107 subjects examined in the Swedish CArdioPulmonary BioImage Study (SCAPIS) using a 3-slice CT protocol covering liver, abdomen, and thighs. Algorithms were developed for automated assessment of liver attenuation, visceral (VAT) and subcutaneous (SAT) abdominal adipose tissue, thigh muscles, subcutaneous, subfascial (SFAT) and intermuscular adipose tissue. These were validated using manual reference measurements. SFAT was studied in selected subjects were the fascia lata could be visually identified (approx. 5%). In addition, precision of manual measurements of intra-(IPAT) and retroperitoneal adipose tissue (RPAT) and deep-and superficial SAT was evaluated using repeated measurements. Automated measurements correlated strongly to manual reference measurements. The SFAT depot showed the weakest correlation (r = 0.744). Automated VAT and SAT measurements were slightly, but significantly overestimated (≤4.6%, p ≤ 0.001). Manual segmentation of abdominal sub-depots showed high repeatability (CV ≤ 8.1%, r ≥ 0.930). We conclude that the low dose CT-scanning and automated analysis makes the setup suitable for large-scale studies. Studies of body composition can improve the understanding of for example type-2 diabetes (T2D) and cardiovascular disease (CVD) and their prediction and prevention. Medical imaging techniques, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) can be used for detailed studies of human body composition. These techniques are often used to quantify liver fat, muscle, and adipose tissue distribution. Increased liver fat can be estimated from liver attenuation measured by CT and is believed to be crucial component in development of dyslipidemia associated with obesity. Abdominal adipose tissue is often separated into subcutaneous and intra-abdominal, or visceral adipose tissue, (SAT and VAT, respectively) 1-6 as these are known to have different associations to glucose tolerance 7 and thereby to T2D and CVD risk. More recently the importance of further separating these into sub-depots has gained attention. The VAT depot can be separated into intraand retroperitoneal adipose tissue 8-10 (IPAT and RPAT, respectively) and the SAT can be separated into deep and superficial depots (DSAT and SSAT, respectively). DSAT and SSAT have been found to differ in structure and function 5, 11, 12 , in association to insulin resistance 5, 12 , and in triglyceride saturation 12, 13. Quantification of thigh composition, in terms of muscle and adipose tissue sub-depots has also been found of importance in metabolic studies 14-17. CT creates images in absolute Hounsfield units (HU) but exposes the subject to ionizing radiation limiting the number of slices that can be obtained. The Swedish CardioPulmonary bioImage Study (SCAPIS) 18 , has a primary aim to study cardiovascular and pulmonary disease for which CT angiography and CT of the lungs are preferred methods. The study also includes CT imaging of liver, abdominal adipose tissue and thighs for the purpose of detailed body composition phenotyping. It aims to include 30,000 study subjects in the age range 50 to 64 years in

View PDFchevron_right
Correlation of Patient Weight and Cross-Sectional Dimensions with Subjective Image Quality at Standard Dose Abdominal CT
Jose Varghese

Korean Journal of Radiology, 2003

Objective: We evaluated the association between patients' weight and abdominal cross-sectional dimensions and CT image quality.

View PDFchevron_right
A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care
Vickie Baracos

Applied Physiology, Nutrition, and Metabolism, 2008

Human body composition is important in numerous cancer research domains. Our objective was to evaluate clinically accessible methods to achieve practical and precise measures of body composition in cancer patients. Dual-energy X-ray absorptiometry (DXA)-based analysis of fat and fat-free mass was performed in 50 cancer patients and compared with bioelectrical impedance analysis (BIA) and with regional computed tomography (CT) images available in the patients’ medical records. BIA overestimated or underestimated fat-free mass substantially compared with DXA as the method of reference (up to 9.3 kg difference). Significant changes in fat-free mass over time detected with DXA in a subset of 21 patients (+2.2 ± 3.2%/100 days, p = 0.003), was beyond the limits of detection of BIA. Regional analysis of fat and fat-free tissue at the 3rd lumbar vertebra with either DXA or CT strongly predicted whole-body fat and fat-free mass (r = 0.86–0.94; p &lt; 0.001). CT images provided detail on spec...

