Saito et al. EJNMMI Reports (2024) 8:15
https://doi.org/10.1186/s41824-024-00205-9

ORIGINAL ARTICLE

EJNMMI Reports

Open Access

Absolute quantitation of sympathetic nerve
activity using ­[123I] metaiodobenzylguanidine
SPECT‑CT in neurology
Shintaro Saito1* , Kenichi Nakajima2 , Junji Komatsu3, Takayuki Shibutani4, Hiroshi Wakabayashi1,
Hiroshi Mori1, Aki Takata1, Kenjiro Ono3 and Seigo Kinuya1*Correspondence:
shintarosaito19861118@gmail.
com
1
Department of Nuclear
Medicine, Kanazawa
University, 13‑1 Takara‑machi,
Kanazawa 920‑8640, Japan2
Department of Functional
Imaging and Artificial
Intelligence, Kanazawa
University, Kanazawa, Japan3
Department of Neurology,
Kanazawa University Graduate
School of Medical Sciences,
Kanazawa, Japan
4
Department of Quantum
Medical Technology, Instituteof Medical, Pharmaceuticaland Health Sciences, Kanazawa
University, Kanazawa, Japan

Abstract
Background and purpose: The ability of ­[123I]metaiodobenzylguanidine (MIBG) sympathetic nerve imaging with three-dimensional (3D) quantitation to clinically diagnose
neurological disorders has not been evaluated. This study compared absolute heartcounts calculated as mean standardized uptake values ­(SUVmean) using conventional
planar imaging and assessed the contribution of ­[123I]MIBG single-photon emission
computed tomography (SPECT)-CT to the diagnosis of neurological diseases.
Methods: Seventy-two patients with neurological diseases were consecutively
assessed using early and delayed ­[123I]MIBG SPECT-CT and planar imaging. Leftventricles were manually segmented in early and delayed SPECT-CT images, thenthe ­SUVmean and washout rates (WRs) were calculated. Heart-to-mediastinum ratios(HMRs) and WRs on planar images were conventionally computed. We investigatedcorrelations between planar HMRs and SPECT-CT ­SUVmeans and between WRs obtained
from planar and SPECT-CT images. The cutoff for SPECT-CT WRs defined by linear
regression and that of normal planar WRs derived from a database were comparedwith neurological diagnoses of the patients. We assigned the patients to groups
according to clinical diagnoses as controls (n = 6), multiple system atrophy (MSA,n = 7), progressive supranuclear palsy (PSP, n = 17), and Parkinson’s disease or dementia with Lewy bodies (PD/DLB, n = 19), then compared SPECT-CT and planar image
parameters.
Results: We found significant correlations between SPECT-CT ­SUVmean and planar
HMR on early and delayed images ­(R2 = 0.69 and 0.82, p < 0.0001) and between SPECTCT and planar WRs ­(R2 = 0.79, p < 0.0001). A threshold of 31% for SPECT-CT WR basedon linear regression resulted in agreement between planar and SPECT-CT WR in 67(93.1%) of 72 patients. Compared with controls, early and delayed ­SUVmean in patientswith PSP and MSA tended more towards significance than planar HMR. This trend
was similar for SPECT-CT WRs in patients with PSP.
Conclusions: Absolute heart counts and ­SUVmean determined using ­[123I]MIBG SPECTCT correlated with findings of conventional planar images in patients with neurological diseases. Three-dimensional quantitation with ­[123I]MIBG SPECT-CT imaging might
differentiate patients with PSP and MSA from controls.

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Saito et al. EJNMMI Reports (2024) 8:15

Keywords: Heart-to-mediastinum ratio, Neurodegenerative disorder, Standardized
uptake value, Three-dimensional quantitation, Washout rate

Background
The physiological noradrenaline analog, ­[123I]metaiodobenzylguanidine (MIBG), tracks
absorption, transit, and the subsequent vesicular storage of noradrenaline in postsynaptic sympathetic nerve terminals. It can reveal sympathetic nerve activity, integrity, anddisturbances in several pathological states. Myocardial ­[123I]MIBG uptake and washout are clinically useful markers of neurodegenerative disorders such as dementia with
Lewy bodies (DLB) and Parkinson’s disease (PD), as well as for evaluating disease severity and prognosis, the effects of treatment on heart failure, and arrhythmogenic disease(Nakajima and Nakata 2015; Nakajima and Yamada 2016; McKeith et al. 2017; Travin
et al. 2017; Yamada et al. 2020; Verschure et al. 2022). Heart-to-mediastinum ratios(HMR) and washout rates (WR) determined from planar images are indicators of sympathetic nerve activity (Okuda et al. 2011; Nakajima et al. 2017; Owenius et al. 2017;
Bateman et al. 2019). However, although assessment protocols have been standardized,
the uncertainty of HMR analysis of clinical data results in up to 40% of patients remaining in the measurement gray zone (Klene et al. 2018). Three-dimensional (3D) images
generated by single-photon emission computed tomography (SPECT) can distinguishbetween cardiac activity and background or overlapping organs. Quantitative 3D evaluation using ­[123I]MIBG SPECT is feasible without CT (Chen et al. 2012; Saito et al. 2021),
and Lewy body disease has been diagnosed based on the heart-to-aorta ratio using ­[123I]
MIBG SPECT-CT (Odagiri et al. 2016), which is a relative 3D quantitation method.
When attenuation is corrected using low-dose CT, absolute heart counts and standardized uptake values (SUVs) in patients with cardiac and neurological diseases can becalculated based on ­[123I]MIBG SPECT images (Saito et al. 2023). However, the relationship between 3D quantitation and clinical diagnoses of neurological diseases remains
unknown. Here, we aimed to determine the contribution of [­123I]MIBG SPECT-CT tothe differential diagnosis of neurological diseases. We therefore compared clinical data
generated by conventional planar image-based quantitation with absolute heart counts
determined as mean SUVs ­(SUVmean) derived from ­[123I]MIBG SPECT-CT images ofpatients with neurological diseases.
Methods
Patients

