Evaluation of Abdominal CT Obtained Using a Deep Learning-Based Image Reconstruction Engine Compared with CT Using Adaptive Statistical Iterative Reconstruction

Yeo Jin Yoo; In Young Choi; Suk Keu Yeom; Sang Hoon Cha; Yunsub Jung; Hyun Jong Han; Euddeum Shim

doi:10.5334/jbsr.2638

Figures & Tables

This figure shows (a) noise power spectrum (NPS) and (b) task-based transfer function (TTF) measurement. **(a)** The red cross represents the center of the phantom section, and the blue circle represents the same distance from the center (red cross). The yellow square represents a voxel of interest (25.78 × 25.78 × 12.50 mm) measuring NPS. **(b)** TTF was measured in the region of interest (ROI)1 (bone, 955 HU) and ROI 2 (acrylic, 120 HU) cylinder robs.

Table 1

Baseline Characteristics of study population.

Demographics
Age (years)	54.4 ± 20.6
Body mass index	23.1 ± 3.6
Radiation dose
CTDIvol (mGy)	5.06 ± 1.85
DLP (mGycm)	281.29 ± 92.69

[i] Data are presented as mean±standard deviation.

CTDIvol, volume CT dose index; DLP, dose-length product.

CT images for quantitative analysis of liver **(a)** AV 30 **(b)** AV 50 **(c)** DLIR-L **(d)** DLIR-M **(e)** DLIR-H. The body mass index of this patient is 34.3.
FBP, filtered back projection; AV, adaptive statistical iterative reconstruction; DLIR, deep learning-based image reconstruction; DLIR-L, DLIR images with low levels; DLIR-M, DLIR with medium levels.

Table 2

Peaks, average spatial frequencies, area under NPS curve in all reconstructions and doses.

	NPS PEAK (HU²MM²)
CTDI_VOL(mGy)	FBP	AV30	AV50	AV100	DLIR-L	DLIR-M	DLIR-H
2.1	1.31	0.88	0.73	0.45	0.68	0.48	0.32
4.2	0.75	0.54	0.45	0.29	0.37	0.27	0.2
6.3	0.48	0.36	0.30	0.21	0.24	0.19	0.14
8.4	0.35	0.28	0.24	0.16	0.18	0.14	0.10
10.5	0.31	0.25	0.22	0.16	0.17	0.14	0.11
	NPS AVERAGE SPATIAL FREQUENCY (MM^–1)
CTDI_VOL(mGy)	FBP	AV30	AV50	AV100	DLIR-L	DLIR-M	DLIR-H
2.1	0.38	0.31	0.27	0.18	0.34	0.33	0.31
4.2	0.36	0.32	0.29	0.19	0.35	0.34	0.33
6.3	0.37	0.33	0.29	0.19	0.35	0.34	0.32
8.4	0.36	0.32	0.29	0.19	0.35	0.34	0.33
10.5	0.36	0.34	0.31	0.20	0.37	0.36	0.34
	NPS AUC
CTDI_VOL(mGy)	FBP	AV30	AV50	AV100	DLIR-L	DLIR-M	DLIR-H
2.1	178.9	114.8	80.5	27.2	91.8	66	42.7
4.2	99.5	64.8	46.2	17.7	49.2	35.9	24
6.3	66.7	43.2	31	12.4	33.1	24.5	16.6
8.4	49.6	32.5	23.4	9.4	24.1	17.6	11.7
10.5	42	27.9	20.4	8.9	21.6	16.2	11.2

[i] FBP, filtered back projection; AV30, and AV50 = ASIR-V with a blending factor of 30% and 50%, respectively; DLIR-L, DLIR-M, and DLIR-H, a deep learning-based image reconstruction with low, medium, or high levels, respectively; NPS, noise power spectrum; AUC, area under the curve.

NPS results at different doses and image reconstruction methods: peak kilovoltage (kVp), 100; tube current modulation range 50 mAs **(a)**, 100 mAs **(b)**, 150 mAs **(c)**, and 200mAs **(d)**.
FBP, filtered back projection; AV30, and AV50 = ASIR-V with a blending factor of 30% and 50%, respectively; DLIR-L, DLIR-M, and DLIR-H, a deep learning-based image reconstruction with low, medium, or high levels, respectively; NPS, noise power spectrum.

Table 3

TTF-50s (mm-1) of the 25% ACR phantom CT according to different discs (bone; 955 HU, acrylic; 120 HU) and reconstructions.