View PDFchevron_right
Optimal Abdominal CT Image Quality in Non-Lean Patients: Customization of CM Injection Protocols and Low-Energy Acquisitions
Elina Khasanova

Diagnostics

We compared the image quality of abdominopelvic single-energy CT with 100 kVp (SECT-100 kVp) and dual-energy CT with 65 keV (DECT-65 keV) obtained with customized injection protocols to standard abdominopelvic CT scans (SECT-120 kVp) with fixed volumes of contrast media (CM). We retrospectively included 91 patients (mean age, 60.7 ± 15.8 years) with SECT-100 kVp and 83 (mean age, 60.3 ± 11.7 years) patients with DECT-65 keV in portovenous phase. Total body weight-based customized injection protocols were generated by a software using the following formula: patient weight (kg) × 0.40/contrast concentration (mgI/mL) × 1000. Patients had a prior abdominopelvic SECT-120 kVp with fixed injection. Iopamidol-370 was administered for all examinations. Quantitative and qualitative image quality comparisons were made between customized and fixed injection protocols. Compared to SECT-120 kVp, customized injection yielded a significant reduction in CM volume (mean difference = 9–12 mL; p ≤ 0.00...

View PDFchevron_right
Body composition determinants of radiation dose during abdominopelvic CT
Niamh Moore

Insights into imaging, 2017

We designed a prospective study to investigate the in-vivo relationship between abdominal body composition and radiation exposure to determine the strongest body composition predictor of dose length product (DLP) at CT. Following institutional review board approval, quantitative analysis was performed prospectively on 239 consecutive patients who underwent abdominopelvic CT. DLP, BMI, volumes of abdominal adipose tissue, muscle, bone and solid organs were recorded. All measured body composition parameters correlated positively with DLP. Linear regression (R(2) = 0.77) revealed that total adipose volume was the strongest predictor of radiation exposure [B (95% CI) = 0.027(0.024-0.030), t=23.068, p &lt; 0.001]. Stepwise linear regression using DLP as the dependent and BMI and total adipose tissue as independent variables demonstrated that total adipose tissue is more predictive of DLP than BMI [B (95% CI) = 16.045 (11.337-20.752), t=6.681, p &lt; 0.001]. The volume of adipose tissue w...

View PDFchevron_right

References (27)