This study initially enrolled 72 consecutive patients (average age, 69.8 ± 8.8; range,
35‒88 years; male, n = 27; female, n = 45) with neurological diagnoses at Kanazawa University Hospital between 2020 and 2022. Table 1 shows that PS/PD/DLB (n = 50) comprised Parkinson’s syndrome (PS; n = 31), PD (n = 14), DLB (n = 5), and non-PS/PD/DLB(n = 22) including familial amyloid polyneuropathy (FAP; n = 7) and other neurologicaldiseases. A neurologist with expertise in movement disorders categorized the diagnoses
based on the recommended criteria for progressive supranuclear palsy (PSP), multiple
system atrophy (MSA), PD, and DLB (Gilman et al. 2008; Postuma et al. 2015; Hoglinger
et al. 2017; McKeith et al. 2017). Cardiac ­[123I]MIBG uptake was significantly reduced toa planar delayed HMR < 1.5 in 17 patients.

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Table 1 Characteristics of 72 patients
CharacteristicsnAge

69.8 ± 8.8

Sex (male)
Body weight (kg)Body mass index (kg/m2)
PS/PD/DLB (n, %)27 (38%)54.4 ± 11.5

22.0 ± 3.5
50 (69%)Parkinson’s syndrome

31 (43%)

  Progressive supranuclear palsy

17 (24%)

  Multiple system atrophy

7 (9%)

  Other Parkinson’s syndrome

7 (9%)

Parkinson’s disease

14 (19%)Dementia with Lewy bodies

5 (7%)

Non-PS/PD/DLB

22 (31%)

Familial amyloid polyneuropathy

7 (9%)

Alzheimer disease

2 (3%)

Cerebral hemorrhage

1 (1%)

Other diseases and/or unknown etiology
Reduced cardiac uptake (planar delayed HMR < 1.5)

12 (17%)11 (15%)

Data are shown as means ± SD or n (%)

DLB dementia with Lewy bodies, HMR heart-to-mediastinum ratio, PD Parkinson’s disease, PS Parkinson’s syndrome, SD
standard deviation[123I]MIBG image processing

All patients were evaluated using early and delayed [­123I]MIBG planar and SPECT-CT
imaging. A medium-energy (ME) collimator was applied to image acquisition using Symbia Intevo and Symbia Intevo Bold SPECT-CT scanners (Siemens Healthcare, Erlangen,Germany). The patients were intravenously administered with 111 MBq of ­[123I] MIBG,
and images were acquired 15–20 (early phase) and 180–240 (delayed phase) minutes
later. Planar images were acquired for 5 min using 256 × 256-matrix with a zoom factorof 1.0 (2.4-mm pixels). The SPECT images were acquired using 128 × 128-matrix with azoom factor of 1.0 (4.8-mm pixels), 60 projections, circular orbit of 360°, rotation radiusof 24 cm, and 30 s per view. The SPECT data were reconstructed by an ordered subset
conjugate gradient minimizer using the xSPECT Quant algorithm. Low-dose CT images
were acquired for attenuation correction after SPECT image acquisition. The CT scanning parameters were: 130 kV, 20 mA with CARE Dose 4D, 16 × 1.2 mm collimation,
0.6-s rotation, and pitch, 1.5. Multiple-energy window scatter and CT-based attenuation
correction was automated.

