Background
Software package | Language | Operating system | License | Models included | Fitting | Other MRI-relevant features | Input/output | Location |
---|---|---|---|---|---|---|---|---|
dcemriS4
| R | Linux, Windows, Mac OS | BSD | - Standard Kelty- Single-compartment model- Extended Kety | - Non-linear least squares- Bayesian estimation | - motion correction and co-registration - B1 mapping - T1 mapping - AIF fitting - DWI fitting - Pixel processing - Job report for later retreival - Access to R functions | - DICOM - NIFTI - Raw data | |
dcetool
| C, Plugin for ClearCanvas | Windows | Proprietary | - Tofts - Adiabatic tissue homogeniety - Thorwarth- Extended Tofts - Semi-quantitative metrics (Slope/AUC) | Proprietary | - DICOM - Raw data | ||
PMI
| IDL | Windows | GNU GPL | - Uptake models - Steady-state - Patlak - Model-free deconvolution - Tofts- Extended Tofts - 2CXM - 2C filtration model for kidney - Dual-inlet models for Liver - Semi-quantitative metrics (Slope/Signal enhancement) | - Non-linear Least squares - Truncated singular value decomposition | - ROI and pixel processing - AIF/time series visualization/editing - Access to IDL functions | - DICOM - Raw data | |
UMMPerfusion
| C, OsiriX plugin | Mac OS | BSD | - Model free deconvolution | - Truncated singular value decomposition | - ROI and pixel processing - Job report for later retreival - AIF/time series visualization/editing | - DICOM | |
Pydcemri
| Python | Linux, Windows, Mac OS | GNU GPL | - Tofts - Extended Tofts | - Non-linear Least squares | - Access to python functions | - Raw data | |
DCEMRI.jl
| Julia | Linux, Windows, Mac OS | MIT | - Tofts - Extended Tofts - Plasma only | - Non-linear Least squares | - ROI and pixel processing - T1 mapping - Batch processing - Access to Julia functions | - Matlab data | |
DCE@UrLAB
| IDL | Windows | BSD | - Tofts - Hoffmann - Larsson - Fast exchange limit reference region | - Non-linear Least squares | - ROI and pixel processing - Access to IDL functions | - DICOM - Bruker - Raw data | |
DATforDCEMRI
| R | Creative Commons | - Tofts - Semi-quantitative metrics (AUC, MRT - mean residence time) | - Numerical deconvolution | - Pixel processing - Access to R functions | - R readable data formats | ||
TOPPCAT
| Javascript, ImageJ plugin | Linux, Windows, Mac OS | BSD | - Patlak | - Non-linear Least squares | - T1 mapping - Access to ImageJ functions | - ImageJ readable data formats | |
Jim
| Java | Linux, Windows, Mac OS | Proprietary | - Tofts - Extended Tofts - One compartment - Fermi - 2CXM - Semi-quantitative metrics (AUC) | Proprietary | - ROI and pixel processing | - DICOM - Analyze - Bruker - Commercial formats - Raw data | |
PermGUI
| Matlab | Windows | Creative Commons | - Patlak | - T1 mapping - ROI and pixel processing | - DICOM - Analyze - NIFTI | ||