  1. Ryan AM, Power DG, Daly L, Cushen SJ, Ni Bhuachalla E, Prado CM. Cancer- associated malnutrition, cachexia and sarcopenia: the skeleton in the hospital closet 40 years later. Proc Nutr Soc. 2016;75(2):199-211.
  2. Paris M, Mourtzakis M. Assessment of skeletal muscle mass in critically ill patients: considerations for the utility of computed tomography imaging and ultrasonography. Curr Opin Clin Nutr Metab Care. 2016;19(2):125-30.
  3. Heymsfield SB, Gonzalez MC, Lu J, Jia G, Zheng J. Skeletal muscle mass and quality: evolution of modern measurement concepts in the context of sarcopenia. Proc Nutr Soc. 2015;74(4):355-66.
  4. Karlsson A, Rosander J, Romu T, Tallberg J, Gronqvist A, Borga M, et al. Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water-fat MRI. J Magn Reson Imaging. 2015;41(6):1558-69.
  5. Levelt E, Pavlides M, Banerjee R, Mahmod M, Kelly C, Sellwood J, et al. Ectopic and Visceral Fat Deposition in Lean and Obese Patients With Type 2 Diabetes. J Am Coll Cardiol. 2016;68(1):53-63.
  6. Mazonakis M, Damilakis J. Computed tomography: What and how does it measure? Eur J Radiol. 2016;85(8):1499-504.
  7. Mattsson S, Thomas BJ. Development of methods for body composition studies. Physics in medicine and biology. 2006;51(13):R203-28.
  8. Agustsson T, Wikrantz P, Ryden M, Brismar T, Isaksson B. Adipose tissue volume is decreased in recently diagnosed cancer patients with cachexia. Nutrition. 2012;28(9):851-5.
  9. Martin L, Birdsell L, Macdonald N, Reiman T, Clandinin MT, McCargar LJ, et al. Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2013;31(12):1539-47.
  10. Anandavadivelan P, Brismar TB, Nilsson M, Johar AM, Martin L. Sarcopenic obesity: A probable risk factor for dose limiting toxicity during neo-adjuvant chemotherapy in oesophageal cancer patients. Clin Nutr. 2016;35(3):724-30.
  11. Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, et al. Definition and classification of cancer cachexia: an international consensus. The Lancet Oncology. 2011;12(5):489-95.
  12. Rossner S, Bo WJ, Hiltbrandt E, Hinson W, Karstaedt N, Santago P, et al. Adipose tissue determinations in cadavers--a comparison between cross-sectional planimetry and computed tomography. Int J Obes. 1990;14(10):893-902.
  13. Mitsiopoulos N, Baumgartner RN, Heymsfield SB, Lyons W, Gallagher D, Ross R. Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. Journal of applied physiology (Bethesda, Md : 1985). 1998;85(1):115-22.
  14. Montano-Loza AJ, Angulo P, Meza-Junco J, Prado CM, Sawyer MB, Beaumont C, et al. Sarcopenic obesity and myosteatosis are associated with higher mortality in patients with cirrhosis. J Cachexia Sarcopenia Muscle. 2016;7(2):126-35.
  15. Mourtzakis M, Prado CM, Lieffers JR, Reiman T, McCargar LJ, Baracos VE. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab. 2008;33(5):997-1006.
  16. Lira D, Padole A, Kalra MK, Singh S. Tube potential and CT radiation dose optimization. AJR Am J Roentgenol. 2015;204(1):W4-10.
  17. Seyal AR, Arslanoglu A, Abboud SF, Sahin A, Horowitz JM, Yaghmai V. CT of the Abdomen with Reduced Tube Voltage in Adults: A Practical Approach. Radiographics. 2015;35(7):1922-39.
  18. Yu L, Li H, Fletcher JG, McCollough CH. Automatic selection of tube potential for radiation dose reduction in CT: a general strategy. Med Phys. 2010;37(1):234-43.
  19. McCollough CH, Leng S, Yu L, Fletcher JG. Dual-and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications. Radiology. 2015;276(3):637-53.
  20. Morsbach F, Wurnig MC, Muller D, Krauss B, Korporaal JG, Alkadhi H. Feasibility of single-source dual-energy computed tomography for urinary stone characterization and value of iterative reconstructions. Invest Radiol. 2014;49(3):125-30.
  21. Winklhofer S, Benninger E, Spross C, Morsbach F, Rahm S, Ross S, et al. CT metal artefact reduction for internal fixation of the proximal humerus: value of mono-energetic extrapolation from dual-energy and iterative reconstructions. Clin Radiol. 2014;69(5):e199- 206.
  22. Tong Y, Udupa JK, Torigian DA. Optimization of abdominal fat quantification on CT imaging through use of standardized anatomic space: a novel approach. Med Phys. 2014;41(6):063501.
  23. McCollough CH, Primak AN, Braun N, Kofler J, Yu L, Christner J. Strategies for reducing radiation dose in CT. Radiol Clin North Am. 2009;47(1):27-40.
  24. Phelps ME, Hoffman EJ, Ter-Pogossian MM. Attenuation coefficients of various body tissues, fluids, and lesions at photon energies of 18 to 136 keV. Radiology. 1975;117(3 Pt 1):573-83.
  25. Nakayama Y, Awai K, Funama Y, Hatemura M, Imuta M, Nakaura T, et al. Abdominal CT with low tube voltage: preliminary observations about radiation dose, contrast enhancement, image quality, and noise. Radiology. 2005;237(3):945-51.
  26. van der Werf A, Dekker IM, Meijerink MR, Wierdsma NJ, de van der Schueren MAE, Langius JAE. Skeletal muscle analyses: agreement between non-contrast and contrast CT scan measurements of skeletal muscle area and mean muscle attenuation. Clin Physiol Funct Imaging. 2017.
  27. Rollins KE, Javanmard-Emamghissi H, Awwad A, Macdonald IA, Fearon KCH, Lobo DN. Body composition measurement using computed tomography: Does the phase of the scan matter? Nutrition. 2017;41:37-44.

Related papers

Body composition evaluation with computed tomography: Contrast media and slice thickness cause methodological errors
Lena Martin

Nutrition, 2019

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights: 1. Different CT parameters influence body composition analysis 2. Contrast medium has a strong impact on the evaluation of body composition 3.Slice Thickness influences evaluation of body composition. 4. Radiation dose can be reduced without impacting body composition evaluation

View PDFchevron_right
Contrast Medium Volume Optimization in Abdominal CT on Basis of Lean Body Weight
Liliana Rodrigues