SUVs and WRs in SPECT‑CT images and left ventricular segmentation

We manually segmented entire left ventricle into regions of interest (ROI) in early anddelayed SPECT-CT images as described (Saito et al. 2023). Two nuclear medicine specialists operated the SPECT and X-ray CT devices, and fusion imaging was applied
to evaluate left ventricular morphology and [­123I]MIBG activity, and to determine theboundaries of the left ventricle. Accumulation in the liver was clearly separated fromthe left ventricle for all segmentation processes. Figure 1 shows an example of 3D left

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Fig. 1 Uptake of ­[123I]MIBG and mean SUVs. Three-dimensional manual segmentation of left ventricle
using ­[123I]MIBG SPECT-CT imaging. Normal A and reduced B ­[123I]MIBG uptake in 83- and 75-year-old
female patients with cognitive dysfunction and autonomic neuropathy, respectively. Mean SUVs calculated
from VOIs are 4.91 (A), and 1.01 (B). Red and green lines in VOI mark borders of manual 3D-segmented
left ventricle. 3D three-dimensional, CT computed tomography, SPECT single photon emission computed
tomography, SUV standardized uptake value, VOI voxel of interest

ventricle segmentation. We calculated SPECT-CT ­SUVmean using the SUV that reflected
the normalized concentration of tissue radioactivity relative to the dose and weight of
the injected radioactive tracer. This value represents the average SUV of voxels withinvolumes of interest (VOI) that included the complete left ventricle.
The SPECT-CT WRs were calculated based on ­SUVmean values as:
SPECT-CT WR = early SUVmean − delayed SUVmean /early SUVmean × 100 (%).
HMRs and WRs in planar images

Early and delayed HMRs were computed using semi-automatic SmartMIBG software(PDRadiopharma, Inc., Tokyo, Japan) for the planar images while ROIs were being established. (Okuda et al. 2011). The software algorithm uses a circular heart ROI and a mediastinal ROI with a 10% body width and a 30% mediastinum height. After the heart centeris adjusted, the entire procedure is automated, with manual correction as required.
Planar WRs with time-decay corrections from early and delayed heart ­(Hearly and­Hdelayed) and mediastinal ­(Mearly and ­Mdelayed) counts were calculated as:
Planar WR =


 
 


Hearly − Mearly − Hdelayed −Mdelayed /0.5(t/13) / Hearly −Mearly × 100(%),

Saito et al. EJNMMI Reports (2024) 8:15

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where t is the elapsed time (hours) between early and delayed image acquisition (Flotats
et al. 2010).
Quantitative analysis of SPECT‑CT and conventional planar images

We analyzed correlations between SPECT-CT ­SUVmean and planar HMRs in early anddelayed images and between WRs derived from SPECT-CT S­ UVmean and planar images.
Based on standard values extracted from the JSNM working group databases (n = 62),
the threshold for differentiating normal from abnormal planar WR was 34.0% (Nakajima et al. 2018). We determined a SPECT-CT WR cutoff by evaluating correlations
between SPECT-CT and planar WRs, then used it to categorize patients as normal or
abnormal. We subsequently assessed the degree of agreement between the methodologies. We assigned the participants (n = 49) to four groups based on clinical diagnoses of:
Alzheimer’s disease, depression, cerebral hemorrhage, cerebral palsy, and somatoform
disorders (control; n = 6), MSA (n = 7), PSP (n = 17), and PD/DLB (n = 19). Figure 2
shows the flow of patient classification into these distinct groups. The exclusion criteria included: patients with FAP (n = 7), Parkinson’s syndrome other than MSA and PSP(n = 7), unknown etiology (n = 4), amyotrophic lateral sclerosis (n = 1) or outpatientsreferred from other hospitals without diagnostic confirmation (n = 4). The S­ UVmeanand WR calculated from SPECT-CT images and HMRs and WR calculated from planar
images were compared among the four groups.
Statistics

All data were analyzed using JMP Pro version 17 (SAS Institute Inc., Cary, NC, USA).
Data are expressed as means and standard deviations (SD). Correlations between­SUVmean and HMR, and between SPECT-CT and planar WRs were assessed using t-tests

Eligible patients(n=72)

Excluded patients (n=23)- FAP (n=7)- Out-patients referred from other hospitals without
diagnostic confirmation (n=4)- Unknown etiology (n=4)

Included patients(n=49)

Control (n=6)

MSA (n=7)- ALS (n=1)

PSP (n=17)

PD/DLB (n=19)

Fig. 2 Flow-chart for classifying patients with neurological diseases into four groups. ALS Amyotrophic lateralsclerosis, DLB dementia with Lewy bodies, FAP familial amyloid polyneuropathy, MSA multiple system atrophy,
PD Parkinson’s disease, PSP progressive supranuclear palsy

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and two-way analysis of variance. Values were compared between groups using t-tests.
We assessed the diagnostic performance of MSA, PSP, and PD/DLB using SPECT-CTand planar images by the analysis of area under the receiver operating characteristics(ROC) curve (AUC). Values with P < 0.05 were considered significantly different.

