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Hounsfield Unit

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Last Update: March 16, 2021.

Definition/Introduction

The Hounsfield unit (HU) is a relative quantitative measurement of radio density used by radiologists in the interpretation of computed tomography (CT) images. The absorption/attenuation coefficient of radiation within a tissue is used during CT reconstruction to produce a grayscale image. The physical density of tissue is proportional to the absorption/attenuation of the X-ray beam. The Hounsfield unit, also referred to as the CT unit, is then calculated based on a linear transformation of the baseline linear attenuation coefficient of the X-ray beam, where distilled water (at standard temperature and pressure) is arbitrarily defined to be zero Hounsfield Units and air defined as -1000 HU. The upper limits can reach up to 1000 for bones, 2000 for dense bones like the cochlea, and more than 3000 for metals like steel or silver. The linear transformation produces a Hounsfield scale that displays as gray tones. More dense tissue, with greater X-ray beam absorption, has positive values and appears bright; less dense tissue, with less X-ray beam absorption, has negative values and appears dark. The Hounsfield unit was named after Sir Godfrey Hounsfield, recipient of the Nobel Prize in Physiology or Medicine in 1979, for his part in the invention of CT, as it had immediate recognition as a revolutionary diagnostic instrument.[1][2][3]

Issues of Concern

This linear transformation of the original linear attenuation makes the Hounsfield scale a relative scale, rather than absolute. Different X-ray beam energies will result in different tissue absorption and hence, different HUs. Early studies showed the HU to be dependent on the various CT parameters.[4] The type of reconstructing algorithm, design of the CT, and X-ray kilovoltage, were the most important factors identified. These factors need standardization to help make the HU a reliable diagnostic measurement tool.[5]

CT artifacts can affect Hounsfield unit measurements.  One of the most encountered CT artifacts, beam-hardening artifact, affects the measurement of radiodensity. Polychromatic energies comprise the conventional CT X-ray. High-density tissue selectively absorbs X-rays of lower energy, thus altering the X-ray beam. This absorption, in turn, can alter the X-ray beam absorption in the center of high-density tissue and results in a change in the HU, leading to falsely lowered HU measurements and appears less dense, or darker, on CT images. Modern CT machines can correct this artifact in the reconstruction process.

Continued advancements in CT as a diagnostic tool have led to different CT designs. Different CT designs, in turn, can alter the HU. For example, cone-beam computed tomography (CBCT), used mainly in dentistry, cannot show the actual HU similar to conventional CT but does show a strong correlation.[6] Dual-energy CT (DECT) uses X-ray beams of two different energies for deriving additional information to produce both anatomic and functional information.[7] Given the dependence of the HU on energy, the use of HU as a quantitative diagnostic parameter is limited in DECT.  The same is true for reduced energy protocols used in CT imaging today.[8]

Lastly, one should remember that the visualization of images on a CT has as its basis differences in tissue density and radiodensity. In the case of foreign body evaluation on CT imaging, if the foreign body has a similar physical density to the tissue that it is embedded in, it will have similar HU and will be hard to detect by visually CT. The radiologic evaluation of a wooden foreign body is complicated, given the varied appearance of wood and changes within the wood. There is documentation showing that a wooden foreign body demonstrates increasing HU over time.[9]

HU for fat is around -50, cerebrospinal fluid +15, white matter +25, grey matter +40, and blood +30-45.

Clinical Significance

The use of the HU to measure tissue density has aided radiologists in the interpretation of images and diagnosis of disease. Its use is identified in different specialties of medicine.

The following are some of the uses of HU that have been identified in the literature:

  1. In diagnosing fatty liver[10]
  2. As a quantitative measurement in the evaluation of solitary pulmonary nodules and thyroid nodules[11]
  3. To determine bone mineral density[12][13] 
  4. To estimate bone quality before spinal instrumentation,[14] predicting pedicle screw loosening[15] and in degenerative lumbar scoliosis[16]
  5. HU of paraspinal muscles have helped identify patients at risk for sarcopenia[17]
  6. In the characterization of bile duct lesions[18]
  7. Predictor of growth in meningiomas[19]
  8. Predictor of spontaneous expulsion of lower ureteric stone[20] and in differentiating pyonephrosis from hydronephrosis[21]
  9. HU of interpeduncular cistern hematomas can predict symptomatic vasospasms[22]
  10. In the differential diagnosis of odontogenic cysts[23]
  11. In predicting outcomes of intracerebral hemorrhage by measuring the HU in the perihematomal edema[24]
  12. In identifying tandem occlusion in acute ischemic stroke[25]
  13. In the diagnosis of acute venous thrombosis in the pediatric population[26]

With continued further advancements in technology, researchers are studying semi-automated measurements of tissue to aid the radiologist in the evaluation and diagnosis of disease. Semi-automated HU measurements of solitary pulmonary nodules have shown to be an accurate approach to determining malignant from benign solitary pulmonary nodules.[27] Other semi-automated measurements of HU are likely to aid in the evaluation of CT images and become part of the landscape of clinical practice in the near future.