Co mputed tomography (CT) in abdominal imaging has undergone great advances in recent years. The administration of iodinated contrast media (CM) has evolved along with the evolution of the CT equipment. The most common method for calculat ion of the CM volu me in abdominal CT is based on total body weight (TBW), but in this way some patients get a lo wer dose and some other patients get a higher dose that the optimal dose they need. In the literature there are many formulas to calculate the CM volume to be administered in abdominal CT, but practice shows that the variability of enhancement is wide. The main objective of this study is to verify that the volume of intravenous CM in abdominal CT calculated on basis of lean body weight (LBW) allo ws good liver enhancement with small amplitude of variat ion around the mean, that is, with less variability in enhancement than CM volu me calcu lated on basis of TBW. In conclusion the calculation of the volume of CM on basis of LBW translates into a low variability of liver enhancement-25.50 HU. In our results 96.05% of sample belongs to a good hepatic enhancement. If we stratify sample by groups of BMI, we also verify no significative differences between slims and fat patients when analysed mean hepatic enhancement.

View PDFchevron_right
Optimization of Contrast Material Dose for Abdominal Multi-Detector Row CT: Predicting Patient Lean Body Weight by Using Preliminary Transverse CT Images
A. Guerrisi

Advances in Computed Tomography, 2014

Estimated LBW could be used to determine the contrast material dose and rate during MDCT. The aim of this study is to test the accuracy of a technique for estimation of lean body weight (LBW) from a single multi-detector row computed tomographic (MDCT) abdominal image, using a bioelectrical body composition analyzer scale as the reference standard. CT images of 21 patients with previously measured LBW (mLBW) were processed using computer-assisted, vendor-specific software (Advantage Windows 4.2; GE Healthcare, Inc). For each transverse image, a fat-fraction was automatically measured as the number of fat pixels (−200 to −50 HU) divided by the total number of pixels having an attenuation value ≥−200 HU. Estimated LBW (eLBW) of five single contiguous sections was calculated in each of three abdominal regions (upper abdomen, mid abdomen and pelvis) by multiplying TBW by (1-fat-fraction). Bland-Altman plot with limits of agreement was used to assess agreement between mLBW and eLBW. The mean mLBW for all patients was 56 kg (range, 39-75 kg). Mean differences and limits of agreement between mLBW and eLBW measurements for the upper abdomen, mid abdomen and pelvis reported were −8.9 kg (−25.6 kg, +7.5 kg), −10.6 kg (−27.7 kg, +6.4 kg), and +0.5 kg (−12.8 kg, +13.8 kg) respectively. eLBW deriving directly from a transverse CT image of the pelvis can accurately predict mLBW.

View PDFchevron_right
Computed Tomography and Magnetic Resonance Imaging in Determination of Human Body Composition Methodological and Applied Studies
John Brandberg

2009

Background: Computed tomography (CT) and magnetic resonance imaging (MRI) provide important research opportunities due to their unique capability of characterizing and quantifying tissues and organs. Ionizing radiation is a limitation using CT, and recent developments aiming to improve MRI for determination of body composition have not been validated. An area with special interest in body composition is obesity research. The prevalence of obesity is increasing and abdominal, in particular visceral, obesity is associated with the metabolic syndrome and type 2 diabetes. Aims: I. To evaluate if the radiation dose to the subject can be substantially reduced in assessment of body composition using CT while maintaining accurate measurements of adipose and muscle tissue areas and muscle tissue attenuation. II. To validate a T1 mapping whole-body MRI method, used for assessment of body composition, by comparing it with a whole-body CT method. III. To examine within-scanner reproducibility and between-scanner performance of CT measurements of adipose and muscle tissue areas and liver attenuation. IV. To study the effects of GH treatment on body composition and insulin sensitivity in postmenopausal women with abdominal obesity. Methods: I. Seventeen subjects, covering a wide range of body diameters, were examined using scan parameters chosen to reduce radiation dose as well as standard clinical scan parameters. Tissue areas and muscle CT-numbers were measured. II. Ten patients were examined both by MRI and CT to validate the T1 mapping whole-body MRI method. MRI and CT results were compared regarding tissue areas and volumes, slice by slice, and for the whole body, respectively. III. Reproducibility of the two CT scanners was investigated using duplicates from 50 patients. Between-scanner performance was evaluated by comparison of results from 40 patients. IV. The effects of GH treatment were studied in 40 women in a randomized, placebo-controlled 12-month trial. Changes in body composition and insulin sensitivity were evaluated using CT and clamp-technique, respectively. Results and conclusions: I. In assessment of body composition using CT, the radiation dose to the subject was reduced to 2-60 % of standard dose used for diagnostic purposes while maintaining accurate measurements of adipose and muscle tissue areas and muscle tissue attenuation,. The resulting effective dose for a single slice examination is <0.1mSv, a dose level associated with trivial risk. Therefore, CT can be justified for body composition assessment even in large populations or for repeated examinations. II. Compared with CT, the MRI method slightly overestimated subcutaneous adipose tissue volume and slightly underestimated visceral adipose tissue volume, but it can be considered sufficiently accurate for whole-body measurements of adipose tissue volumes. III. Within-scanner reproducibility and betweenscanner agreement were high for measurements of adipose and muscle tissue area. For measurements of liver attenuation, within-scanner reproducibility was high while a systematic bias was revealed in comparison between scanners. Therefore, comparison of CT numbers for liver from different scanners may be unreliable. IV. GH treatment of postmenopausal women with abdominal obesity reduced visceral adipose tissue and improved insulin sensitivity. CT revealed adipose tissue changes not detectable by waist-to-hip ratio, sagittal diameter, or waist circumference.