Results
SPECT‑CT ­SUVmean versus planar HMR and SPECT‑CT WR versus planar WR

The ­SUVmean was calculated from early and delayed ­[123I]MIBG SPECT-CT images
acquired from all the patients. Figure 1 shows examples of manual 3D left ventricular­ UVmean and plasegmentation and the resulting S­ UVmean. We compared SPECT-CT S
123
nar HMR on early and delayed ­[ I]MIBG images. The early and delayed SPECT-CT­SUVmean significantly correlated with planar HMR ­(R2 = 0.69 and 0.82, p < 0.0001; Fig. 3).
We also compared SPECT-CT WRs using ­SUVmean and planar WR using conventional
methods. significantly correlated between planar and SPECT-CT WRs (­R2 = 0.79,
p < 0.0001; Fig. 4). We determined a 31% threshold for SPECT-CT WRs using planar
WR linear regression with a maximum standard deviation of 34% (Nakajima et al. 2018).
Normal and abnormal WRs that were subsequently classified based on the cutoff values
for SPECT-CT and planar WRs agreed in 67 (93.1%) of 72 patients (Table 2).
Comparison of SPECT‑CT and planar image parameter values between groups

Table 3 shows comparisons of ­[123I]MIBG SPECT-CT and planar parameters
between groups. Early and delayed S­ UVmean and WR in SPECT-CT images significantly differed between patients with PD/DLB and controls (p = 0.0006, p < 0.0001,
and p = 0.0003, respectively). The statistical significance of early and delayed HMR
and WR in planar images between patients with PD/DLB and controls was similar to that of the SPECT-CT parameters (p = 0.0153, p = 0.0021, and p = 0.0011,
respectively). Early and delayed ­SUVmean and WRs tended to differ more than planar parameters in patients with PSP compared with controls (p = 0.195, p = 0.0845,

A

B
SPECT-CT delayed SUVmean

SPECT-CT early SUVmean

7
6
5
4
3
2

R2 = 0.69, p < .0001

1

y = -1.08 + 1.74*x
1

2

3

Planar early HMR

4

5

7
6
5
4
3
2

R2 = 0.82, p < .0001

1

y = -1.18 + 1.50*x
1

2

3

4

Planar delayed HMR

Fig. 3 Correlations between ­SUVmean and HMR. Correlations between ­SUVmean derived from ­[123I]
MIBG SPECT-CT images using manual 3D left ventricle segmentation and HMR from planar images
using conventional methods. Early SPECT-CT-derived ­SUVmean versus planar early HMR (A); Delayed
SPECT-CT-derived ­SUVmean versus planar delayed HMR (B). Shaded area, confidence of fit. 3Dthree-dimensional, CT computed tomography, HMR heart-to-mediastinum, SPECT single photon emission
computed tomography, SUV standardized uptake value

5

Saito et al. EJNMMI Reports (2024) 8:15

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80

SPECT-CT WR (%)

60

40

20

0

-20

R2 = 0.79, p < .0001
y = 6.08 + 0.74*x
0

20

40

60

80

100

120

Planar WR (%)
Fig. 4 Correlations between WRs derived from ­[123I]MIBG SPECT-CT images and planar images. SPECT-CT WR
using ­SUVmean versus planar WR. Shaded area, confidence of fit. SPECT, single photon emission computed
tomography, SUV, standardized uptake value; WR, washout rate

Table 2 Washout rates derived from SPECT-CT images using 3D manual segmentation and planar
images using conventional method
Planar WR< 34%
SPECT-CT WR
Total

Total
≥ 34%

< 31%

40 (55.5%)

1 (1.4%)41 (56.9%)≥ 31%

4 (5.6%)27 (37.5%)

31 (43.1%)

44 (61.1%)

28 (38.9%)

72

SPECT-CT washout rates calculated from early and delayed ­SUVmean
3D three-dimensional, CT computed tomography, SPECT single photon emission computed tomography, WR washout rate

and p = 0.0644, respectively). The trends of early and delayed ­SUVmeans (p = 0.125
and p = 0.132, respectively) were similar between patients with MSA and controls.
The SPECT-CT parameters significantly differed between patients with PSP or MSA
and those with PD/DLB, except for early S­ UVmean between patients with MSA and
PD/DLB. Early and delayed HMRs and WRs significantly differed between planar
images of patients with PSP or MSA and those with PD/DLB. No significant differences were found between PSP and MSA. Diagnoses of MSA, PSP, and PD/DLB,ROC derived using planar and SPECT-CT parameters did not significantly differ.

Discussion
The present findings revealed a close correlation between planar imaging-based
quantitative and ­SUVmean values obtained from [­123I]MIBG SPECT-CT images ofpatients with neurological diseases. We confirmed that ­[123I]MIBG SPECT-CT
revealed consistently reduced uptake in patients with PD/DLB in general. Quantitative evaluation using ­[123I]MIBG SPECT-CT also helped to differentiate MSA andPSP from controls.

