# US 2017/0285124 A1 · Edema invariant tractography

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- Number: US 2017/0285124 A1
- Document ID: US20170285124A1
- Status: Published application
- Kind: A1
- Application number: 15/509,402
- Filed: 2015-09-08
- Published: 2017-10-05
- Earliest claimed: 2014-09-09
- Assignees: The Trustees of the University of Pennsylvania
- Applicants: The Trustees of the University of Pennsylvania
- Inventors: Ragini Verma; Luke Macyszyn; Emmanuel Caruyer; Jeremy Lecoeur; Mark Elliot; Steven Brem
- Web page: https://patentgrep.com/patent/US20170285124A1

## CPC

- G01R 33/56341: {Diffusion imaging}
- G01R 33/5608: {Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels (image data processing or generation, in general G06T)}
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)

## Abstract

Edema invariant tractography methods are provided. The methods include acquiring data using a multishell, high angular resolution diffusion imaging sequence; (ii) modeling the data using a multi-compartment model; and (iii) reconstructing the fibers through a probabilistic tracking algorithm using a fiber orientation distribution which has streamlines fitted to the output of said probabilistic tracking algorithm.

## Claims

### Claim 1

An edema invariant tractography method comprising, with a processor:
acquiring data from a patient using a 3-shell, high angular resolution diffusion imaging sequence, wherein:
the first shell as a b-value of about 300 s/mm2 and a gradient direction of about 15, the second shell has a b-value of about 800 s/mm2 and a gradient direction of about 30, and the third shell has a b-value of about 2000 s/mm2 and a gradient direction of about 64;
(ii) modeling said data using a multi-compartment model; and
(iii) reconstructing the fibers through a probabilistic tracking algorithm using a fiber orientation distribution which has streamlines fitted to the output of said probabilistic tracking algorithm.

### Claim 2

An edema invariant tractography method comprising, with a processor:
acquiring data from a patient using a multishell, high angular resolution diffusion imaging sequence;
(ii) modeling said data using a multi-compartment model comprising a free water compartment; and
(iii) reconstructing the fibers through a probabilistic tracking algorithm using a fiber orientation distribution which has streamlines fitted to the output of said probabilistic tracking algorithm.

### Claim 3

A method of identifying tracts in an edematous brain of a patient, said method comprising, with a processor:
acquiring data from said patient using a 3-shell, high angular resolution diffusion imaging sequence, wherein:
the first shell as a b-value of about 300 s/mm2 and a gradient direction of about 15, the second shell has a b-value of about 800 s/mm2 and a gradient direction of about 30, and the third shell has a b-value of about 2000 s/mm2 and a gradient direction of about 64;
(ii) modeling said data using a multi-compartment model; and
(iii) reconstructing the fibers through a probabilistic tracking algorithm using a fiber orientation distribution which has streamlines fitted to the output of said probabilistic tracking algorithm.

### Claim 4

A method of identifying tracts in an edematous brain of a patient, said method comprising, with a processor:
acquiring data from a patient using a multishell, high angular resolution diffusion imaging sequence;
(ii) modeling said data using a multi-compartment model comprising a free water compartment; and
(iii) reconstructing the fibers through a probabilistic tracking algorithm using a fiber orientation distribution which has streamlines fitted to the output of said probabilistic tracking algorithm.

### Claim 5 (depends on 2)

The method according to claim 2, wherein said multicompartment model further comprises a fiber orientation distribution.

### Claim 6 (depends on 1)

The method according to claim 1, wherein said multicompartment model estimates diffusion directions and the amount of extra-cellular water in the presence of edema.

### Claim 7 (depends on 1)

The method according to claim 1, wherein said multicompartment model estimates underlying fiber tract visualizations in the presence of said edema.

### Claim 8 (depends on 1)

The method according to claim 1, wherein said multi-compartment model is:
sk=f sow exp(−bkdow)+(1−f) FOD*FIR(bk, uk)
wherein:
sk=measured signal intensity corresponding to k-th diffusion gradient;
uk=direction corresponding to k-th diffusion gradient;
bk=b-value corresponding to k-th diffusion gradient;
f=volume fraction and baseline signal intensity of the FW compartment;
sow=volume fraction and baseline signal intensity of the FW compartment;
dow=FW diffusion coefficient;
FOD=fiber orientation distribution;
FIR=fiber impulse response; and
*=convolution operator.

### Claim 9 (depends on 8)

The method according to claim 8, wherein said FIR is estimated by fitting a DTI-model to the data and identifying voxels with FA>0.7 as single fiber voxels.

### Claim 10 (depends on 8)

The method according to claim 8, wherein sow is estimated using a CSF map from a tumor growth model or segmentation map.

### Claim 11 (depends on 8)

The method according to claim 8, wherein said FOD and the volume fraction f are estimated using the following equation:
{circumflex over (f)}, ĉ=arg min|s−(fsw+(1−f)HRc)|2
wherein:
0≦f≦1;
Bc≧0;
sw=signal from FW compartment;
H=spherical harmonics observation matrix;
R=convolution operator computed from FIR;
c=spherical harmonic representation of FOD; and
B=spherical harmonics evaluated on points on which positive constraint is imposed.

### Claim 12 (depends on 1)

The method according to claim 1, wherein the reconstruction comprises white matter tracts.

### Claim 13 (depends on 12)

The method according to claim 12, wherein the reconstruction comprises diffusion measures associated with said tracts.

### Claim 14 (depends on 1)

The method according to claim 1, wherein the reconstruction comprises high curvature fibers.

