\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{CJKutf8} 
\usepackage[utf8]{inputenc}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}
\begin{CJK}{UTF8}{gbsn}

\title{Transcranial Phase Correction Using Pulse-echo Ultrasound and Deep
Learning: A 2D Numerical Study}



\author[1]{Zixuan Tian }%
\author[1]{Matthew Olmstead }%
\author[1]{Yun Jing }%
\author[1]{Aiguo Han }%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 07, 2026}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Phase aberration caused by human skulls severely degrades the quality of
transcranial ultrasound images, posing a major challenge in the
practical application of transcranial ultrasound techniques in adults.
Aberration can be corrected if the skull profile (i.e., thickness
distribution) and speed of sound (SOS) are known. However, accurately
estimating the skull profile and SOS using ultrasound with a
physics-based approach is challenging due to the complexity of the
interaction between ultrasound and the skull. A deep learning approach
is proposed herein to estimate the skull profile and SOS using
ultrasound radiofrequency (RF) signals backscattered from the skull. A
numerical study was performed to test the approach's feasibility.
Realistic numerical skull models were constructed from computed
tomography (CT) scans of five ex vivo human skulls in this numerical
study. Acoustic simulations were performed on 3,595 skull segments to
generate array-based ultrasound backscattered signals. A deep learning
model was developed and trained to estimate skull thickness and SOS from
RF channel data. The trained model was shown to be highly accurate. The
mean absolute error was 0.15 mm (2\% error) for thickness estimation and
13 m/s (0.5\% error) for SOS estimation. The Pearson correlation
coefficient between the estimated and ground-truth values was 0.99 for
thickness and 0.95 for SOS. Aberration correction performed using
deep-learning-estimated skull thickness and SOS values yielded
significantly improved beam focusing (e.g., narrower beams) and
transcranial imaging quality (e.g., improved spatial resolution and
reduced artifacts) compared with no aberration correction. The results
demonstrate the feasibility of the proposed approach for transcranial
phase aberration correction.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{CJK}\end{document}

