Improved Accuracy of Amyloid PET Quantification with Adaptive Template–Based Anatomic Standardization

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FIGURE 1.
Workflow of PET-only quantitative evaluation method. First, PET images are anatomically standardized to either template using positive-template method, negative-template method, or adaptive-template method. Second, transformation vector used for standardization is calculated. Third, empirical PiB-prone region of interest (EPP-ROI) is inverse-transformed to individual PET image using transformation vector.
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FIGURE 2.
NCC results. *P < 0.05.
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FIGURE 3.
mcSUVR results. *P < 0.05.
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FIGURE 4.
mcSUVR receiver-operating-characteristic curves for each template. Areas under curve for positive template–based method, negative template–based method, and adaptive template–based method were 0.806, 0.801, and 0.815, respectively.

TABLE 1

Participant Characteristics

Characteristic	HC	MCI	AD
Sex (n)
Male	30	30	21
Female	28	32	25
Age (y)
Mean ± SD	66.4 ± 4.5	71.4 ± 5.5	74.4 ± 6.3
Range	60–80	60–82	62–84
NINCDS-ADRDA	—	—	Probable AD
MMSE-J
Mean ± SD	29.3 ± 1.1	26.7 ± 1.8	22.2 ± 1.8
Range	24–30	24–30	20–26
CDR-J	0	0.5	0.5 or 1.0
WMS-R	Above cutoff	Below cutoff	Below cutoff
Visually positive (n)	14	41	43
Visually negative (n)	44	21	3

NINCDS-ADRDA = National Institute of Neurologic and Communicative Disorders and Alzheimer’s Disease and Related Disorders Association.

TABLE 2

PET Scanners and Reconstruction Parameters for ¹¹C-PiB PET in J-ADNI Study

Scanner vendor	Scanner model	Algorithm	Iterations	Subsets
GE Healthcare	Advance	Iterative (FORE + OSEM)	6	16
	Discovery ST Elite	Iterative (VUE Point plus)	2	40
Shimadzu	Eminence Sophia G/X	FORE + DRAMA	4	NA
	Eminence Sophia B/L	FORE + DRAMA	4	NA
	Eminence G/X	FORE + DRAMA	4	NA
	Headtome V	Iterative (FORE + OSEM)	4	16
Siemens	ECAT Accel	Iterative (FORE + OSEM)	6	16
	ECAT Exact HR+	Iterative (FORE + OSEM)	4	16
	Biograph 6	Iterative (FORE + OSEM)	4	16
	Biograph 16	Iterative (FORE + OSEM)	4	14

FORE = Fourier rebinning; OSEM = ordered-subsets expectation maximization; NA = not available; DRAMA = dynamic row-action maximum-likelihood algorithm.

TABLE 3

Visual Evaluation of Participants

Visual evaluation	Clinical diagnosis	No. of participants		No. of images
Visual evaluation	Clinical diagnosis	No. of participants	Positive template	Negative template	Adaptive template
Positive	HC	14	58	0	7
	MCI	41	62	0	35
	AD	43	46	0	38
	Total	98	166	0	80
Negative	HC	44	0	58	51
	MCI	21	0	62	27
	AD	3	0	46	8
	Total	68	0	166	86
Concordance rate			59.0%	41.0%	89.2%
Coefficient of association			not	not	0.80