b-value optimization for diffusion weighted magnetic resonance imaging

1. A method for selecting a set of b-values for a diffusion weighted scan, comprising:

identifying a single reference slice within a volume of interest (VOI);

progressively changing b-values over a plurality of scans of the reference slice wherein each one of the scans has a different b-value to obtain a plurality of diffusion weighted images;

calculating ADC maps for combinations of b-values from the obtained plurality of diffusion weighted images;

evaluating the calculated ADC maps;

selecting from the evaluation optimal b-values; and

using the selected b-values for subsequent scans.

2. A method for selecting a set of b-values for a diffusion weighted scan, comprising:

identifying a single reference slice having a position and orientation providing a representative view of the volume of interest (VOI);

performing n scans, where n is an integer greater than 1, on the reference scan at progressively increasing b-values to provide a set of pre-scan reference images;

interactively identifying a region of interest (ROI), or multiple ROI, within the reference slice and relevant regions outside of the ROI(s) for reference, with the ROIs identified in one scan being propagated to the other images;

calculating ADC maps for combinations of b-values as such values are available from the set of pre-scan reference images including calculating a subset of the maps based on only two b-values, and the other maps being based on more than two b-values,

evaluating each combination of b-values according to a plurality of criteria;

selecting optimal b-values based on the evaluation; and

using the selected b-values for subsequent scans.

3. The method recited in claim 2 wherein the criterion includes one or more of: goodness of fit parameters in the ADC map calculations; the error in predicting SI for each b-value image; SNR within the b-value image; SNR within the ROI; contrast between the ROI and surrounding regions; similarity measures in the ROI and surrounding regions.

4. The method recited in claim 1 wherein the evaluation includes a weighting scheme based on the measures pertaining to complete ADC maps and b-value images, ADC goodness of fit, ADC error of prediction and b-value image SNR.

5. The method recited in claim 4 wherein the weighting scheme includes giving greater weight to pixels within the ROI and near to the ROI based on the level of SNR and contrast in the vicinity of the pixel in the b-value images.

6. The method recited in claim 1 wherein the evaluation includes a weighting scheme based on the measurements of SNR and contrast in the b-value images.

7. The method recited in claim 4 wherein the weighting scheme includes obtaining an overall image weighted score.

8. The method recited in claim 5 wherein the weighting scheme includes obtaining an overall image weighted score and obtaining ROI weighted scores, which are all combined into a global score for each combination of b-values.

9. The method recited in claim 8 including setting a range of acceptable global scores, and selecting the global score within that range that employs the smallest number of b-values to minimize subsequent scan time.

10. The method recited in claim 4 wherein the weighting scheme includes adding the number of b-values used for a particular ADC map into the weighting scheme.

11. The method recited in claim 2 including using linear optimization methods to minimize the number of b-values used to minimize scan time while simultaneously maximizing one or more of the other computed metrics.

12. The method recited in claim 11 wherein one of the metrics is SNR.