Professional Education

  • Doctor of Philosophy, Stanford University, EE-PHD (2014)
  • Master of Science, Stanford University, EE-MS (2011)
  • Bachelor of Science, Pennsylvania State University, Electrical Eng & Mech Eng (2006)

Research & Scholarship

Lab Affiliations


All Publications

  • Toward Volumetric MR Thermometry With the MASTER Sequence IEEE TRANSACTIONS ON MEDICAL IMAGING Marx, M., Plata, J., Pauly, K. B. 2015; 34 (1): 148-155


    MR temperature monitoring is an indispensable tool for high intensity focused ultrasound. In this paper, a new technique known as MASTER (multiple adjacent slice thermometry with excitation refocusing) is presented which improves the speed and accuracy of multiple-slice MR thermometry. Defocusing the magnetization after exciting a slice allows for multiple slices to be excited concurrently and stored in k-space. The magnetization from each excitation is then refocused and read in sequence. This approach increases TE for each slice, greatly improving temperature SNR as compared to conventional slice interleaving. Gradient sequence design optimization is required to minimize diffusion losses while maintaining high sequence efficiency. Flexibility in selecting position, update rate, accuracy, and voxel size for each slice independently allows for freedom in design to fit different application needs. Results are shown in phantom and in vivo validating the feasibility of the sequence, and comparing it to interleaved GRE. Sample design curves are presented that contrast the MASTER design space with that of interleaved GRE thermometry.

    View details for DOI 10.1109/TMI.2014.2349912

    View details for Web of Science ID 000346975900015

    View details for PubMedID 25163055

  • A novel approach for global noise reduction in resting-state fMRI: APPLECOR NEUROIMAGE Marx, M., Pauly, K. B., Chang, C. 2013; 64: 19-31


    Noise in fMRI recordings creates uncertainty when mapping functional networks in the brain. Non-neural physiological processes can introduce correlated noise across much of the brain, altering the apparent strength and extent of intrinsic networks. In this work, a new data-driven noise correction, termed "APPLECOR" (for Affine Parameterization of Physiological Large-scale Error Correction), is introduced. APPLECOR models spatially-common physiological noise as the linear combination of an additive term and a mean-dependent multiplicative term, and then estimates and removes these components. APPLECOR is shown to achieve greater consistency of the default mode network across time and across subjects than was achieved using global mean regression, respiratory volume and heart rate correction (RVHRCOR (Chang et al., 2009)), or no correction. Combining APPLECOR with RVHRCOR regressors attained greater consistency than either correction alone. Use of the proposed noise-reduction approach may help to better identify and delineate the structure of resting state networks.

    View details for DOI 10.1016/j.neuroimage.2012.09.040

    View details for Web of Science ID 000312504200003

    View details for PubMedID 23022327

  • Application of Zernike polynomials towards accelerated adaptive focusing of transcranial high intensity focused ultrasound MEDICAL PHYSICS Kaye, E. A., Hertzberg, Y., Marx, M., Werner, B., Navon, G., Levoy, M., Pauly, K. B. 2012; 39 (10): 6254-6263


    To study the phase aberrations produced by human skulls during transcranial magnetic resonance imaging guided focused ultrasound surgery (MRgFUS), to demonstrate the potential of Zernike polynomials (ZPs) to accelerate the adaptive focusing process, and to investigate the benefits of using phase corrections obtained in previous studies to provide the initial guess for correction of a new data set.The five phase aberration data sets, analyzed here, were calculated based on preoperative computerized tomography (CT) images of the head obtained during previous transcranial MRgFUS treatments performed using a clinical prototype hemispherical transducer. The noniterative adaptive focusing algorithm [Larrat et al., "MR-guided adaptive focusing of ultrasound," IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57(8), 1734-1747 (2010)] was modified by replacing Hadamard encoding with Zernike encoding. The algorithm was tested in simulations to correct the patients' phase aberrations. MR acoustic radiation force imaging (MR-ARFI) was used to visualize the effect of the phase aberration correction on the focusing of a hemispherical transducer. In addition, two methods for constructing initial phase correction estimate based on previous patient's data were investigated. The benefits of the initial estimates in the Zernike-based algorithm were analyzed by measuring their effect on the ultrasound intensity at the focus and on the number of ZP modes necessary to achieve 90% of the intensity of the nonaberrated case.Covariance of the pairs of the phase aberrations data sets showed high correlation between aberration data of several patients and suggested that subgroups can be based on level of correlation. Simulation of the Zernike-based algorithm demonstrated the overall greater correction effectiveness of the low modes of ZPs. The focal intensity achieves 90% of nonaberrated intensity using fewer than 170 modes of ZPs. The initial estimates based on using the average of the phase aberration data from the individual subgroups of subjects was shown to increase the intensity at the focal spot for the five subjects.The application of ZPs to phase aberration correction was shown to be beneficial for adaptive focusing of transcranial ultrasound. The skull-based phase aberrations were found to be well approximated by the number of ZP modes representing only a fraction of the number of elements in the hemispherical transducer. Implementing the initial phase aberration estimate together with Zernike-based algorithm can be used to improve the robustness and can potentially greatly increase the viability of MR-ARFI-based focusing for a clinical transcranial MRgFUS therapy.

    View details for DOI 10.1118/1.4752085

    View details for Web of Science ID 000310101900044

    View details for PubMedID 23039661

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