BioMap
| IDL | Linux, Windows, Mac OS | Proprietary | - Extended Tofts | - Non-linear Least squares | - T1 fitting | - DICOM - Analyze - TIF/PNG | |
ROCKETSHIP
| Matlab | Linux, Windows, Mac OS | GNU GPL | - Tofts - Extended Tofts - Fast exchange regime (FXR) - 2CXM - Tissue uptake - Nested-model selection - Patlak - Semi-quantitative metrics (AUC) | - Non-linear Least squares | - T1 mapping - ROI and pixel processing - AIF fitting/import - DWI fitting - Job report for later retreival - AIF/time series visualization/editing - Batch processing - Access to Matlab functions - Model fit comparisons with statistical metrics - Drift correction | - DICOM - Analyze - NIFTI - Raw data - Matlab data |
Implementation
Architecture design and GUI description
Estimation of model parameters
Inputs and outputs
Results
Validation of ROCKETSHIP software with simulation
Precision and accuracy of individual kinetic model fitting
Generating model
|
Fitting model
|
Acquistion duration (min)
|
Time resolution (s)
|
SNR
|
Ktrans (1/min)
|
ve
|
vp
|
Fp (1/min)
|
τi (s)
|
---|---|---|---|---|---|---|---|---|---|
Patlak
|
Patlak
| 10 | 0.5, 6 | 5, 100 | 0.01, 0.02, 0.05, 0.1, 0.2, 0.35 | N/A | 0.001, 0.005 0.01 0.02 0.05 0.1 | N/A | N/A |
Tofts
|
Tofts
| 10 | 0.5, 6 | 5, 100 | 0.01, 0.02, 0.05, 0.1, 0.2, 0.35 | 0.01, 0.02, 0.05, 0.1, 0.2, 0.5 | N/A | N/A | N/A |
Ex-Tofts
|
Ex-Tofts
| 10 | 0.5, 6 | 5, 100 | 0.01, 0.02, 0.05, 0.1, 0.2, 0.35 | 0.01, 0.02, 0.05, 0.1, 0.2, 0.5 | 0.001, 0.005 0.01 0.02 0.05 0.1 | N/A | N/A |
2CXM
|
2CXM
| 10 | 0.5, 6 | 5, 100 | 0.01, 0.02, 0.05, 0.1, 0.2, 0.35 | 0.01, 0.02, 0.05, 0.1, 0.2, 0.5 | 0.001, 0.005 0.01 0.02 0.05 0.1 | 0.5, 1, 5 | N/A |
Tissue uptake
|
Tissue uptake
| 10 | 0.5, 6 | 5, 100 | 0.01, 0.02, 0.05, 0.1, 0.2, 0.35 | N/A | 0.001, 0.005 0.01 0.02 0.05 0.1 | 0.5, 1, 5 | N/A |
FXR
|
FXR
| 10 | 0.5, 6 | 5, 100 | 0.01, 0.02, 0.05, 0.1, 0.2, 0.35 | 0.01, 0.02, 0.05, 0.1, 0.2, 0.5 | N/A | N/A | 0.1, 0.5, 2 |
Steady-state
|
Nested
| 10 | 0.5 | 5, 100 | N/A | N/A | 0.005, 0.1 | N/A | N/A |
Patlak
|
Nested
| 10 | 0.5 | 5, 100 | 0.01, 0.35 | N/A | 0.005, 0.1 | N/A | N/A |
Ex-Tofts
|
Nested
| 10 | 0.5 | 5, 100 | 0.01, 0.35 | 0.01, 0.1 | 0.005, 0.1 | N/A | N/A |
Generating model | Fitting model | Time resolution (s) | SNR | Dependent parameter |
vp = 0.001
|
0.005
|
0.01
|
0.02
|
0.05
|
0.1
|
---|---|---|---|---|---|---|---|---|---|---|
Patlak
|
Patlak
| 0.5 | 5 |
vp
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
0.5 | 100 |
vp
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
6 | 5 |
vp
| 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | ||
6 | 100 |
vp
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
ve = 0.01
|
0.02
|
0.05
|
0.1
|
0.2
|
0.5
| |||||
Tofts
|
Tofts
| 0.5 | 5 |
ve
