16HPCI magazine Vol.

Digital Twin of Cerebral
Blood Circulation
Provides Optimal
Medical Care for
Individuals
Cerebral infarction and subarachnoid hemorrhage are serious and scary conditions
related to abnormalities in the circulation of blood within the brain. Imaging tests
such as CT (computed tomography) and MRI (magnetic resonance imaging) areused to diagnose these diseases, but they do not provide detailed information on
blood flow. Some brain diseases are also related to the circulation of cerebrospinal
fluid and cerebral interstitial fluid. Therefore, Prof. Satoshi Ii and his colleagues are
conducting research to create a “brain circulation digital twin” that reproduces the
cerebral circulation of an individual patient in cyberspace, so that clinicians canpredict the risk of disease at the point of care.

Satoshi Ii

Professor, Department of Mechanical Engineering, School of Engineering, Tokyo Institute of Technology

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Until fiscal year 2022, Prof. Ii was a member of the research project of theProgram for Promoting Research on the Supercomputer Fugaku led by
Prof. Shigeo Wada of Osaka University. “When it comes to the brain,neural activity tends to attract attention, but blood circulation that carries
oxygen and nutrients supports neural activity. Furthermore, abnormalities
in blood circulation can lead to illness, so detailed information on
circulation could be used to help with diagnosis in clinical settings. That’s
why Prof. Wada thought about simulating blood circulation throughout thebrain,” Ii said, explaining how his research began.
To simulate blood circulation, we must first create a model of the entire
vascular network of the brain. Prof. Ii undertook this task. “In the brain, thethick carotid arteries branch into thinner vessels that spread throughout
various parts of the brain and then reconverge into the thick jugular veins.
While the shapes of the thick vessels can be determined from CT or MRI
images, the arrangement of the thinner vessels cannot be seen.
Therefore, we established mathematical rules to create the network ofthin vessels,” he explained.
The brain vascular network model constructed in this manner (Fig. 1)
closely replicated the actual vascular network. Furthermore, in thisproject, other members created a “physical model” that modeled the flowof blood according to the physical laws. This enabled a successful
simulation of blood flow through the vascular network model that Prof. Ii
had built (Fig. 2). This has made it viable to analyze cerebral blood flow
in detail and examine its relationship with brain diseases.

A. Comparison of pial vessel diameters
Diameter (μm)

Simulating Blood Flow throughout the Brain

ArteryVein

Present

Measure d*B. Blood vessels on the surface of the cerebral gyri

Present

Observed**Duvernoy et al., 1981, Brain Res Bull, 7:519

Fig. 1: Comparison of the brain vascular networkmodel and the actual brain vascular network
Rules such as “blood flow at the terminal is proportional to the brain region size”
and “minimize the total volume of the entire pathway” were presupposed tocreate the thin vessels and construct a model of the vascular network acrossthe entire brain surface. The distribution of the diameters of the model’s vessels
closely matches the distribution of the diameters of actual vessels (A), and theshape of the vascular network is also very similar to observed results (B).
A and B left: Reprinted from Ii S et al. (2020) Multiscale modeling of humancerebrovasculature: A hybrid approach using image-based geometry and a mathematicalalgorithm. PLoS Comput Biol 16(6): e1007943. © 2020 Ii et al. under the terms of theCreative Commons CC BY license.
B right: Reprinted from Brain Research Bulletin Vol. 7(5), Duvernoy et al., Cortical bloodvessels of the human brain, Copyright (2024), with permission from Elsevier.

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Digital Twin of Cerebral Blood Circulation
Provides Optimal Medical Care for Individuals

Whole brain blood circulation simulation
Courtesy of Shunichi Ishida & Yohsuke Imai (Kobe Univ.)

Posterior viewof Prof. Wada’s work to other aspects of brain circulation beyond blood,
as well as to develop tools that can be immediately used by doctors inclinical settings,” he said. Let’s illustrate how this research is progressing,using a cerebral aneurysm as an example.
A cerebral aneurysm is a bulging, weakened area in the wall of a bloodvessel in the brain (Fig. 3). The rupture of this bulge leads tosubarachnoid hemorrhage, which can be fatal in many cases. There are
two main treatments for cerebral aneurysms. One involves performing acraniotomy to clip the base of the aneurysm, preventing blood flow to it.
The other involves using a catheter to deliver metal coils into theaneurysm through the blood vessel, filling it internally. Both treatments
carry significant risks and are challenging procedures.