Nursing, Allied Health, and Interprofessional Team Interventions

Proper communication should be there between the radiologist, surgeon, and physician with regard to imaging. Subtle findings may need the radiologist to measure the Housfiled unit of the lesion and let the medical or surgical team know if indicated.

Continuing Education / Review Questions

References

1.
Raju TN. The Nobel chronicles. 1979: Allan MacLeod Cormack (b 1924); and Sir Godfrey Newbold Hounsfield (b 1919). Lancet. 1999 Nov 06;354(9190):1653. [PubMed: 10560712]
2.
Mahesh M. Search for isotropic resolution in CT from conventional through multiple-row detector. Radiographics. 2002 Jul-Aug;22(4):949-62. [PubMed: 12110725]
3.
Hounsfield GN. Computed medical imaging. Nobel lecture, Decemberr 8, 1979. J Comput Assist Tomogr. 1980 Oct;4(5):665-74. [PubMed: 6997341]
4.
Levi C, Gray JE, McCullough EC, Hattery RR. The unreliability of CT numbers as absolute values. AJR Am J Roentgenol. 1982 Sep;139(3):443-7. [PubMed: 6981306]
5.
Zerhouni EA, Spivey JF, Morgan RH, Leo FP, Stitik FP, Siegelman SS. Factors influencing quantitative CT measurements of solitary pulmonary nodules. J Comput Assist Tomogr. 1982 Dec;6(6):1075-87. [PubMed: 7174924]
6.
Razi T, Niknami M, Alavi Ghazani F. Relationship between Hounsfield Unit in CT Scan and Gray Scale in CBCT. J Dent Res Dent Clin Dent Prospects. 2014 Spring;8(2):107-10. [PMC free article: PMC4120902] [PubMed: 25093055]
7.
Aran S, Daftari Besheli L, Besheli LD, Karcaaltincaba M, Gupta R, Flores EJ, Abujudeh HH. Applications of dual-energy CT in emergency radiology. AJR Am J Roentgenol. 2014 Apr;202(4):W314-24. [PubMed: 24660729]
8.
Bolus D, Morgan D, Berland L. Effective use of the Hounsfield unit in the age of variable energy CT. Abdom Radiol (NY). 2017 Mar;42(3):766-771. [PubMed: 28132073]
9.
Natung T, Shullai W, Lynser D, Tripathy T. A challenging case of a large intraorbital foreign body perforating the nasal septum in a child. Indian J Ophthalmol. 2018 Oct;66(10):1511-1513. [PMC free article: PMC6173013] [PubMed: 30249858]
10.
Zeb I, Li D, Nasir K, Katz R, Larijani VN, Budoff MJ. Computed tomography scans in the evaluation of fatty liver disease in a population based study: the multi-ethnic study of atherosclerosis. Acad Radiol. 2012 Jul;19(7):811-8. [PMC free article: PMC3377794] [PubMed: 22521729]
11.
Wei PY, Jiang ND, Xiang JJ, Xu CK, Ding JW, Wang HB, Luo DC, Han ZJ. Hounsfield Unit Values in ACR TI-RADS 4-5 Thyroid Nodules with Coarse Calcifications: An Important Imaging Feature Helpful for Diagnosis. Cancer Manag Res. 2020;12:2711-2717. [PMC free article: PMC7184120] [PubMed: 32368148]
12.
Scheyerer MJ, Ullrich B, Osterhoff G, Spiegl UA, Schnake KJ., Arbeitsgruppe Osteoporotische Frakturen der Sektion Wirbelsäule der Deutschen Gesellschaft für Orthopädie und Unfallchirurgie. [Hounsfield units as a measure of bone density-applications in spine surgery]. Unfallchirurg. 2019 Aug;122(8):654-661. [PubMed: 31053924]
13.
Narayanan A, Cai A, Xi Y, Maalouf NM, Rubin C, Chhabra A. CT bone density analysis of low-impact proximal femur fractures using Hounsfield units. Clin Imaging. 2019 Sep - Oct;57:15-20. [PubMed: 31102777]
14.
Zaidi Q, Danisa OA, Cheng W. Measurement Techniques and Utility of Hounsfield Unit Values for Assessment of Bone Quality Prior to Spinal Instrumentation: A Review of Current Literature. Spine (Phila Pa 1976). 2019 Feb 15;44(4):E239-E244. [PubMed: 30063528]
15.