| 0.38 | 0.85 | 0.98 | 0.99 | 1.00 | 1.00 |
0.5 | 100 |
ve
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
6 | 5 |
ve
| 0.08 | 0.32 | 0.76 | 0.92 | 0.96 | 0.97 | ||
6 | 100 |
ve
| 0.67 | 0.92 | 1.00 | 1.00 | 1.00 | 1.00 | ||
ve = 0.01
|
0.02
|
0.05
|
0.1
|
0.2
|
0.5
| |||||
Ex-Tofts
|
Ex-Tofts
| 0.5 | 5 |
ve
| 0.01 | 0.33 | 0.95 | 0.99 | 1.00 | 1.00 |
0.5 | 100 |
ve
| 0.92 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
6 | 5 |
ve
| −0.02 | 0.05 | 0.41 | 0.84 | 0.96 | 0.98 | ||
6 | 100 |
ve
| 0.22 | 0.48 | 0.98 | 1.00 | 1.00 | 1.00 | ||
vp = 0.001
|
0.005
|
0.01
|
0.02
|
0.05
|
0.1
| |||||
0.5 | 5 |
vp
| 0.74 | 0.73 | 0.71 | 0.68 | 0.61 | 0.51 | ||
0.5 | 100 |
vp
| 0.99 | 0.98 | 0.98 | 0.99 | 0.99 | 0.98 | ||
6 | 5 |
vp
| 0.57 | 0.58 | 0.57 | 0.56 | 0.45 | 0.41 | ||
6 | 100 |
vp
| 0.89 | 0.90 | 0.88 | 0.81 | 0.54 | 0.35 | ||
ve = 0.01
|
0.02
|
0.05
|
0.1
|
0.2
|
0.5
| |||||
2CXM
|
2CXM
| 0.5 | 5 |
ve
| −0.02 | −0.02 | 0.11 | 0.22 | 0.65 | 0.90 |
0.5 | 100 |
ve
| 0.07 | 0.42 | 0.65 | 0.76 | 0.98 | 0.99 | ||
6 | 5 |
ve
| −0.01 | −0.01 | −0.01 | 0.10 | 0.52 | 0.84 | ||
6 | 100 |
ve
| −0.03 | −0.06 | 0.04 | 0.38 | 0.84 | 0.98 | ||
vp = 0.001
|
0.005
|
0.01
|
0.02
|
0.05
|
0.1
| |||||
0.5 | 5 |
vp
| 0.18 | 0.20 | 0.23 | 0.31 | 0.43 | 0.47 | ||
0.5 | 100 |
vp
| 0.32 | 0.63 | 0.72 | 0.76 | 0.76 | 0.74 | ||
6 | 5 |
vp
| 0.20 | 0.19 | 0.21 | 0.21 | 0.28 | 0.29 | ||
6 | 100 |
vp
| 0.18 | 0.26 | 0.34 | 0.41 | 0.45 | 0.45 | ||
Fp = 0.5
|
1
|
5
| ||||||||
0.5 | 5 |
Fp
| 0.21 | 0.30 | 0.40 | |||||
0.5 | 100 |
Fp
| 0.46 | 0.69 | 0.81 | |||||
6 | 5 |
Fp
| 0.20 | 0.22 | 0.27 | |||||
6 | 100 |
Fp
| 0.26 | 0.35 | 0.43 | |||||
vp = 0.001
|
0.005
|
0.01
|
0.02
|
0.05
|
0.1
| |||||
vp = 0.001
|
0.005
|
0.01
|
0.02
|
0.05
|
0.1
| |||||
0.5 | 5 |
vp
| 0.98 | 1.00 | 0.98 | 1.00 | 1.00 | 1.00 | ||
0.5 | 100 |
vp
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
6 | 5 |
vp
| 0.88 | 0.88 | 0.88 | 0.91 | 0.98 | 0.99 | ||
6 | 100 |
vp
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Tissue uptake
|
Tissue uptake
| 0.5 | 5 |
vp
| 0.98 | 1.00 | 0.98 | 1.00 | 1.00 | 1.00 |
0.5 | 100 |
vp
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
6 | 5 |
vp
| 0.88 | 0.88 | 0.88 | 0.91 | 0.98 | 0.99 | ||
6 | 100 |
vp
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Fp = 0.5
|
1
|
5
| ||||||||
0.5 | 5 |
Fp
| 1.00 | 0.98 | 1.00 | |||||
0.5 | 100 |
Fp
| 1.00 | 1.00 | 1.00 | |||||
6 | 5 |
Fp
| 0.82 | 0.97 | 0.98 | |||||
6 | 100 |
Fp
| 1.00 | 1.00 | ||||||
ve = 0.01
|
0.02
|
0.05
|
0.1
|
0.2
|
0.5
| |||||
ve = 0.01
|
0.02
|
0.05
|
0.1
|
0.2
|
0.5
| |||||
0.5 | 5 |
ve
| −0.01 | 0.02 | 0.08 | 0.14 | 0.39 | 0.95 | ||
0.5 | 100 |
ve
| 0.07 | 0.31 | 0.97 | 1.00 | 1.00 | 1.00 | ||
6 | 5 |
ve
| 0.01 | −0.01 | 0.03 | 0.06 | 0.12 | 0.38 | ||
6 | 100 |
ve
| 0.05 | 0.14 | 0.71 | 0.96 | 0.99 | 1.00 | ||