Left side viewFig. 2: Whole brain (surface) blood circulation simulation
A scene from a video showing blood entering from the carotid artery, circulating
through the brain surface, and flowing out to the jugular vein. The peripheral bloodvessels (microcirculation) extending from the brain’s surface toward the center
have not yet been calculated. However, this simulation required the full power ofthe K computer.

Cerebral Aneurysms: Difficulties in Assessing
Risks between Surgery and Observation
Since fiscal year 2023, Prof. Ii has been working on a new research project
as the principal investigator. “In this project, the goal is to extend the results

Fig. 3: Three-dimensional shape of cerebral aneurysm
constructed from MRI Images
Cerebral aneurysms can form at a branching point (left) or on the side (right) of
cerebral arteries.

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For physicians, whether to adopt a treatment is a dilemma, since many
individuals with cerebral aneurysms live their entire lives without
experiencing a rupture. Assessing whether the risk of the aneurysm
growing and rupturing is greater than the risk of treatment is difficult. This
has led to a growing demand for predicting the rupture risk of specificaneurysms in specific patients.

Blood Flow Simulation of Cerebral Aneurysm
Cerebral aneurysm growth and rupture are thought to be influenced by
the pressure of blood flowing through the aneurysm and the wall shear
stress (shear forces exerted on the blood vessel wall). Therefore, Prof. Ii
utilized insights gained from the previous project led by Prof. Wada tosimulate the blood flow near the aneurysm.

Specifically, Prof. Ii segmented the blood vessels near the cerebral
aneurysm into an orthogonal grid, and conducted simulations using aphysical model (Fig. 4). Since each patient’s blood vessel shapes vary,using an orthogonal grid approach avoids the complexity of generating
calculation grids that align with the vessel shapes each time. One reason
for choosing orthogonal grids is their suitability for large-scale parallel
computing, at which Fugaku excels.
A significant feature of this research is the incorporation of “dataassimilation,” which is used in applications such as meteorologicalsimulations. Data assimilation involves integrating observational data into+

Time series observation
data of blood flow

Vascular (aneurysm)
shape

A

Observed velocity(PC-MRI)+

B

Physical modelC

Simulation(data assimilation analysis)Fig. 5: Blood flow simulation using data assimilation
Fig. 4: Blood flow simulation near cerebral aneurysm
Blood vessels near the cerebral aneurysm (in this case, with a diameter ofapproximately 14mm for the aneurysm, and overall approximately 20mm)
segmented into orthogonal grids for simulating blood flow.

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Time-series data of the blood flow velocity measured by PC-MRI was incorporated
into the simulation. With PC-MRI it is possible to observe the blood flow velocity at
various points inside the blood vessels, though the level of granularity is low as shownin the lower left. By assimilating this data into the simulation, it becomes viable tocalculate the blood flow velocity (B) and shear stress on the vessel wall (C) at eachpoint with high resolution. Image A was prepared by decimating the high-resolution
blood flow velocity distribution data obtained from the simulation (B) to reproduce theobserved velocity.

Digital Twin of Cerebral Blood Circulation
Provides Optimal Medical Care for Individuals

simulations to ensure that simulations do not deviate from the reality. “In
this case, we are simulating a portion of the brain’s blood vessels, so weneed to define boundary conditions such as the flow velocity entering thatarea. Using average values alone would not replicate the individual
conditions of each patient,” explained Prof. Ii. Therefore, he enabled
specific simulations to each patient by assimilating the flow velocities atvarious points inside the blood vessels measured using PC-MRI*1 (Fig. 5).

