Post-processing correction of the endorectal coil reception effects in MR spectroscopic imaging of the prostate

The Center for Molecular and Functional Imaging, Department of Radiology and Biomedical Imaging, The University of California, San Francisco, California 94107, USA.
Journal of Magnetic Resonance Imaging (Impact Factor: 3.21). 09/2010; 32(3):654-62. DOI: 10.1002/jmri.22258
Source: PubMed


To develop and validate a post-processing correction algorithm to remove the effect of the inhomogeneous reception profile of the endorectal coil on MR spectroscopic imaging (MRSI) data.
A post-processing algorithm to correct for the endorectal coil reception effects on MRSI data was developed based upon theoretical modeling of the endorectal coil reception profile and of the spatial saturation pulse profiles. This algorithm was evaluated on three-dimensional (3D) MRSI data acquired at 3T from a uniform phantom and from 18 patients with known or suspected prostate cancer.
For the phantom data, the coefficient of variation of metabolite peak areas decreased 16% to 46% and the peak area distributions became more Gaussian with correction, as demonstrated by higher Q-Q plot linear correlations (R(2) = 0.98 +/- 0.007 vs. R(2) = 0.89 +/- 0.066). Across the 18 patients, the mean coefficient of variation for suppressed water decreased significantly, from 0.95 +/- 0.18, to 0.66 +/- 0.11, (P < 10(-6), paired t-test) and the linear correlations of the Q-Q plots for the suppressed water increased from R(2) = 0.91 to R(2) = 0.95 (P = 0.0083, paired t-test) with correction.
An algorithm for reducing the effect of the inhomogeneous reception profile in endorectal coil acquired 3D MRSI prostate data was demonstrated, illustrating increased homogeneity and more Gaussian peak area distributions.

Full-text preview

Available from:
  • [Show abstract] [Hide abstract]
    ABSTRACT: Even though 1 in 6 men in the US, in their lifetime are expected to be diagnosed with prostate cancer (CaP), only 1 in 37 is expected to die on account of it. Consequently, among many men diagnosed with CaP, there has been a recent trend to resort to active surveillance (wait and watch) if diagnosed with a lower Gleason score on biopsy, as opposed to seeking immediate treatment. Some researchers have recently identified imaging markers for low and high grade CaP on multi-parametric (MP) magnetic resonance (MR) imaging (such as T2 weighted MR imaging (T2w MRI) and MR spectroscopy (MRS)). In this paper, we present a novel computerized decision support system (DSS), called Semi Supervised Multi Kernel Graph Embedding (SeSMiK-GE), that quantitatively combines structural, and metabolic imaging data for distinguishing (a) benign versus cancerous, and (b) high- versus low-Gleason grade CaP regions from in vivo MP-MRI. A total of 29 1.5Tesla endorectal pre-operative in vivo MP MRI (T2w MRI, MRS) studies from patients undergoing radical prostatectomy were considered in this study. Ground truth for evaluation of the SeSMiK-GE classifier was obtained via annotation of disease extent on the pre-operative imaging by visually correlating the MRI to the ex vivo whole mount histologic specimens. The SeSMiK-GE framework comprises of three main modules: (1) multi-kernel learning, (2) semi-supervised learning, and (3) dimensionality reduction, which are leveraged for the construction of an integrated low dimensional representation of the different imaging and non-imaging MRI protocols. Hierarchical classifiers for diagnosis and Gleason grading of CaP are then constructed within this unified low dimensional representation. Step 1 of the hierarchical classifier employs a random forest classifier in conjunction with the SeSMiK-GE based data representation and a probabilistic pairwise Markov Random Field algorithm (which allows for imposition of local spatial constraints) to yield a voxel based classification of CaP presence. The CaP region of interest identified in Step 1 is then subsequently classified as either high or low Gleason grade CaP in Step 2. Comparing SeSMiK-GE with unimodal T2w MRI, MRS classifiers and a commonly used feature concatenation (COD) strategy, yielded areas (AUC) under the receiver operative curve (ROC) of (a) 0.89±0.09 (SeSMiK), 0.54±0.18 (T2w MRI), 0.61±0.20 (MRS), and 0.64±0.23 (COD) for distinguishing benign from CaP regions, and (b) 0.84±0.07 (SeSMiK),0.54±0.13 (MRI), 0.59±0.19 (MRS), and 0.62±0.18 (COD) for distinguishing high and low grade CaP using a leave one out cross-validation strategy, all evaluations being performed on a per voxel basis. Our results suggest that following further rigorous validation, SeSMiK-GE could be developed into a powerful diagnostic and prognostic tool for detection and grading of CaP in vivo and in helping to determine the appropriate treatment option. Identifying low grade disease in vivo might allow CaP patients to opt for active surveillance rather than immediately opt for aggressive therapy such as radical prostatectomy.
    Medical image analysis 12/2012; 17(2). DOI:10.1016/ · 3.65 Impact Factor

  • Journal of Radiotherapy in Practice 03/2013; 12(01). DOI:10.1017/S1460396913000058
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In many studies, it has been demonstrated that (1) H MRSI of the human prostate has great potential to aid prostate cancer management, e.g. in the detection and localisation of cancer foci in the prostate or in the assessment of its aggressiveness. It is particularly powerful in combination with T2 -weighted MRI. Nevertheless, the technique is currently mainly used in a research setting. This review provides an overview of the state-of-the-art of three-dimensional MRSI, including the specific hardware required, dedicated data acquisition sequences and information on the spectral content with background on the MR-visible metabolites. In clinical practice, it is important that relevant MRSI results become available rapidly, reliably and in an easy digestible way. However, this functionality is currently not fully available for prostate MRSI, which is a major obstacle for routine use by inexperienced clinicians. Routine use requires more automation in the processing of raw data than is currently available. Therefore, we pay specific attention in this review on the status and prospects of the automated handling of prostate MRSI data, including quality control. The clinical potential of three-dimensional MRSI of the prostate is illustrated with literature examples on prostate cancer detection, its localisation in the prostate, its role in the assessment of cancer aggressiveness and in the selection and monitoring of therapy. Copyright © 2013 John Wiley & Sons, Ltd.
    NMR in Biomedicine 01/2014; 27(1). DOI:10.1002/nbm.2973 · 3.04 Impact Factor
Show more