Zou D, Muheremu A, Sun Z, Zhong W, Jiang S, Li W. Computed tomography Hounsfield unit-based prediction of pedicle screw loosening after surgery for degenerative lumbar spine disease. J Neurosurg Spine. 2020 Jan 03;:1-6. [PubMed: 31899883]
16.
Wang H, Zou D, Sun Z, Wang L, Ding W, Li W. Hounsfield Unit for Assessing Vertebral Bone Quality and Asymmetrical Vertebral Degeneration in Degenerative Lumbar Scoliosis. Spine (Phila Pa 1976). 2020 Nov 15;45(22):1559-1566. [PubMed: 32756284]
17.
Barnard R, Tan J, Roller B, Chiles C, Weaver AA, Boutin RD, Kritchevsky SB, Lenchik L. Machine Learning for Automatic Paraspinous Muscle Area and Attenuation Measures on Low-Dose Chest CT Scans. Acad Radiol. 2019 Dec;26(12):1686-1694. [PMC free article: PMC6878160] [PubMed: 31326311]
18.
Batur A, Kerimoglu U, Ataseven H. Hounsfield unit density in the characterisation of bile duct lesions. Pol J Radiol. 2019;84:e397-e401. [PMC free article: PMC6964322] [PubMed: 31969956]
19.
Nakasu S, Onishi T, Kitahara S, Oowaki H, Matsumura KI. CT Hounsfield Unit Is a Good Predictor of Growth in Meningiomas. Neurol Med Chir (Tokyo). 2019 Feb 15;59(2):54-62. [PMC free article: PMC6375817] [PubMed: 30686812]
20.
Bokka S, Jain A. Hounsfield unit and its correlation with spontaneous expulsion of lower ureteric stone. Ther Adv Urol. 2019 Jan-Dec;11:1756287219887661. [PMC free article: PMC6891007] [PubMed: 31832102]
21.
Boeri L, Fulgheri I, Palmisano F, Lievore E, Lorusso V, Ripa F, D'Amico M, Spinelli MG, Salonia A, Carrafiello G, Montanari E. Hounsfield unit attenuation value can differentiate pyonephrosis from hydronephrosis and predict septic complications in patients with obstructive uropathy. Sci Rep. 2020 Oct 29;10(1):18546. [PMC free article: PMC7596071] [PubMed: 33122830]
22.
Ishihara H, Oka F, Kawano R, Shinoyama M, Nishimoto T, Kudomi S, Suzuki M. Hounsfield Unit Value of Interpeduncular Cistern Hematomas Can Predict Symptomatic Vasospasm. Stroke. 2020 Jan;51(1):143-148. [PubMed: 31694506]
23.
Uehara K, Hisatomi M, Munhoz L, Kawazu T, Yanagi Y, Okada S, Takeshita Y, Saito EA, Asaumi J. Assessment of Hounsfield unit in the differential diagnosis of odontogenic cysts. Dentomaxillofac Radiol. 2021 Feb 01;50(2):20200188. [PMC free article: PMC7860949] [PubMed: 32783633]
24.
Huan R, Li Y, Tan J, Tang J, Huang N, Cheng Y. The Hounsfield Unit of Perihematomal Edema Is Associated With Poor Clinical Outcomes in Intracerebral Hemorrhage. World Neurosurg. 2021 Feb;146:e829-e836. [PubMed: 33189917]
25.
Mühl-Benninghaus R, Dressler J, Haußmann A, Simgen A, Reith W, Yilmaz U. Utility of Hounsfield unit in the diagnosis of tandem occlusion in acute ischemic stroke. Neurol Sci. 2020 Oct 14; [PMC free article: PMC8159780] [PubMed: 33052575]
26.
de la Vega Muns G, Quencer R, Ezuddin NS, Saigal G. Utility of Hounsfield unit and hematocrit values in the diagnosis of acute venous sinus thrombosis in unenhanced brain CTs in the pediatric population. Pediatr Radiol. 2019 Feb;49(2):234-239. [PubMed: 30327829]
27.
Choi Y, Gil BM, Chung MH, Yoo WJ, Jung NY, Kim YH, Kwon SS, Kim J. Comparing attenuations of malignant and benign solitary pulmonary nodule using semi-automated region of interest selection on contrast-enhanced CT. J Thorac Dis. 2019 Jun;11(6):2392-2401. [PMC free article: PMC6626776] [PubMed: 31372276]
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Bookshelf ID: NBK547721PMID: 31613501

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