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Table 3 Comparisons groups using ­[123I]MIBG SPECT-CT and planar parameters
A. Mean ± SD of parameters

Early ­SUVmean

PD/DLBPSP

MSA

Control(n = 19)(n = 17)(n = 7)(n = 6)2.40 ± 1.19

3.83 ± 1.52

1.50 ± 1.02

2.96 ± 1.52

43.3 ± 15.4

28.3 ± 18.2

2.13 ± 0.61

2.81 ± 0.58
1.61‒3.86

1.48‒4.02

2.33‒3.61

1.88 ± 0.60

0.97 ‒2.86

2.78 ± 0.85

1.22 ‒4.23

2.21‒3.57

46.2 ± 21.9

28.4 ± 17.7

0.86‒5.09

Delayed ­SUVmean

0.29‒3.79

SPECT‒CT WR (%)

0.40‒5.58

20.7‒66.3

Early HMR

Planar WR (%)

3.19‒6.15

2.94 ± 1.21

2.69 ‒4.91

3.99 ± 0.81

20.0 ± 16.4

14.0 ± 4.34

2.9‒49.5

7.5‒20.2

2.90 ± 0.89

2.88 ± 0.47

3.04 ± 1.07

1.18‒4.33

16.5‒111.5

4.65 ± 1.00

1.01‒4.43
0.51‒3.87

5.2‒68.7

1.19‒3.29

Delayed HMR

3.52 ± 1.21

1.00‒5.58

3.04 ± 0.47

‒3.3‒63.1

0.5 ‒27.8

22.2 ± 21.6

8.8‒70.6

14.5 ± 10.3

B. Results of t tests (p values)
PD/DLBversusControl

PSP versus MSA versus PD/DLB
Control
Controlversus PSP

PD/DLBMSA versus PSP
versus MSA

Early ­SUVmean

0.0006

0.195

0.125

0.00190.0587

0.588

Delayed ­SUVmean< 0. 0001

0.08450.132

0.00090.01090.971

SPECT-CT WR (%)

0.00030.06440.503

0.00680.00180.249

Early HMR

0.0153

0.819

0.941

0.00250.0082

0.738

Delayed HMR

0.0021

0.485

0.998

0.00080.0012

0.462

Planar WR (%)

0.0011

0.138

0.482

0.009

0.0076

0.476

C. Area under the ROC curve (AUC) and p values obtained from ROC analysis
Early ­SUVmean

Early HMR

Delayed ­SUVmean

Delayed HMR

SPECT-CT WR

Planar WR

MSA

0.548

0.654

0.590

0.691

0.704

0.682

p

0.205

PSP

0.654

p

0.914

PD/DLB

0.804

p

0.796

0.128
0.660

0.631

0.560
0.643

0.792
0.790

0.822
0.624

0.570

0.580

0.781
0.840

0.821

0.809

0.710

Data are shown as means ± standard deviations with ranges

AUC​ area under the ROC curve, CT computed tomography, DLB dementia with Lewy bodies, HMR heart-to-mediastinum
ratio, MSA multiple system atrophy, PD Parkinson’s disease, PSP progressive supranuclear palsy, ROC receiver operating
characteristic, SD standard deviation, SPECT single photon emission computed tomography, SUVmean mean standardized
uptake value, WR washout rate

Absolute quantitation using SPECT‑CT

Absolute quantitation using 3D images could be one option for analyzing cardiac sympathetic images. Standard cardiac sympathetic imaging is not completely quantitative andis assessed by comparing the heart to other tissues. Semi-quantitative standard visualscoring systems that compare the heart to the mediastinum improves visual scoring but
relies on the extracardiac position as a reference area. In addition, anatomically overlapping organs on two-dimensional (2D) images are avoided on 3D images. Calculating theabsolute amount of myocardial uptake using quantitative SPECT might further improve

Saito et al. EJNMMI Reports (2024) 8:15

diagnostic performance by narrowing the gray zone in conventional 2D quantitativeevaluation. Nuclear medicine quantitation has progressed from simple thyroid uptake
measurements to PET-based kinetic and SUV analyses. The need for radionuclide therapy has primarily led to SPECT quantitation becoming more prevalent. The potential
applications of absolute SPECT-CT image quantitation such as myocardial perfusion,
99m
Tc-Pyrophosphate scintigraphy, and cardiac sympathetic nerve imaging to diagnoses
have been discussed (Ramsay and Cuscaden 2020; Dorbala et al. 2021; Lehner et al. 2022;
Nakajima et al. 2023; Saito et al. 2023). These applications should become more established in the field of nuclear medicine diagnostics.
Application of ­SUVmean