CTDI_VOL	TTF₅₀ (MM^–1) of ROI1 (BONE)							TTF₅₀ (MM^–1) of ROI2 (ACRYLIC)
(mGy)	FBP	AV30	AV50	AV100	DLIR-L	DLIR-M	DLIR-H	FBP	AV30	AV50	AV100	DLIR-L	DLIR-M	DLIR-H
2.1	0.45	0.35	0.45	0.44	0.45	0.44	0.44	0.36	0.35	0.35	0.28	0.40	0.40	0.40
4.2	0.44	0.44	0.44	0.45	0.44	0.44	0.44	0.42	0.41	0.41	0.39	0.44	0.43	0.44
6.3	0.44	0.44	0.44	0.45	0.44	0.44	0.44	0.36	0.35	0.35	0.36	0.37	0.38	0.41
8.4	0.44	0.44	0.44	0.45	0.44	0.44	0.44	0.40	0.39	0.42	0.39	0.44	0.42	0.43
10.5	0.45	0.45	0.45	0.45	0.45	0.44	0.44	0.41	0.42	0.41	0.38	0.44	0.44	0.42

[i] TTF, task-based transfer function; ACR, American College of Radiology; FBP, filtered back projection; AV30, and AV50 = ASIR-V with blending factors of 30%, and 50%, respectively; DLIR-L, DLIR-M, and DLIR-H, a deep learning-based image reconstruction with low, medium, or high levels, respectively.

Table 4

Mean image noise (HU) according to the image reconstruction method.

RECONSTRUCTION	FBP	AV30	AV50	DLIR-L	DLIR-M	DLIR-H	P-VALUE
Liver
HU	130.46 ± 22.91	130.46 ± 22.91	130.47 ± 22.91	130.63 ± 22.85	130.74 ± 22.86	130.76 ± 22.86	1.000
SD	25.65 ± 1.81	20.03 ± 1.51	16.36 ± 1.34	18.43 ± 1.56	14.40 ± 1.26	10.05 ± 1.00 ^a	<.001
Aorta
HU	206.21 ± 50.56	206.47 ± 50.08	206.43 ± 50.07	208.01 ± 50.11	208.11 ± 50.07	206.47 ± 50.08	1.000
SD	27.01 ± 2.51	20.72 ± 2.10	16.69 ± 1.91	19.41 ± 1.97	15.13 ± 1.52	10.50 ± 1.30	<.001
Fat
HU	107.59 ± 17.71	107.51 ± 17.73	107.49 ± 17.71	106.06 ± 19.58	106.79 ± 17.55	106.58 ± 17.52	1.000
SD	22.56 ± 2.10	17.88 ± 1.77	14.88 ± 1.64	14.82 ± 1.54	11.31 ± 1.32	7.56 ± 1.18	<.001

[i] Data are presented as mean ± standard deviation. The subscripts represent the same group of post hoc analysis (alphabetical order indicates the order, starting from the lowest mean value). P-values were calculated using repeated-measures ANOVA among the six groups.

FBP, filtered back projection; AV30, ASIR-V with a blending factor of 30%; AV50, ASIR-V with a blending factor of 50%; DLIR-L, DLIR-M, and DLIR-H, deep learning-based image reconstruction images with low, medium, or high strength levels, respectively; HU, Hounsfield unit; SD, standard deviation.

Table 5

Image quality assessment ranking of the image reconstruction methods.

RECONSTRUCTION	AV30	AV50	DLIR-L	DLIR-M	DLIR-H
Overall image quality	1.93 ± 1.1	1.63 ± 0.78	4.04 ± 0.76	4.51 ± 0.75	2.89 ± 0.84
Noise	1.18 ± 0.39	1.83 ± 0.40	2.99 ± 0.09	4.00 ± 0.00	5.00 ± 0.00
Spatial resolution	2.18 ± 0.67	1.27 ± 0.72	4.67 ± 0.57	4.19 ± 0.60	2.69 ± 0.63±

[i] Data are mean ranking score ± standard deviation.

FBP, filtered back projection; AV30, ASIR-V with a blending factor of 30%; AV50, ASIR-V with a blending factor of 50%; DLIR-L, DLIR-M, and DLIR-H, a deep learning-based image reconstruction image with low, medium, or high strength levels.

Evaluation of Abdominal CT Obtained Using a Deep Learning-Based Image Reconstruction Engine Compared with CT Using Adaptive Statistical Iterative Reconstruction

Figures & Tables

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