In this way, it is now possible to perform blood flow simulations forindividual cerebral aneurysms (Fig. 6). Preparations have finally been
completed to gather data on wall shear stress and local pressure, whichcannot be known with imaging devices. “The risk factors for the growth of
cerebral aneurysms are still not clearly understood. There are reportssuggesting that strong wall shear stress contributes to growth, whileothers indicate that significant changes lead to growth, creating divided
opinions on what exactly constitutes a risk factor. To clarify these risk
factors, it is crucial to accumulate numerical data obtained throughsimulations and analyze them quantitatively,” Prof. Ii explained,emphasizing the significance of this approach.

Aiming to Develop Applications That Can Be
Used in Clinical Settings“However, even if the risk factors are identified, it is impractical forphysicians to conduct patient-specific simulations to predict the growth or
rupture risks of cerebral aneurysms in clinical settings. Considering thebusyness of clinical environments and computing resources, a simpler
predictive model is needed. Therefore, we are focusing on developing
machine learning models,” Prof. Ii continued.

Fig. 6: Scene from a simulation video of one cardiac
cycle of a cerebral aneurysm
From left to right in the lower section: Blood flow velocity, pressure exerted on thevessel wall, and wall shear stress are shown. The intensity increases from blue to red.
The upper left section shows observational results from PC-MRI. The circles (◯) on
the right graph represent blood flow velocity measured by PC-MRI. The solid red line
represents the blood flow velocity obtained from the simulation. The measured andsimulated values closely match.

This machine learning model infers the pressure on blood vessel walls
and wall shear stress from imaging data. Since inference is feasible evenwith the computational power available in clinical settings, this model
could enable PC applications where, for example, clicking on ananeurysm’s wall in an MRI image displays the pressure and wall shear
stress there. Physicians could use this information to assess the risk.
However, to create this machine learning model, it is necessary to train it
on a large teaching dataset pairing imaging examination data (vascularshape and velocity field) and data calculated from simulations, including

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the pressure on the blood vessel wall and the wall shear stress.
Gathering these data by conducting PC-MRI examinations on a large
number of patients in clinical settings is not feasible. Therefore, Prof. Ii
and his team plan to generate teaching data using simulations. Whileindividual simulations are not computationally intensive, they intend toleverage the computational power of Fugaku to quickly obtain simulation
results for tens of thousands of different scenarios.
To obtain teaching data, they will create a large amount of pseudo-image
test data from single image test data by slightly changing the shape ofblood vessels, blood viscosity, etc., and perform a blood flow simulationfor each. Using the simulation results as teaching data has the advantageof creating a machine learning model with better performance than using
noisy image test data as is.

Predicting Disease Progression with Digital
Twins
In Prof. Ii’s project, Prof. Marie Oshima from the University of Tokyo,
another participant, aims to construct a different type of machine learningmodel focusing on severe internal carotid artery stenosis. Instead of
directly simulating, the model aims to infer simulation-equivalent results
using parameters such as the length of the vessel and the degree ofnarrowing or derive risk factors (such as changes in blood flow velocity inthis case) by transforming outcomes.
Furthermore, in this project, they are expanding the scope of simulations
and machine learning to include not only blood-related fluids relevant toconditions like stroke and cerebral aneurysm, but also cerebrospinal fluid
related to hydrocephalus and brain interstitial fluid associated with
Alzheimer’s disease.

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Physical space

Brain
Brain
circulation Risk
circulation Risk
Transition
factordata
data
factorto disease

Person A

Transition ofhealth status

Digital space

Healthy
Person A’ s brain circulation digital twin

Human Digital Twin for Cerebral Circulation

DiseasedTransmission of brain
circulation information
related to transition todiseasePerson B
Digital space

Person B’ s brain circulation digital twin

Transition
to disease

Transition ofhealth status

Physical space

Brain
Risk
circulation factor
data

Brain
Risk
circulation factor
data

Predictionof transition
to disease

Fig. 7: Predicting disease progression using “Human
Digital Twin for Cerebral Circulation”
Using the digital twin of brain circulation for an individual (Person A), the values of risk
factors can be determined from their brain circulation data. Each examination provides
the values of risk factors, which are then sent to the “Human Digital Twin for Cerebral
Circulation” along with clinical information representing the disease state. By
implementing this for many individuals and accumulating data, a “Human Digital Twin
for Cerebral Circulation” is created that depicts the relationship between thelongitudinal changes in risk factors and the progression of disease. Using this, it
becomes possible to predict the progression of disease for another person (Person B).
In other words, by analyzing the longitudinal changes in risk factors and health statusobtained from Person B’s brain circulation digital twin with the “Human Digital Twin for
Cerebral Circulation,” physicians will be able to predict the progression of Person B’s
disease. This figure illustrates an example of predicting the transition to disease before
the onset of illness.