Activity normalization within an ROI is represented by SUVs, which comprise theamount of injected activity divided by that distributed within a patient. The significant
advantage of quantitative evaluation using SUVs is that the calculated values are independent of body weight. Standard uptake values vary. Here, we calculated SPECT-CT­SUVmean as the average SUV of voxels inside a VOI that comprised the entire left ventricle (Saito et al. 2023). The following might offer a possible explanation. The maximum
standardized uptake value ­(SUVmax) can serve as a quantitative measure when assessing­[123I]MIBG sympathetic images. However, ­SUVmax is derived from a single pixel rather
than an entire ROI. When ­SUVmax is calculated for ­[123I]MIBG sympathetic images of
the heart, this could result in significant deviation. The myocardial uptake of [­ 123I]MIBG
is variable and can be affected by inferior deficiencies or diffusely decreased uptake
with Lewy body disorders. Another reason for using ­SUVmean is that the connection iscloser to ­SUVmean than ­SUVmax compared with 2D quantitative values in patients withneurological and cardiac disorders (Saito et al. 2023). We identified a close correlation
between ­SUVmean and planar HMR in patients with neurological diseases. The correlation was also good for ­SUVmean and WRs. The ­SUVmean in ­[123I]MIBG sympathetic imaging could be a valuable quantitative indicator of myocardial accumulation. However, theaverage SUV depends on the definition of the ROI and might be affected by intra- andinter-observer variability.
Differentiation of PSP, MSA, and controls on cardiac sympathetic nerve images

We determined that quantifying sympathetic neural function in 3D SPECT-CT images
might be useful to differentiate patients with PSP and MSA from controls. Although 3D
evaluation found no significant differences, the S­ UVmean tended to be lower and the WR
tended to be higher in patients with PSP than controls, and was stronger than the conventional 2D findings (p = 0.195, p = 0.0845 and p = 0.0644, respectively; Table 3). A trend
towards lower early and delayed S­ UVmean values in patients with MSA versus controls was
also more obvious in 3D, than conventional 2D images (p = 0.125 and p = 0.132, respectively; Table 3). Planar images of patients with PSP have revealed slightly reduced MIBG
accumulation, which might be associated with brainstem atrophy (Kamada et al. 2019).
Although cardiac ­[123I]MIBG uptake is thought to be primarily maintained in MSA withprejunctional autonomic failure, the planar HMR was decreased compared with controls during the early and delayed phases in 5 and 4 of 9 patients with Parkinson’s variant of multiple system atrophy (MSA-P) (Kollensperger et al. 2007). Reduced ­[123I]MIBG

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accumulation in MSA might be partly explained by factors associated with Lewy bodypathology (Nagayama et al. 2010). The elimination of organ overlaps and mediastinal
dependence in 3D quantitative assessment might have resulted in a more precise reflection of sympathetic nerve function. Because the S­ UVmean is based on absolute quantitationand not ratios as in HMR, it has a wider range than conventional HMR values and could
be subdivided into normal, PSP, MSA, and PD/DLB. Currently, HMR derived from planar
images is the most popular measure of sympathetic nerve activity, whereas SPECT data
are considered supplementary. However, a diagnostic system that includes SPECT might
establish 3D quantitative assessment as the diagnostic standard. Moreover, data collection
by tomographic imaging is a standard procedure for semiconductor SPECT systems that do
not rely on traditional planar images. Consequently, direct use of SPECT images could be apractical alternative.
The future

We manually segmented the heart including the entire left ventricular cavity and the myocardium because our objective was to determine the contribution of quantified 3D values
to the diagnosis of neurological diseases. We therefore compared conventional 2D, with 3D
quantitative values using SPECT-CT. Isolating the myocardium using non-contrast X-ray
CT is challenging. The entire left ventricle was segmented because preserved and decreased
accumulation must be considered when [­123I]MIBG is used to assess sympathetic nerve
function. Subsequent investigations will require finding a way to separate and isolate the
myocardium from the left ventricular cavity. Since manual segmentation is complicatedand requires prolonged processing, integrating artificial intelligence with automatic 3D segmentation should significantly enhance convenience. Cardiac segmentation and quantitation by [­123I]MIBG SPECT has been based on a convolutional neural network (Saito et al.
2021). This technique should be extended to [­123I]MIBG SPECT-CT imaging using segmentation and quantitation automated by artificial intelligence.
Limitations

The possibility of unintentional selection bias could not be entirely excluded due to oursmall patient cohort and the retrospective, single-center study design. The current investigation is an initial attempt to assess whether quantitative 3D assessment of [­123I] MIBG
sympathetic images could differentiate neurological diseases based solely on their diagnoses. This study included consecutive cases with diverse backgrounds, and did not focus ona specific disorder. Our expert neurologist retrospectively determined the most likely neurological diagnoses based on clinical evaluations during hospital stays and outpatient visits. While this approach of consecutive inclusion might introduce some level of diagnostic
uncertainty, it reflects real-world clinical practice. Prospective, large-scale investigations areneeded to validate the diagnostic accuracy of ­[123I]MIBG scintigraphy using SPECT/CT toaccount for disease progression and severity.

