High-res PD-SPACE for segmentation with Chondral Quant

Here is a brief description of the sequences:

• Sagittal PD-weighted SPACE sequence with fat suppression: 
  This isotropic sequence (or as close as possible) can be used to visualize bone, cartilage, fluid, and synovium interfaces for accurate segmentation and quantification of the volume of each joint structure. Reformatting in sagittal, coronal, and axial planes allows for additional accuracy in delineating surfaces and interfaces. Acute injuries with high signal intensity findings representing edema, inflammation, or hemorrhage within the soft tissue and bone may also be detected.
• Coronal T1-weighted sequence: 
  A non-fat suppressed T1-weighted sequence provides good SNR and fat contrast for detecting bone processes such as edema and masses or neoplasms. This sequence may also be used to detect chronic soft tissue and fat scarring. However, there is little soft tissue contrast other than for fat.
• Sagittal PD-weighted sequence: 
  A non-fat suppressed PD-weighted sequence provides soft tissue and fluid contrast with good SNR.
• Axial T2-weighted sequence: 
  A non-fat suppressed T2-weighted sequence is used to assess areas with true fluid signal. A fat-saturated sequence could mistake prominent soft tissue degeneration such as meniscus or chondral degeneration for a tear or defect, respectively.
• Sagittal T2 mapping sequence: 
  A multi-echo spin echo sequence is used for quantitative T2 relaxation time mapping to evaluate cartilage biochemical composition and integrity. T2 relaxation time in cartilage is sensitive to water and collagen content and organization. A sagittal sequence is used to evaluate and map cartilage in the medial, lateral, and patellofemoral compartments. Given the low resolution and 2D nature of the sequence, cartilage is delineated first on the SPACE sequence and transferred to the T2 relaxation time map to more accurately define regions of cartilage within the T2 relaxation time map and avoid adjacent bone and fluid-containing voxels.