Digital Twin of Cerebral Blood Circulation
Provides Optimal Medical Care for Individuals

The ultimate goal of these studies, as pursued by Prof. Ii and his team, is
to construct a “Human Digital Twin for Cerebral Circulation” (Fig. 7). “Upto this point, our aim has been to create individual digital twins of brain
circulation in cyberspace, enabling to determine values of risk factors
such as wall shear stress and use them for risk prediction,” Prof. Ii
explained. “The ‘Human Digital Twin for Cerebral Circulation’ aggregatesthese digital twins from numerous individuals, akin to a databank thatcombines values of risk factors derived from individual brain circulation
data with information on disease progression. Referencing an individual’s
digital twin against the ‘Human Digital Twins for Cerebral Circulation’ willallow us to make predictions of disease progression beyond what anindividual digital twin alone can achieve.”
However, achieving such research goals is not achievable withengineering researchers alone. Therefore, Prof. Ii’s project involves
collaboration with medical researchers, including Lecturer Shigeki
Yamada from Nagoya City University and Prof. Yoshiyuki Watanabe from
Shiga University of Medical Science. Together with medical researchers,
they are advancing the collection of clinical data and striving tounderstand the relationship between diseases and brain circulation.
Prof. Ii started his tenure at the Tokyo Institute of Technology in April 2024.
Around the same time, “TSUBAME 4.0” began operations at the Tokyo
Institute of Technology (Photo 1). “TSUBAME 4.0” is equipped withhigh-performance GPUs (a type of computing device) suitable formachine learning. In his research on brain circulation digital twins, Prof. Ii
is eager to use “TSUBAME 4.0.” While it requires a different approachfrom the Fugaku CPU, leveraging the strengths of each, he aims toadvance his research. The day may soon come when hospitals will ask
us, “Could you provide data to the ‘Human Digital Twins for Cerebral
Circulation’?”Photo 1: “TSUBAME 4.0”

The theoretical computing performance in the computing mode used for machinelearning, etc. (16-bit, half precision), has achieved 952 petaflops. As of June 2024, it
ranks second among supercomputers at domestic research/educational institutions,following Fugaku. It can also be utilized as one of the computing resources in the HPCI(High Performance Computing Infrastructure) system.

※1 Phase contrast (PC) MRI is one of the imaging methods used in MRI. It provides images that reflect
blood flow velocity.

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Digital Twin of Cerebral Blood Circulation
Provides Optimal Medical Care for Individuals

About theResearcher
Prof. Ii is an active simulation specialist. Surprisingly, he hardly opened the PC he bought when he enrolled
until his third year of university, and wrote almost all of his reports by hand. When choosing a laboratory, helearned about a study simulating the flight of insects and was impressed that “simulation can reveal so muchabout the flow of invisible things.” He entered the world of simulation research, and after a period of researchon the simulation technique itself, he wanted to create a simulation that would be useful in some way, so hefollowed the path of biological simulation.
Looking back on that time, he said he was impressed by the fact that an approach that combines simulation
with physics such as fluid mechanics can elucidate complex biological phenomena.
To relax on his days off, Prof. Ii tries his hand at cutting-edge programming. When he gets a headache, he can’t
help but think of various diseases and the state of their circulatory fluids.

Associated Research Project: Development of personalized medical support technology based on simulation data science of whole brain blood circulation (hp220161)
Principal Investigator: Shigeo Wada, Osaka University
Associated Research Project: Development of human digital twins for cerebral circulation using Fugaku (hp230208)
Principal Investigator: Satoshi Ii, Tokyo Institute of Technology
Tokyo Institute of Technology and Tokyo Medical and Dental University will merge in October 2024, becoming Institute of Science Tokyo.

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Issued: August 2024
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