Conclusions
Absolute cardiac counts and SUVs significantly correlated between [­ 123I]MIBG SPECTCT and conventional quantitative planar images of neurological diseases. This outcome
indicated that a differential diagnosis between controls and patients with PSP and MSA

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Saito et al. EJNMMI Reports (2024) 8:15

would require 3D quantitative evaluation using [­123I]MIBG SPECT-CT. Absolute 3D
quantitation of sympathetic innervation is a viable approach that could be integrated
into methods to quantify cardiac sympathetic activity.
Abbreviations
AUC​	Area under the ROC curve
DLB	Dementia with Lewy bodies
HMR	Heart-to-mediastinum ratio
MIBG	Metaiodobenzylguanidine
MSA	Multiple system atrophy
PD	Parkinson’s diseasePS	Parkinson’s syndrome
SPECT	Single-photon emission computed tomography
ROC	Receiver operating characteristic
SUV	Standardized uptake value
WR	Washout rates
Acknowledgements
The authors appreciate Hiroto Yoneyama and Takahiro Konishi (Kanazawa University Hospital, Kanazawa, Japan) for technical assistance. We also thank Norma Foster (English Express, North Vancouver, BC, Canada) for editorial assistance.
Author contributions
SS and KN designed and summarized the study. JK classified the neurological diagnoses in accordance with the recommended criteria. TS provided technical support for SPECT-CT data acquisition. SS statistically analyzed the data, and KN
confirmed the statistical outcomes. SS drafted, and KN, JK, HW, and HM revised the manuscript. AT acquired the clinicaldata. KO and SK supervised the study. All authors read and approved the final manuscript.
Funding
This study was partly funded by a G-7 Scholarship Foundation and JSPS Grant-in-Aid for Early-Career Scientists in Japan(PI: S. Saito, No. 22K15801) and for Scientific Research (C) in Japan (PI: K. Nakajima, No. 20K07990 and 23K07203).
Availability of data and materials
Without authorization from the Ethics Committees of Kanazawa University, the image datasets generated or analyzed inthis research are not disclosed publicly. However, the corresponding author may provide them upon reasonable request.

Declarations
Ethics approval and consent to participate
The Ethics Committee at Kanazawa University approved this retrospective study of data, and waived the need for written
informed consent from the patients.
Consent for publication
Informed consent including publication was obtained from all patients in the form of opt-out.
Competing interests
KN is in a research collaboration with PDRadiopharma, Inc. Tokyo, Japan, which supplied the [­ 123I]MIBG. All other authors
declare that they have no competing interests.
Received: 29 February 2024 Accepted: 18 April 2024
Published: 1 June 2024

References
Bateman TM, Ananthasubramaniam K, Berman DS, Gerson M, Gropler R, Henzlova M et al (2019) Reliability of the­[123I]mIBG heart/mediastinum ratio: results of a multicenter test-retest reproducibility study. J Nucl Cardiol
26(5):1555–1565
Chen J, Folks RD, Verdes L, Manatunga DN, Jacobson AF, Garcia EV (2012) Quantitative I-123 mIBG SPECT in differentiating
abnormal and normal mIBG myocardial uptake. J Nucl Cardiol 19(1):92–99
Dorbala S, Park MA, Cuddy S, Singh V, Sullivan K, Kim S et al (2021) Absolute quantitation of cardiac [­ 99mTc]Pyrophosphate using cadmium–zinc–telluride-based SPECT/CT. J Nucl Med 62(5):716–722
Flotats A, Carrio I, Agostini D, Le Guludec D, Marcassa C, Schafers M et al (2010) Proposal for standardization of [­ 123I]
metaiodobenzylguanidine (MIBG) cardiac sympathetic imaging by the EANM cardiovascular committee and theEuropean council of nuclear cardiology. Eur J Nucl Med Mol Imaging 37(9):1802–1812
Gilman S, Wenning GK, Low PA, Brooks DJ, Mathias CJ, Trojanowski JQ et al (2008) Second consensus statement on the
diagnosis of multiple system atrophy. Neurology 71(9):670–676
Hoglinger GU, Respondek G, Stamelou M, Kurz C, Josephs KA, Lang AE et al (2017) Clinical diagnosis of progressive supranuclear palsy: the movement disorder society criteria. Mov Disord 32(6):853–864