### Claim 15 (depends on 14)

The method according to claim 14, wherein said high curvature fibers comprise the lateral projections of the cortico-spinal tract, arcuate fasciculus, Meyer's loop of optic radiation, or combinations thereof.

### Claim 16 (depends on 1)

The method according to claim 1, wherein the reconstruction comprises crossing fibers.

### Claim 17 (depends on 1)

The method according to claim 1, wherein one or more shells of said data is obtained at low b-values that are sensitive to free water diffusion.

### Claim 18 (depends on 2)

The method according to claim 2, wherein said multishell multiband image acquisition comprises a 3 or more shell acquisition.

### Claim 19 (depends on 18)

The method according to claim 18, wherein the first shell has a b-value of about 10 to about 400 s/mm2 and a gradient direction of about 7 to about 100, the second shell has a b-value of about 400 to 1000 s/mm2 and a gradient direction of about 10 to about 80, and the third shell has a b-value of about 1500 to about 5000 s/mm2 and a gradient direction of about 25 to about 128.

### Claim 20 (depends on 19)

The method according to claim 19, wherein the first shell as a b-value of about 300 s/mm2 and a gradient direction of about 15, the second shell has a b-value of about 800 s/mm2 and a gradient direction of about 30, and the third shell has a b-value of about 2000 s/mm2 and a gradient direction of about 64.

### Claim 21 (depends on 1)

The method according to claim 1, which comprises at least about 7 non-diffusion weighted acquisitions.

### Claim 22 (depends on 21)

The method according to claim 21, which comprises 7 non-diffusion weighted acquisitions.

### Claim 23 (depends on 1)

The method according to claim 1, which is performed on the brain of said patient.

### Claim 24 (depends on 3)

The method according to claim 3, which identifies crossing fibers in the brain of said patient.

### Claim 25 (depends on 3)

The method according to claim 3, wherein the white matter of said brain is analyzed.

### Claim 26 (depends on 3)

The edema invariant method according to claim 3, which tracks through the white matter of said brain to produce fiber tract visualizations.

### Claim 27 (depends on 1)

The method according to claim 1, which is invariant to increased local free water diffusion.

### Claim 28

The method according to 3, wherein a tumor is present in said brain.

### Claim 29 (depends on 28)

The method according to claim 28, wherein peritumoral regions are analyzed.

### Claim 30 (depends on 1)

The method according to claim 1, which is performed in the absence of manually designating a region of interest.

### Claim 31 (depends on 1)

The method according to claim 1, which may be utilized to improve resecting a tumor from the brain of said patient.

### Claim 32 (depends on 3)

The method according to claim 3, wherein the edema results from a tumor, low blood sodium, multiple sclerosis, an injury, blood clot, infection, hemorrhage, high altitudes, or combinations thereof.

### Claim 33 (depends on 32)

The method according to claim 32, wherein said infection comprises meningitis, encephalitis, toxoplasmosis, subdural empyem, or combinations thereof.

### Claim 34 (depends on 2)

The method according to claim 2, which is performed using software that performs the functions of FIG. 4A or FIG. 4B.

### Claim 35

A tractography electronic device comprising:
(a) code for performing a 3-shell, high angular resolution diffusion imaging sequence, wherein:
the first shell as a b-value of about 300 s/mm2 and a gradient direction of about 15, the second shell has a b-value of about 800 s/mm2 and a gradient direction of about 30, and the third shell has a b-value of about 2000 s/mm2 and a gradient direction of about 64;
(b) code for performing a multi-compartment model;
(c) code for reconstructing the fibers through a probabilistic tracking algorithm using a fiber orientation distribution which has streamlines fitted to the output of said probabilistic tracking algorithm; and
(d) at least one processing unit for executing said code of (a)-(c).

### Claim 36

A tractography electronic device comprising:
(a) code for performing a multishell, high angular resolution diffusion imaging sequence;
(b) code for performing a multi-compartment model comprising a free water compartment; and
(c) code for reconstructing the fibers through a probabilistic tracking algorithm using a fiber orientation distribution which has streamlines fitted to the output of said probabilistic tracking algorithm; and
(d) at least one processing unit for executing said code of (a)-(c).

### Claim 38

A tractography electronic device, said device comprising at least one processing unit configured to:
acquire data using a multishell, high angular resolution diffusion imaging sequence;
model said data using a multi-compartment model comprising a free water compartment; and
reconstruct the fibers through a probabilistic tracking algorithm using a fiber orientation distribution which has streamlines fitted to the output of said probabilistic tracking algorithm.

### Claim 39

A computer program product, stored on a computer readable medium, said product comprising instructions for configuring a computer to:
acquire data using a 3-shell, high angular resolution diffusion imaging sequence, wherein:
the first shell as a b-value of about 300 s/mm2 and a gradient direction of about 15, the second shell has a b-value of about 800 s/mm2 and a gradient direction of about 30, and the third shell has a b-value of about 2000 s/mm2 and a gradient direction of about 64;
model said data using a multi-compartment model; and
reconstruct the fibers through a probabilistic tracking algorithm using a fiber orientation distribution which has streamlines fitted to the output of said probabilistic tracking algorithm.

### Claim 40

A computer program product, stored on a computer readable medium, said product comprising instructions for configuring a computer to:
acquire data using a multishell, high angular resolution diffusion imaging sequence;
model said data using a multi-compartment model comprising a free water compartment; and
reconstruct the fibers through a probabilistic tracking algorithm using a fiber orientation distribution which has streamlines fitted to the output of said probabilistic tracking algorithm.

### Claim 41 (depends on 39)

The product according to claim 39, which is web-accessible.