τi = 0.1
|
0.5
|
2
| ||||||||
0.5 | 5 |
τi
| 0.11 | 0.14 | 0.14 | |||||
0.5 | 100 |
τi
| 0.56 | 0.59 | 0.56 | |||||
6 | 5 |
τi
| 0.06 | 0.07 | 0.07 | |||||
6 | 100 |
τi
| 0.54 | 0.61 | 0.52 |
Generating model | Fitting model | Time resolution (s) | SNR | Dependent parameter |
Ktrans = 0.01
|
0.02
|
0.05
|
0.1
|
0.2
|
0.35
|
---|---|---|---|---|---|---|---|---|---|---|
Patlak
|
Patlak
| 0.5 | 5 |
Ktrans
| 0.99 | 0.99 | 0.99 | 0.99 | 1.00 | 0.99 |
0.5 | 100 |
Ktrans
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
6 | 5 |
Ktrans
| 0.95 | 0.95 | 0.95 | 0.95 | 0.94 | 0.95 | ||
6 | 100 |
Ktrans
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Ktrans = 0.01
|
0.02
|
0.05
|
0.1
|
0.2
|
0.35
| |||||
Ex-Tofts
|
Ex-Tofts
| 0.5 | 5 |
Ktrans
| 0.99 | 0.99 | 0.99 | 0.99 | 0.98 | 0.98 |
0.5 | 100 |
Ktrans
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
6 | 5 |
Ktrans
| 0.95 | 0.95 | 0.94 | 0.93 | 0.91 | 0.88 | ||
6 | 100 |
Ktrans
| 1.00 | 1.00 | 1.00 | 1.00 | 0.95 | 0.89 | ||
ve = 0.01
|
0.02
|
0.05
|
0.1
|
0.2
|
0.5
| |||||
0.5 | 5 |
ve
| 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.99 | ||
0.5 | 100 |
ve
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
6 | 5 |
ve
| 0.92 | 0.90 | 0.89 | 0.93 | 0.95 | 0.94 | ||
6 | 100 |
ve
| 0.92 | 0.92 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Ktrans = 0.01
|
0.02
|
0.05
|
0.1
|
0.2
|
0.35
| |||||
2CXM
|
2CXM
| 0.5 | 5 |
Ktrans
| 0.79 | 0.85 | 0.51 | 0.26 | 0.10 | 0.06 |
0.5 | 100 |
Ktrans
| 1.00 | 1.00 | 0.95 | 0.96 | 0.81 | 0.41 | ||
6 | 5 |
Ktrans
| 0.02 | 0.07 | 0.37 | 0.16 | 0.07 | 0.05 | ||
6 | 100 |
Ktrans
| 0.96 | 0.93 | 0.90 | 0.73 | 0.38 | 0.09 | ||
ve = 0.01
|
0.02
|
0.05
|
0.1
|
0.2
|
0.5
| |||||
0.5 | 5 |
ve
| 0.95 | 0.90 | 0.65 | 0.31 | 0.15 | 0.02 | ||
0.5 | 100 |
ve
| 0.99 | 0.97 | 0.89 | 0.75 | 0.82 | 0.57 | ||
6 | 5 |
ve
| 0.44 | 0.56 | 0.35 | 0.11 | 0.02 | −0.03 | ||
6 | 100 |
ve
| 0.97 | 0.93 | 0.75 | 0.51 | 0.34 | 0.13 | ||
Fp = 0.5
|
1
|
5
| ||||||||
0.5 | 5 |
Fp
| 0.22 | 0.28 | 0.31 | |||||
0.5 | 100 |
Fp
| 0.64 | 0.88 | 0.95 | |||||
6 | 5 |
Fp
| 0.09 | 0.09 | 0.03 | |||||
6 | 100 |
Fp
| 0.39 | 0.56 | 0.54 | |||||
Ktrans = 0.01
|
0.02
|
0.05
|
0.1
|
0.2
|
0.35
| |||||
Tissue uptake
|
Tissue uptake
| 0.5 | 5 |
Ktrans
| 0.95 | 0.78 | 0.18 | 0.03 | −0.02 | −0.03 |
0.5 | 100 |
Ktrans
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.48 | ||
6 | 5 |
Ktrans
| 0.33 | 0.39 | 0.31 | 0.17 | 0.02 | 0.01 | ||
6 | 100 |
Ktrans
| 0.74 | 0.83 | 0.79 | 0.80 | 0.77 | 0.69 | ||
Fp = 0.5
|
1
|
5
| ||||||||
0.5 | 5 |
Fp
| -0.01 | -0.01 | 0.90 | |||||
0.5 | 100 |
Fp
| 0.67 | 1.00 | 0.99 | |||||
6 | 5 |
Fp
| 0.03 | 0.20 | 0.07 | |||||
6 | 100 |
Fp
| 0.95 | 0.97 | 0.44 |
CCC for ve (simulated) vs. ve (fitted) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Generating model | Fitting model | Time resolution (s) | SNR | Dependent parameter |