View PDFchevron_right
Body composition assessment: comparison of quantitative values between magnetic resonance imaging and computed tomography
Rolf Wyttenbach

Quantitative Imaging in Medicine and Surgery

Background: The primary objective of this study was to compare measurements of skeletal muscle index (SMI), visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) at the level of L3, on subjects who underwent computed tomography (CT) and magnetic resonance imaging (MRI) examinations within a three-month period. The secondary objective was to compare the automatic and semi-automatic quantifications of the same values for CT images. Methods: Among subjects who underwent CT and MRI at our Institution between 2011 and 2020, exclusion criteria were: presence of extensive artifacts; images not including the whole waist circumference; CT acquired with low-dose technique and lack of non-contrast images. A set of three axial images (CT, MRI T1-weighted and T2-weighted) were used to extract the following measurements with semi-automatic segmentations: SMI [calculated normalizing skeletal muscle area (SMA) by the square height], SAT, VAT. For the CT images only, the same values were also calculated by using automatic segmentation. Statistical analysis was performed comparing quantitative MRI and CT measurements by Pearson correlation analysis and by Bland-Altman agreement analysis. Results: A total of 123 patients were included. By performing linear regression analysis, CT and MRI measurements of SMI showed a high correlation (r 2 =0.81 for T1, r 2 =0.89 for T2), with a mean logarithmic difference between CT and MRI quantitative values of 0.041 for T1-weighted and 0.072 for T2-weighted images. CT and MRI measurements of SAT showed high correlation (r 2 =0.81 for T1; r 2 =0.81 for T2), with a mean logarithmic difference between CT and MRI values of 0.0174 for T1-weighted and 0.201 for T2weighted images. CT and MRI measurements of VAT showed high correlation (r 2 =0.94 for T1; r 2 =0.93 for T2), with a mean logarithmic difference of 0.040 for T1-weighted and -0.084 for T2-weighted images. The comparison of values extracted by semi-automatic and automatic segmentations were highly correlated. Conclusions: Quantification of body composition values at MRI from T1-weighted and T2-weighted images was highly correlated to same values at CT, therefore quantitative values of body composition among ^ ORCID: 0000-0002-5151-0866.

View PDFchevron_right

Related topics

  • Nutrition
  • Medicine
  • Medicine and Health Sciences
  • Medical and Health Sciences

    • Cited by

    Muscle fat infiltration in chronic kidney disease: a marker related to muscle quality, muscle strength and sarcopenia
    Alice Sabatino

    Journal of Nephrology

    Muscle fat infiltration (MFI) also known as myosteatosis refers to any deposit of lipids found in the skeletal muscle. MFI is preferably assessed by image-based methods like computed tomography (CT), magnetic resonance image (MRI) and ultrasound, normally from muscle groups located in the legs, arms and in the trunk. MFI is understood as a marker of muscle quality, where a muscle with higher fat deposition has lower contraction power and capacity to produce force per unit of muscle mass. This concept supports the hypothesis that a decrease in muscle strength is not always explained by a decrease in muscle mass, but also by other factors, including lipid deposition in the muscle. In the general population, MFI is associated with older age, physical inactivity and with insulin resistance and inflammation. In chronic kidney disease (CKD), MFI has been associated with a decrease in muscle strength and impaired muscle quality as well as with metabolic abnormalities, cardiovascular diseas...

    View PDFchevron_right
    580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved