Evaluation of Synthetically Generated CT for use in Transcranial Focused Ultrasound Procedures Han Liuay Michelle K. Sigonabcy Thomas J. Manuelbc Li Min Chencd Benoit M.

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Evaluation of Synthetically Generated CT for use in Transcranial

Focused Ultrasound Procedures

Han Liua,†, Michelle K. Sigonab,c,†, Thomas J. Manuelb,c, Li Min Chenc,d, Benoit M.

Dawante, Charles F. Caskeyb,c,d,*

aDept. of Computer Science, Vanderbilt University, Nashville, TN 37235, USA

bDept. of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA

cInstitute of Imaging Science, Vanderbilt University, Nashville, TN 37235, USA

dDept. of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37235, USA

eDept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA

†These authors contributed equally to this work

Abstract.

Purpose: Transcranial focused ultrasound (tFUS) is a therapeutic ultrasound method that focuses sound through the

skull to a small region noninvasively and often under MRI guidance. CT imaging is used to estimate the acoustic

properties that vary between individual skulls to enable effective focusing during tFUS procedures, exposing patients

to potentially harmful radiation. A method to estimate acoustic parameters in the skull without the need for CT would

be desirable.

Approach: Here, we synthesized CT images from routinely acquired T1-weighted MRI by using a 3D patch-based

conditional generative adversarial network (cGAN) and evaluated the performance of synthesized CT (sCT) images

for treatment planning with tFUS. We compared the performance of sCT to real CT (rCT) images for tFUS planning

using Kranion and simulations using the acoustic toolbox, k-Wave. Simulations were performed for 3 tFUS scenarios:

1) no aberration correction, 2) correction with phases calculated from Kranion, and 3) phase shifts calculated from

time-reversal.

Results: From Kranion, skull density ratio, skull thickness, and number of active elements between rCT and sCT had

Pearson’s Correlation Coefﬁcients of 0.94, 0.92, and 0.98, respectively. Among 20 targets, differences in simulated

peak pressure between rCT and sCT were largest without phase correction (12.4±8.1%) and smallest with Kranion

phases (7.3±6.0%). The distance between peak focal locations between rCT and sCT was less than 1.3 mm for all

simulation cases.

Conclusions: Real and synthetically generated skulls had comparable image similarity, skull measurements, and

acoustic simulation metrics. Our work demonstrates the feasibility of replacing real CTs with the MR-synthesized CT

for tFUS planning. Source code and a docker image with the trained model are available at https://github.

com/han-liu/SynCT_TcMRgFUS

Keywords: Transcranial Focused Ultrasound, Acoustic Simulation, Image-guided, Image Translation, Synthetic CT,

Conditional Adversarial Networks.

*Charles F. Caskey, charles.f.caskey@vanderbilt.edu

1 Introduction

Transcranial focused ultrasound (tFUS) is a novel noninvasive method of focusing energy through

the skull that often uses Magnetic Resonance Imaging (MRI) for target identiﬁcation, treatment

planning, and closed-loop control of energy deposition.1Focused ultrasound is clinically approved

for thermally ablating the thalamus2and when used at lower energy levels is being explored for

arXiv:2210.14775v1 [eess.IV] 26 Oct 2022

other applications, such as drug delivery and neuromodulation.3Precise focusing is critical for

all tFUS procedures to minimize treatment of off-target tissues.4Prior to tFUS, CT images are

acquired to estimate regional skull density, speed of sound, and ultrasound attenuation during ul-

trasound wave propagation.5Thermally ablative thalamotomy procedures use MR thermometry6

to intraoperatively monitor thermal dose and targeting accuracy. MR Thermometry relies on the

temperature dependence of the proton resonance frequency shift to linearly map phase differences

between two time points to temperature change. Another tFUS application is neuromodulation, a

nonthermal method that has been demonstrated in humans targeting the thalamus,7somatosensory

cortex,8and primary visual cortex.9During neuromodulation procedures, neuronavigation aids in

real-time transducer placement by calculating the position and rotation of optically tracked tools

and projecting the transducer’s focus onto pre-acquired images. The projected focus from optical

tracking is usually a free-ﬁeld estimate of the focus location,10 neglecting the inhomogenous layers

of the skull known to shift and distort the focus.11 The inclusion of CT images to the neuromod-

ulation planning process allows incorporation of skull models to map the skull layers to acoustic

properties and estimate spatial accuracy, spatial extent, and output pressure for patient-speciﬁc

skull models. CT imaging burdens patients by requiring longer screening time and increased risk

due to radiation. For tFUS research in development and preclincal phases, it is unrealistic to obtain

CT scans of a healthy participant. Therefore, it is desirable to replace the real CT (rCT) images

with synthetic CT (sCT) images that are generated from other imaging modalities.

Values from CT images of the head are used in different ways during treatment planning for

all tFUS procedures. One important metric is the Skull Density Ratio (SDR), an estimate of the

transparency of the skull to ultrasound. The SDR is not always predictive of the energy needed

to generate a focal spot transcranially, but a lower SDR is generally interpreted to mean lower

acoustic transmission through the skull.12,13 Although the precise method for computing SDR

on a clinical system is proprietary, the metric is derived from the ratio of the Hounsﬁeld Units

(HU) of trabecular to cortical bone along the line from a transducer element to the focus,13 and

an open source software, Kranion, is available that is capable of generating SDR metrics highly

correlated to those found in clinical procedures.14 Along with SDR, Kranion can report a Skull

Thickness (ST) measurement between bone layers and Number of Active Elements (NAE), or

an element’s ray less than <20 degrees incident to the skull. Detailed spatial maps have been

created from CT images to map acoustic properties and model the propagation of sound through

the skull.5,15,16 Using modeling tools like the acoustic toolbox, k-Wave,17 simulations are used to

observe ultrasound waves interacting with subject-speciﬁc heterogeneous skulls, quantifying the

focal shift, focus size, and energy loss caused by the aberrating skull.

The use of multi-element arrays during tFUS procedures is desirable because each individ-

ual element’s amplitude and phase can be precisely controlled. Electronically controlled elements

are integral during tFUS procedures to move the transducer’s focus location without physically

manipulating the transducer and to calculate phase shifts to correct for the skull.18,19 Several aber-

ration correction methods have been explored that vary in run-time and focus restoration perfor-

mance.14,20,21 For clinical thermoablation, real-time estimation of amplitude and phase correction

are essential as procedures require shifting the small focal volume throughout the brain to ablate

the full target.19 Selection of correction method is usually dependent on a trade-off between time

constraints and intensity required for a given application.

Deep-learning based methods have been previously used to generate synthetic CTs from MR

images.22 Dual-echo ultrashort TE (UTE) MR imaging23 was used to train a 2D U-Net24 that

was efﬁcient at generating realistic skulls, but UTE scans are not widely available and require

development on an MR scanner, as they are not standard protocols. An alternative to UTE images

are T1-weighted images, but these can be more challenging to synthesize CT skulls from than UTE

because UTE imaging can capture signals from tissues with a very short transverse relaxation time

such as bone, providing more information for skull synthesis. For instance, Lei et al.25 proposed

to use patch-based features extracted from MRIs to train a sequence of alternating random forests

based on an iterative reﬁnement model. Maspero et al.26 trained three 2D cGANs27 for each

plane and combine the results to generate synthetic CT from T1-weighted MRI. Gupta et al.28

proposed to train a 2D U-Net on sagittal views of MRIs and synthesize the HU of air, soft tissue

and bone in three output channels. However, 2D networks are limited by the lack of information

of relationship between slices29 and the skulls are not spatially continuous (i.e generated volumes

can appear jagged) along the views that are not involved in training. The irregular skull geometry

of sCT may lead to signiﬁcant differences in tFUS planning. In very recent work done in parallel

to ours30 a 3D cGAN was proposed to synthesize the whole head CT from MR images. Here, we

focus on the skull, which is the critical structure for tFUS.

We hypothesize that synthetic CT generated from MRI can yield comparable clinical metrics

for transcranial ultrasound that are derived from CT. Our study used two open-source software

tools to compare skull metrics derived from sCT and rCT images using 10 testing cases with two

targets. We evaluated the performance of the rCT and sCT skulls using Kranion to report the SDR,

ST, and NAEs. Acoustic simulations were performed using k-Wave to calculate the pressure ﬁeld

formed from interactions with each CT and compared the aberration correction performance capa-

bilities through a fast ray-tracing method and a computationally expensive time reversal technique.

From each simulation we quantiﬁed the maximum intracranial pressure, focal shift between the

peak pressure and intended target locations, and focal volumes. Demonstration of similarity be-

tween sCT and rCT would show feasibility of synthesizing spatially continuous CT skulls from

T1-weighted MRI.

2 METHODS

2.1 Dataset and Pre-processing

In this study, our dataset included 86 paired CT and T1-weighted MRI scans of Parkinson patients,

who underwent Deep Brain Stimulation from Vanderbilt University Medical Center. Informed

consent was obtained from all subjects included in this study. The in-plane resolution of CT images

ranged from 0.4297 to 0.5449 mm with a slice thickness of 0.67 mm, while the MR images had

an isotropic voxel size of 1 mm. To prepare the paired CT-MRI dataset for network training,

we applied a series of image pre-processing procedures as follows. First, for each subject, we

spatially aligned the MRI and CT images by rigid registration. Speciﬁcally, we registered the

low-resolution MRI scans to the high-resolution CT images to preserve the HU values in high-

resolution CT images. Rigid registration was used based on the assumption that the shape and size

of brain anatomical structures do not vary for the same subject in different imaging modalities.

Here, we employed an open-source medical imaging library ANTsPy for rigid registration, where

mutual information was used as the cost function. Second, to discard the irrelevant brain regions

to our skull synthesis task, we ﬁltered out the non-skull regions from the CT images. Speciﬁcally,

we extracted a binary mask of the skull region by using an empirically selected threshold, i.e., 400

HU. We then took the largest connected component of the mask to remove other isolated regions.

To preserve some contextual information around the skull, we further performed morphological

dilation to the skull mask with a ball-shaped structuring element with radius of 4 voxels. This

dilated mask was used to ﬁlter the raw CT image to obtain the skull-only CT image. Besides, for

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EvaluationofSyntheticallyGeneratedCTforuseinTranscranialFocusedUltrasoundProceduresHanLiua,y,MichelleK.Sigonab,c,y,ThomasJ.Manuelb,c,LiMinChenc,d,BenoitM.Dawante,CharlesF.Caskeyb,c,d,*aDept.ofComputerScience,VanderbiltUniversity,Nashville,TN37235,USAbDept.ofBiomedicalEngineering,VanderbiltUniversity...

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Evaluation of Synthetically Generated CT for use in Transcranial Focused Ultrasound Procedures Han Liuay Michelle K. Sigonabcy Thomas J. Manuelbc Li Min Chencd Benoit M.

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