Ktrans = 0.01
|
0.02
|
0.05
|
0.1
|
0.2
|
0.35
|
Tofts
|
Tofts
| 0.5 | 5 |
Ktrans
| 0.71 | 0.96 | 1.00 | 1.00 | 0.94 | 0.97 |
0.5 | 100 |
Ktrans
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
6 | 5 |
Ktrans
| 0.33 | 0.52 | 0.68 | 0.63 | 0.65 | 0.68 | ||
6 | 100 |
Ktrans
| 0.95 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Ktrans = 0.01
|
0.02
|
0.05
|
0.1
|
0.2
|
0.35
| |||||
Ex-Tofts
|
Ex-Tofts
| 0.5 | 5 |
Ktrans
| 0.67 | 0.86 | 0.87 | 0.80 | 0.66 | 0.54 |
0.5 | 100 |
Ktrans
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
6 | 5 |
Ktrans
| 0.22 | 0.40 | 0.48 | 0.47 | 0.35 | 0.35 | ||
6 | 100 |
Ktrans
| 0.95 | 1.00 | 1.00 | 1.00 | 0.93 | 0.74 | ||
vp = 0.001
|
0.02
|
0.05
|
0.1
|
0.2
|
0.5
| |||||
0.5 | 5 |
vp
| 0.74 | 0.73 | 0.69 | 0.71 | 0.73 | 0.70 | ||
0.5 | 100 |
vp
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
6 | 5 |
vp
| 0.38 | 0.38 | 0.36 | 0.36 | 0.32 | 0.32 | ||
6 | 100 |
vp
| 0.92 | 0.94 | 0.93 | 0.93 | 0.93 | 0.94 | ||
Ktrans = 0.01
|
0.02
|
0.05
|
0.1
|
0.2
|
0.35
| |||||
2CXM
|
2CXM
| 0.5 | 5 |
Ktrans
| 0.51 | 0.52 | 0.29 | 0.19 | 0.12 | 0.06 |
0.5 | 100 |
Ktrans
| 0.97 | 0.97 | 0.93 | 0.84 | 0.70 | 0.49 | ||
6 | 5 |
Ktrans
| 0.17 | 0.27 | 0.24 | 0.15 | 0.06 | 0.03 | ||
6 | 100 |
Ktrans
| 0.81 | 0.88 | 0.73 | 0.46 | 0.23 | 0.10 | ||
vp = 0.001
|
0.02
|
0.05
|
0.1
|
0.2
|
0.5
| |||||
0.5 | 5 |
vp
| 0.09 | 0.15 | 0.24 | 0.36 | 0.41 | 0.37 | ||
0.5 | 100 |
vp
| 0.50 | 0.83 | 0.88 | 0.91 | 0.87 | 0.86 | ||
6 | 5 |
vp
| 0.11 | 0.12 | 0.14 | 0.16 | 0.21 | 0.19 | ||
6 | 100 |
vp
| 0.23 | 0.41 | 0.49 | 0.55 | 0.59 | 0.63 | ||
Fp = 0.5
|
1
|
5
| ||||||||
0.5 | 5 |
Fp
| 0.19 | 0.24 | 0.31 | |||||
0.5 | 100 |
Fp
| 0.68 | 0.81 | 0.90 | |||||
6 | 5 |
Fp
| 0.14 | 0.15 | 0.17 | |||||
6 | 100 |
Fp
| 0.40 | 0.48 | 0.54 | |||||
FXR
|
FXR
| 0.5 | 5 |
Ktrans
| 0.37 | 0.41 | 0.43 | 0.48 | 0.52 | 0.46 |
0.5 | 100 |
Ktrans
| 0.90 | 0.92 | 0.96 | 0.93 | 0.93 | 0.94 | ||
6 | 5 |
Ktrans
| 0.20 | 0.23 | 0.25 | 0.24 | 0.23 | 0.23 | ||
6 | 100 |
Ktrans
| 0.63 | 0.76 | 0.80 | 0.81 | 0.74 | 0.82 | ||
τi = 0.1
|
0.5
|
2
| ||||||||
0.5 | 5 |
τi
| 0.41 | 0.45 | 0.44 | |||||
0.5 | 100 |
τi
| 0.94 | 0.93 | 0.92 | |||||
6 | 5 |
τi
| 0.24 | 0.23 | 0.22 | |||||
6 | 100 |
τi
| 0.76 | 0.75 | 0.73 |
Accuracy of model selection using nested model analysis
Percentage of voxels selected (%)
| |||||
---|---|---|---|---|---|
Generating model | SNR | Model 0 | Model 1 | Model 2 | Model 3 |
Steady-State (Model 1)
| 5 | 2.75 | 41 | 43 | 13.25 |
Steady-State (Model 1)
| 100 | 0 | 75 | 20.75 | 4.25 |
Patlak (Model 2)
| 5 | 0 | 0 | 94.75 | 5.25 |
Patlak (Model 2)
| 100 | 0 | 0 | 100 | 0 |
Extended Tofts (Model 3)
| 5 | 0 | 11.25 | 19 | 69.5 |
Extended Tofts (Model 3)
| 100 | 0 | 8.5 | 0 | 91.5 |