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Open Access Publications from the University of California

Scholarly Works (3 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2024)

Magnetic resonance imaging (MRI) is widely adopted to assess anatomy and function of human bodies. Current clinical MRI mainly relies on contrast-weighted images and the clinicians make diagnosis by observing the relative intensity differences between tissues. In contrast, quantitative MRI directly measures the tissue physical parameters, thus providing biomarkers with enhanced objectivity, reproducibility, and sensitivity. Considering the clinicians’ reliance on conventional weighted MRI and the advantages of quantitative MRI, obtaining both is promising to provide a comprehensive tissue characterization and to benefit patient care. However, the acquisition of a full array of contrast-weighted images and parameter maps demands a prolonged scan time, which has been a major challenge.In this dissertation, we developed deep learning approaches to obtain both multi-contrast weighted images and multi-parametric maps within short scan times. Based on the physical relationships between the contrast-weighted images and the tissue parameter maps, we proposed to only acquire one of them (i.e., either weighted images or quantitative maps) and to retrospectively derive the other from the same acquired data. Both directions were explored. First, an efficient multi-parametric quantitative MRI technique was employed for acquisition, and multiple conventional T1-weighted and T2-weighted images were synthesized from the quantitative MRI data using deep learning. The method was evaluated on healthy volunteers and multiple sclerosis patients, achieving comparable image quality with true acquired images and better performance than the traditional Bloch-equation-based synthesis. In the second direction, multi-contrast weighted images were acquired following current clinical practices and deep learning approaches were developed to retrospectively estimate T1 and T2 maps from them. Both supervised and physics-guided self-supervised learning methods were developed. The supervised learning method requires a set of label parameter maps paired with the input weighted images as training data, whereas the self-supervised learning framework eliminated such a demand by incorporating physics models and formulating the optimization problem in the weighted image space. Both approaches generated results well matched with the conventionally acquired T1 and T2 maps. The self-supervised learning approach was further applied to a public glioblastoma dataset and the resulting retrospective T1 and T2 values showed potential in brain tumor characterization.

    • Article
    • Peer Reviewed
    UCLA Previously Published Works (2022)

    Purpose

    To develop a deep-learning-based method to quantify multiple parameters in the brain from conventional contrast-weighted images.

    Methods

    Eighteen subjects were imaged using an MR Multitasking sequence to generate reference T1 and T2 maps in the brain. Conventional contrast-weighted images consisting of T1 MPRAGE, T1 GRE, and T2 FLAIR were acquired as input images. A U-Net-based neural network was trained to estimate T1 and T2 maps simultaneously from the contrast-weighted images. Six-fold cross-validation was performed to compare the network outputs with the MR Multitasking references.

    Results

    The deep-learning T1 /T2 maps were comparable with the references, and brain tissue structures and image contrasts were well preserved. A peak signal-to-noise ratio >32 dB and a structural similarity index >0.97 were achieved for both parameter maps. Calculated on brain parenchyma (excluding CSF), the mean absolute errors (and mean percentage errors) for T1 and T2 maps were 52.7 ms (5.1%) and 5.4 ms (7.1%), respectively. ROI measurements on four tissue compartments (cortical gray matter, white matter, putamen, and thalamus) showed that T1 and T2 values provided by the network outputs were in agreement with the MR Multitasking reference maps. The mean differences were smaller than ± 1%, and limits of agreement were within ± 5% for T1 and within ± 10% for T2 after taking the mean differences into account.

    Conclusion

    A deep-learning-based technique was developed to estimate T1 and T2 maps from conventional contrast-weighted images in the brain, enabling simultaneous qualitative and quantitative MRI without modifying clinical protocols.
      Cover page: Multiparametric mapping in the brain from conventional contrast‐weighted images using deep learning
      • Article
      • Peer Reviewed
      UCLA Previously Published Works (2023)

      Purpose

      To develop a deep learning-based method to retrospectively quantify T2 from conventional T1- and T2-weighted images.

      Methods

      Twenty-five subjects were imaged using a multi-echo spin-echo sequence to estimate reference prostate T2 maps. Conventional T1- and T2-weighted images were acquired as the input images. A U-Net based neural network was developed to directly estimate T2 maps from the weighted images using a four-fold cross-validation training strategy. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean percentage error (MPE), and Pearson correlation coefficient were calculated to evaluate the quality of network-estimated T2 maps. To explore the potential of this approach in clinical practice, a retrospective T2 quantification was performed on a high-risk prostate cancer cohort (Group 1) and a low-risk active surveillance cohort (Group 2). Tumor and non-tumor T2 values were evaluated by an experienced radiologist based on region of interest (ROI) analysis.

      Results

      The T2 maps generated by the trained network were consistent with the corresponding reference. Prostate tissue structures and contrast were well preserved, with a PSNR of 26.41 ± 1.17 dB, an SSIM of 0.85 ± 0.02, and a Pearson correlation coefficient of 0.86. Quantitative ROI analyses performed on 38 prostate cancer patients revealed estimated T2 values of 80.4 ± 14.4 ms and 106.8 ± 16.3 ms for tumor and non-tumor regions, respectively. ROI measurements showed a significant difference between tumor and non-tumor regions of the estimated T2 maps (P < 0.001). In the two-timepoints active surveillance cohort, patients defined as progressors exhibited lower estimated T2 values of the tumor ROIs at the second time point compared to the first time point. Additionally, the T2 difference between two time points for progressors was significantly greater than that for non-progressors (P = 0.010).

      Conclusion

      A deep learning method was developed to estimate prostate T2 maps retrospectively from clinically acquired T1- and T2-weighted images, which has the potential to improve prostate cancer diagnosis and characterization without requiring extra scans.
        Cover page: Retrospective T2 quantification from conventional weighted MRI of the prostate based on deep learningCreative Commons 'BY' version 4.0 license
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