Page 11 of 12

Saito et al. EJNMMI Reports (2024) 8:15

Kamada T, Miura S, Kida H, Irie KI, Yamanishi Y, Hoshino T et al (2019) MIBG myocardial scintigraphy in progressive supranuclear palsy. J Neurol Sci 396:3–7
Klene C, Jungen C, Okuda K, Kobayashi Y, Helberg A, Mester J et al (2018) Influence of ROI definition on the heart-tomediastinum ratio in planar [­ 123I]MIBG imaging. J Nucl Cardiol 25(1):208–216
Kollensperger M, Seppi K, Liener C, Boesch S, Heute D, Mair KJ et al (2007) Diffusion weighted imaging best discriminates
PD from MSA-P: a comparison with tilt table testing and heart MIBG scintigraphy. Mov Disord 22(12):1771–1776
Lehner S, Nowak I, Zacherl M, Brosch-Lenz J, Fischer M, Ilhan H et al (2022) Quantitative myocardial perfusion SPECT/CT
for the assessment of myocardial tracer uptake in patients with three-vessel coronary artery disease: initial experiences and results. J Nucl Cardiol 29(5):2511–2520
McKeith IG, Boeve BF, Dickson DW, Halliday G, Taylor JP, Weintraub D et al (2017) Diagnosis and management of dementia with Lewy bodies: fourth consensus report of the DLB Consortium. Neurology 89(1):88–100
Nagayama H, Ueda M, Yamazaki M, Nishiyama Y, Hamamoto M, Katayama Y (2010) Abnormal cardiac [­ 123I]meta-iodobenzylguanidine uptake in multiple system atrophy. Mov Disord 25(11):1744–1747
Nakajima K, Nakata T (2015) Cardiac ­[123I]MIBG imaging for clinical decision making: 22-year experience in Japan. J Nucl
Med 56(Suppl 4):11S-19S
Nakajima K, Yamada M (2016) [­ 123I]Meta-iodobenzylguanidine sympathetic imaging: standardization and application toneurological diseases. Chonnam Med J 52(3):145–150
Nakajima K, Scholte A, Nakata T, Dimitriu-Leen AC, Chikamori T, Vitola JV et al (2017) Cardiac sympathetic nervous system
imaging with [­ 123I]meta-iodobenzylguanidine: perspectives from Japan and Europe. J Nucl Cardiol 24(3):952–960
Nakajima K, Okuda K, Matsuo S, Wakabayashi H, Kinuya S (2018) Is [­ 123I]metaiodobenzylguanidine heart-to-mediastinum ratio dependent on age? From Japanese society of nuclear medicine normal database. Ann Nucl Med
32(3):175–181
Nakajima K, Shibutani T, Massanes F, Shimizu T, Yoshida S, Onoguchi M et al (2023) Myocardial perfusion imaging withretrospective gating and integrated correction of attenuation, scatter, respiration, motion, and arrhythmia. J Nucl
Cardiol 30(6):2773–2789
Odagiri H, Baba T, Nishio Y, Iizuka O, Matsuda M, Inoue K et al (2016) On the utility of MIBG SPECT/CT in evaluating cardiac
sympathetic dysfunction in lewy body diseases. PLoS One. https://​doi.​org/​10.​1371/​journ​al.​pone.​01527​46
Okuda K, Nakajima K, Hosoya T, Ishikawa T, Konishi T, Matsubara K et al (2011) Semi-automated algorithm for calculatingheart-to-mediastinum ratio in cardiac Iodine-123 MIBG imaging. J Nucl Cardiol 18(1):82–89
Owenius R, Zanette M, Cella P (2017) Variability in heart-to-mediastinum ratio from planar [­ 123I]MIBG images of a thorax
phantom for 6 common gamma-camera models. J Nucl Med Technol 45(4):297–303
Postuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W et al (2015) MDS clinical diagnostic criteria for Parkinson’s
disease. Mov Disord 30(12):1591–1601
Ramsay SC, Cuscaden C (2020) The current status of quantitative SPECT/CT in the assessment of transthyretin cardiac
amyloidosis. J Nucl Cardiol 27(5):1464–1468
Saito S, Nakajima K, Edenbrandt L, Enqvist O, Ulen J, Kinuya S (2021) Convolutional neural network-based automatic heart
segmentation and quantitation in [­ 123I]metaiodobenzylguanidine SPECT imaging. EJNMMI Res 11(1):105
Saito S, Nakajima K, Shibutani T, Wakabayashi H, Yoneyama H, Konishi T et al (2023) Three-dimensional heart segmentation and absolute quantitation of cardiac [­ 123I]metaiodobenzylguanidine sympathetic imaging using SPECT/CT.
Ann Nucl Cardiol 9(1):61–67
Travin MI, Henzlova MJ, van Eck-Smit BLF, Jain D, Carrio I, Folks RD et al (2017) Assessment of [­ 123I]mIBG and [­ 99mTc]tetrofosmin single-photon emission computed tomographic images for the prediction of arrhythmic events in patientswith ischemic heart failure: intermediate severity innervation defects are associated with higher arrhythmic risk. J
Nucl Cardiol 24(2):377–391
Verschure DO, Nakajima K, Verberne HJ (2022) Cardiac ­[123I]mIBG imaging in heart failure. Pharmaceuticals (basel)
15(6):656
Yamada M, Komatsu J, Nakamura K, Sakai K, Samuraki-Yokohama M, Nakajima K et al (2020) Diagnostic criteria for dementia with lewy bodies: updates and future directions. J Mov Disord 13(1):1–10

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