This is the developer site of the OpenKnee project. The development efforts are organized by [http://www.lerner.ccf.org/bme/erdemir/ Ahmet Erdemir] and [http://www.lerner.ccf.org/bme/cobi/ CoBi Core] of the Cleveland Clinic. This study branched from a current NIH funded study on multiscale modeling and simulation of the knee joint, [https://simtk.org/home/j2c J2C]. If you are a new member ([:InstructionsForProjectSite#Team:How do I become a member?]), please read the following documentation to familiarize yourself with operational details:

For recent wiki activity, check RecentChanges.

Goals

Roadmap

Releases relies on the following numbering scheme:

version.major.minor.revision

version: numbering based on goals of the roadmap
major: implementation of a new feaure
minor: improvement of a feature or a bug fix
revision: revision number of the subversion repository on which the release is based on

Version 1.0

Imaging data; DICOM files
Geometry; STEP or IGES files
Mesh; text based input deck
Literature based material properties
Frictionless contact between tissue structures
Loading and boundary conditions representative of passive flexion
Output at desired time increments

Long Term

Release Notes

Specifications

Geometry

Mesh

Material Properties

Bone

Cartilage

Ligament

Meniscus

Interactions

Loading & Boundary Conditions

Output

Solver

Software

For finite element analysis [http://mrl.sci.utah.edu/software/febio FEBio], a freely accessible package, will be used. This software is a product of significant efforts by Jeff Weiss and his group from the [http://mrl.sci.utah.edu/ Musculoskeletal Research Laboratories] at the University of Utah. Current version used in this project is FEBio 1.2, which can be downloaded from their [http://mrl.sci.utah.edu/software/febio site].

Settings

Data

Data for model development efforts are courtesy of [http://www.lerner.ccf.org/bme/bogert/ van den Bogert Laboratory] at the Cleveland Clinic. The information was collected is part of doctoral work conducted by Bhushan Borotikar.

Specimen

NDRI ID	08956 (Specimen acquired from National Disease Research Exchange)
MRMTC#	022508-03 (Specimen tested in Musculoskeletal Robotics and Mechanical Testing Facility at the Cleveland Clinic)
Side	Right
Donor Age	70 years
Donor Estimated Body Weight	170 lbs (77.1 kg)
Donor Heigt	5'6" (1.68 m)
Donor Gender	Female
Donor Cause of Death	Pneumonia/Cancer

Imaging

Source: https://simtk.org/websvn/wsvn/openknee/dat/mri/

The knee specimen was imaged at the Biomechanics laboratory of the Cleveland Clinic using a 1.0T (Tesla) extremity MRI scanner (Orthone, ONI Medical Systems Inc, Wilmington MA). The scanner has the capability to scan upper and lower extremities of up to 180mm diameter. A scanning protocol that gave a good contrast for articular cartilage and ligaments in the same scan were used [#Borotikar09 Borotikar (2009)]. The specifics of this protocol are detailed in following:

Setting for Magneric Resonance Imaging
Scan Parameters
	Sagittal	Axial	Coronal
Pulse sequence	GE3D	GE3D	GE3D
TR	30	30	30
TE	8.9	8.9	8.9
Frequency	260	260	260
Phase	192	192	192
FOV	150	150	150
BW	20	20	20
Echo train	1	1	1
NEX	1	1	1
Flip angle	35	35	35
Time	5.03	3.19	3.30
Scan Options
	Sagittal	Axial	Coronal
Graphics SL	Y	Y	Y
RF spoiling	Y	Y	Y
Fat suppression	N	N	N
Minimum TE	Y	Y	Y
Inversion recovery	N	N	N
Partial data	N	N	N
No phase wrap	Y	Y	Y
Spatial saturation	N	N	N
Flow comp	N	N	N
Magnetic transfer	N	N	N
Prescan Parameters
	Sagittal	Axial	Coronal
Prescan	Auto	Auto	Auto
Center freq.	Peak	Peak	Peak
Slice Parameters
	Sagittal	Axial	Coronal
Number of slices	70	45	60
Slice thickness (mm)	1.5	1.5	1.5
Gap (mm)	0	0	0
Range (mm)	105	67.5	90

The knee was kept in full extension position. Imaging technique utilizes 3D spoiled gradient echo sequence with fat suppression, TR = 30, TE = 6.7, Flip Angle = 200, Field of View (FOV) = 150mm X 150mm, Slice Thickness = 1.5mm. Scans in three anatomical planes, axial, sagittal, and coronal, were conducted. Total scanning time was approximately 18 minutes. Selecting these specific sequence parameters produced images that highlighted articular cartilage such that it could be easily discriminated from surrounding bone and tissue. The protocols and the image set reflect partial data from the doctoral work of [#Borotikar09 Borotikar (2009)].

Anchor(Borotikar09) Borotikar, Bhushan, Subject specific computational models of the knee to predict anterior cruciate ligament injury, Doctoral Dissertation, Cleveland State University, December 2009.