Anatomic measurement accuracy: CT parameters and 3D rendering effects

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Anatomic measurement accuracy: CT parameters and 3D rendering effects Brian J Whyms a, E Michael Schimek a, Houri K Vorperian a, Lindell R Gentry b, and Edward T Bersu c University of Wisconsin-Madison a Waisman Center, Vocal Tract Development Lab b UW Hospital and Clinics, Radiology Department c Department of Neuroscience

INTRODUCTION Measurements from 3D-CT rendering are used in research and clinical management (treatment planning, patient monitoring, oncology, and diagnostic aid/efficiency) Measurement accuracy has been documented for linear measurements, but not volume or surface area How do image acquisition parameters affect measurements?

Purpose: Confirm linear measurement accuracy Investigate accuracy of volume and surface area Assess effect of 3D-CT image resolution on measurements Evaluate effect of 3D-CT rendering techniques on measurements

METHODS Scanned: 3 human mandibles a phantom object Phantom Object Prism

Independent Variables: CT Scanner Parameters: 1. Reconstruction Algorithm (Boneplus, Soft, Standard) 2. Slice Thickness (1.25mm, 2.5mm) 3. FOV (16x16cm, 18x18cm, 29.9x29.9cm) 3D Rendering Techniques: (Next Slide)

3D Rendering Techniques: 1. Volume Render Restricting Density Surface-Shaded Display: 2. Automatically Segmented & 3. Manually Defined g

Linear Measurements (8 linear, 2 angular) were based on landmarks common to published literature Volume & Regional Surface Area Measurements Measurements: Computer models vs. Anatomic Truth

RESULTS: 1. Linear 2. Volume 3. Surface Area

RESULTS: Linear Measurements

RESULTS: Linear Measurements Alg. Linear measurements from 3D-CT are accurate Linear accuracy is not sensitive to the CT scanning parameters or rendering techniques

RESULTS: Volume Measurements Alg. Only 1.25mm Slice Thickness for the Mandibles produced accurate group of volumes HOWEVER, all but one of the phantom object parameters produced accurate volumes Therefore, we looked further into the effect of slice thickness

RESULTS: Volume Measurements Algorithm 1. Models from 1.25mm original data = (7/9) groups within 0.05 ARE and remaining two approach threshold. 2. Models of 2.5mm slice thickness = NO groups achieve 0.05 ARE.

RESULTS: Volume Measurements (*) = group not statistically different from anatomic truth Increase slice thickness = groups of measurements stray farther from anatomic truth

To Summarize: Volume measurement accuracy is optimal from thin slices (1.25mm) Volume measurements from thin slices are not sensitive to CT scanning parameters or 3D rendering techniques THIN (1.25mm) vs. THICK (2.5mm)

Surface Area Measurements Separated by Rendering Mode Algorithm Surface Area (square cm) 9 8 7 6 5 4 3 Volume Render Mand1 Mand2 Mand3 Auto ROI Manual ROI Volume Render Auto ROI Manual ROI Volume Render Auto ROI Manual ROI Volume Render Auto ROI Manual ROI Alg.

Surface area measurements are not accurate All measurements were 25-45% inflated Volume Render = Slice Summation Surface ROI= Stair-Stepping

CONCLUSIONS 1. Linear Measurements: ACCURATE NOT sensitive to scanning parameters or rendering techniques 2. Volume : THIN slices MOST ACCURATE NOT sensitive to scanning parameters or rendering techniques 3. Surface Area: NOT ACCURATE Inflated Measurements

Potential applications : _ Characterization and grading of bone fractures Orthopedic treatment and Surgical implants 3D-CT alongside 2D images helps increase rate of successful diagnoses Vocal Tract Development Lab subject scans can be used for researching: Speech production Biological basis of development Developmental Disorders

References: 1. Guitton, T. G., H. J. van der Werf, et al. (2010). "Quantitative three-dimensional computed tomography measurement of radial head fractures." J Shoulder Elbow Surg 19(7): 973-977. 2. Ravenel, J. G., W. M. Leue, et al. (2008). "Pulmonary nodule volume: effects of reconstruction parameters on automated measurements--a phantom study." Radiology 247(2): 400-408. 3. Ball, G., D. Woodside, et al. (2011). "Relationship between cervical vertebral maturation and mandibular growth." Am J Orthod Dentofacial Orthop 139(5): e455-461. 4. El-Zanaty, H. M., A. R. El-Beialy, et al. (2010). "Three-dimensional dental measurements: An alternative to plaster models." Am J Orthod Dentofacial Orthop 137(2): 259-265. 5. Alkadhi, H., S. Wildermuth, et al. (2004). "Accuracy and time efficiency for the detection of thoracic cage fractures: volume rendering compared with transverse computed tomography images." J Comput Assist Tomogr 28(3): 378-385. 6. Ringl, H., R. Schernthaner, et al. (2009). "Three-dimensional fracture visualisation of multidetector CT of the skull base in trauma patients: comparison of three reconstruction algorithms." Eur Radiol 19(10): 2416-2424.

QUESTIONS? Process of Volume Render Segmentation: Acknowledgements This work was supported by National Institute on Deafness and Other Communication Disorders NIH-NIDCD Grant R01 DC6282 (MRI and CT Studies of the Developing Vocal Tract)