DW-MRI and MRS to Differentiate Radiation Necrosis and Recurrent

Disease in Gliomas

Protocol MRS data status11/27/07 – 12/17/07

Thomas Chong

Patient MRS (Magnetic Resonance Spectroscopy) Data

Provides data on in vivo chemical composition (via proton NMR) of the lesion and surrounding regions of the brain

15x15x3 grid of 1cc voxels Raw data is time response processed to

spectrum (ppm) Characteristic resonance peaks in spectrum

correspond to specific compounds (e.g. water, lipids, metabolites). area = ~amount

Focus on Choline/ NAA ratio change for glioma diagnosis

Status Summary 11/27/07 - 12/17/07

Previous Work What have I done? team role?

Recap of last summary: Remaining patient #'s 1-10 data manually processed

Knowledge available from the EU INTERPRET project – show and tell of journal paper info

Related Previous Work Metabolite phantom experiment goal to quantify

MRS errors (“registration” or alignment) T2 FLAIR pulse sequence inappropriate for cuvette

phantom but enough data to show alignment is close enough

for our purposes (*) created dynamic web-accessible MRS

database and metabolite amount computation tool (http://web.arizona.edu/mrs_db)

processed all inf/med/sup MRS grid slices for all patients (1-10), inc. validity maps

Low Percentage of “Valid” MRS Spectra -1

No metabolite amount info obtainable from most data best, cleanest P1, P2, P5

Motivated investigation into possible spatial correlations No clear correlations obvious. Need more data

Low Percentage of “Valid” MRS Spectra -2

Artifacts present in MRS protocol data are consistent with those seen by other researchers large baseline distortions exceptionally broadened metabolite peaks large phase errors

Other observed data corrupting factor SNR of cho, cre, or naa peaks reduced by large unknown

resonance peak broad non-metabolite peak, or non-constant floor

MRS signal interpretation for tumors recognized as a complicated task – see INTERPRET project (International Network for Pattern Recognition of Tumours Using Magnetic Resonance), a consortium of 10 EU countries [1]

INTERPRET MRS project Ongoing since 2000, latest developments in

2007 paper Their goal: “to develop a computer-based

decision support tool, that will enable radiologists and other clinicians without special expertise to diagnose and grade brain tumours routinely using magnetic resonance spectroscopy.” Using: “A large "training set" of data contributed by

members of the INTERPRET consortium. “Automated pattern recognition techniques for

tumour classification.

INTERPRET Tumor Classifier Description

INTERPRET DSS (decision support system) “Easy access to a database of spectra, images and

clinical information from 304 validated cases of human brain tumour.

“designed to allow the display of classification plots useful for automating the classification of tumour spectra.

“Currently only one classification plot is provided (suitable for discriminating spectra from low grade gliomas vs Glioblastomas and Metastasis vs Low-grade Meningiomas).

http://www.cogs.susx.ac.uk/users/joshuau/interpret/

INTERPRET MRS project Developed MRS brain tumor classification

software. Pattern recognition based on large training dataset: 6 MR systems/ 3 diff manufacturers, custom

phantom, 3yrs of patient protocol data SW and data is available for download to aid

clinicians not <competing?> researchers http://www.cogs.susx.ac.uk/users/joshuau/

interpret/ A lot of interesting MRS-tumor related info

INTERPRET MRS project – so? what of it?

Still, how to distinguish artifacts from presence of unwanted/unknown substances?

Common recognition that MRS data interpretation is not easy: see above name of the big EU project rigorous process for deciding validity of MRS voxel

data in their database entailed up to 3 expert spectrologists.

INTERPRET MRS project – so? what of it?

Useful SNR and WBW measures defined phantom reference gives info on data variability (it's

noisy, based on successive bimonthly meas.) automated program to check spectrum for WBW <

8Hz, SNR > 10 Tumor recognition tool does not utilize track of

time trend changes in data

INTERPRET Recognition Software Still research-quality, i.e. use at own risk Reverse-engineered guess at method:

candidate spectrum matched to database-derived mean reference spectrum using neural network and/or (Bayesian) statistical methods.

Can leverage their data & findings as resource to better understand our own data

SV 1H-MRS SHORT echo time spectra of different human brain

tumoural pathologies

Normal brain mean of 22 cases

Glioblastoma mean of 86 cases

Tate et al. NMR Biomed. 19:411-434, 2006, http://azizu.uab.es/INTERPRET/mean_spectra_images.html

SV 1H-MRS SHORT echo time spectra of different human brain

tumoural pathologies

Normal brain mean of 22 cases

Astrocytoma II mean of 22 cases

Tate et al. NMR Biomed. 19:411-434, 2006, http://azizu.uab.es/INTERPRET/mean_spectra_images.html

SV 1H-MRS SHORT echo time spectra of different human brain

tumoural pathologies

Normal brain mean of 22 cases

Abscess mean of 8 cases

Tate et al. NMR Biomed. 19:411-434, 2006, http://azizu.uab.es/INTERPRET/mean_spectra_images.html

SV 1H-MRS SHORT echo time spectra of different human brain

tumoural pathologies

Glioblastoma mean of 86 cases

Abscess mean of 8 cases v similar to glio.

SV 1H-MRS SHORT echo time spectra of different human brain

tumoural pathologies

Normal brain mean of 22 cases

Metastasis mean of 38 cases

Tate et al. NMR Biomed. 19:411-434, 2006, http://azizu.uab.es/INTERPRET/mean_spectra_images.html

SV 1H-MRS SHORT echo time spectra of different human brain

tumoural pathologies

Normal brain mean of 22 cases

Meningioma mean of 58 cases

Tate et al. NMR Biomed. 19:411-434, 2006, http://azizu.uab.es/INTERPRET/mean_spectra_images.html

New Perspective of Our “Bad” Data Absence of distinct metabolite peaks prevents

calculation of relative amounts and ratios, ... But others have empirically categorized

tumor types based on spectral characteristics. MRS data we're collecting usable for future

studies using different methods

Patient 10, Exam 3446, S16.1“short” or “long” echo time in our protocol?

Patient 1, Exam 3103, S38

Patient 1, Exam 3103, S67

Questions What is water-suppressed spectrum?

non-suppressed spectrum? What is the water line artifact in spectrum? Shimming procedure for optimization of field

homogeneity? What does van der Graaf, et al refer to as

signal linearity? WBW (water bandwidth): line width at half max

intensity of water resonance in real non-suppressed spectrum - a measure of field homogeneity [1]. significance?

Tasks Process patient 11 scan data Answer questions on previous slide Return to INTERPRET website, continue

looking for useful information How useful was their diagnosis system in the

clinical setting? (MRS data alone) Read source ref for mean brain MRS data: Tate et

al. NMR Biomed. 19:411-434, 2006 Request e-mailed for access to their tool

download page and database

Reference [1] MRS quality assessment in a multicentre

study on MRS-based classification of brain tumours. M. van der Graaf, et. al., NMR in Biomedicine, DOI: 10.1002, 2007.

[2] Tate et al. NMR Biomed. 19:411-434, 2006 http://azizu.uab.es/INTERPRET/

mean_spectra_images.html