Fast and low-dose medical imaging generation empowered by hybrid deep-learning and iterative reconstruction

Abstract

Fast and low-dose reconstructions of medical images are highly desired in clinical routines. We propose a hybrid deep-learning and iterative reconstruction (hybrid DL-IR) framework and apply it for fast magnetic resonance imaging (MRI), fast positron emission tomography (PET), and low-dose computed tomography (CT) image generation tasks. First, in a retrospective MRI study (6,066 cases), we demonstrate its capability of handling 3- to 10-fold under-sampled MR data, enabling organ-level coverage with only 10- to 100-s scan time; second, a low-dose CT study (142 cases) shows that our framework can successfully alleviate the noise and streak artifacts in scans performed with only 10% radiation dose (0.61 mGy); and last, a fast whole-body PET study (131 cases) allows us to faithfully reconstruct tumor-induced lesions, including small ones (<4 mm), from 2- to 4-fold-accelerated PET acquisition (30-60 s/bp). This study offers a promising avenue for accurate and high-quality image reconstruction with broad clinical value.

Keywords: deep learning; fast MRI; fast PET; iterative reconstruction; low-dose CT.

Graphical abstract

Figure 1

Schematics of general hybrid deep-learning and iterative reconstruction (hybrid DL-IR) The acquired data (left), reconstruction methods (middle), and reconstructed images (right) are shown for fast MRI, low-dose CT, and fast PET, respectively.

Figure 2

AI-assisted compressed-sensing (ACS) reconstruction performance under different acceleration factors (A) The schematics of ACS reconstruction framework. (B) Representative T2w head images reconstructed from fully sampled k-space data from the testing dataset (left) and from 3× down-sampled k-space data using ACS, PI, AI, PI + AI, MoDL, and E2E-VarNet methods. (C) Six quantitative metrics (i.e., MSE, NMSE, NRMSE, SNR, PSNR, and SSIM) of reconstructed images from six methods (i.e., ACS, PI, AI, PI + AI, MoDL, and E2E-VarNet) under different acceleration factors for testing data. See also Tables S2 and S3.

Figure 3

ACS reconstruction performance under different acceleration factors in the external validation dataset (A) Representative T1w FLAIR and T2w FLAIR head images reconstructed from fully sampled k-space data from the external validation dataset and from down-sampled k-space data with acceleration factors of 2, 3, and 4 using ACS and PI methods. (B) NRMSE of ACS- and PI-reconstructed images under different acceleration factors for external validation dataset. Statistical analyses are performed using paired t tests (n = 78), ∗∗∗p < 0.001. Significant differences are observed in all acceleration factors. See also Table S4.

Figure 4

ACS reconstruction for 100-s-level MRI scans and single-breath-hold MRI scans (A and B) Representative images of the head (A) and knee (B) reconstructed by ACS (at a 100-s level) and PI using four pulse sequences. (C and D) Reconstruction of the chest MR images by ACS with data acquired in a single breath hold and by PI with data acquired in three breath holds at the transversal (C) and sagittal (D) sections. The red circle in (C) labels a focal lesion, which is missed in the three-breath-hold acquisition reconstructed by PI while being successfully captured in the single-breath-hold acquisition reconstructed by ACS. The reference in (D) is acquired with a spoiled gradient echo sequence in a single breath hold. Red circles highlight the focal lesions in the liver. See also Figure S3.

Figure 5

Deep IR reconstruction for low-dose CT scans (A) The schematics of deep IR reconstruction framework. (B) The ROC curves of three models, including FBP with reference dose, FBP with a lower dose, and deep IR with a lower dose. (C–E) Representative images reconstructed by FBP and deep IR at 8.00 mGy (40% of a normal dose) (C) and ultra-low doses of 0.67 (D) and 0.61 mGy (E). (F) Mean opinion score from two radiologists evaluating the chest and abdomen parts of the images. Statistical analyses are performed between R1 and R2 using Mann-Whitney U tests with ∗∗∗∗p < 0.0001.

Figure 6

HYPER DPR reconstruction for fast PET scans (A) Schematics of HYPER DPR. (B) Representative body images reconstructed by OSEM and DPR with varied acquisition times. The locations of small lesions are highlighted by red circles. (C and D) The SUV_max of the identified lesions (C) and SNR in the liver (D) for different methods. Statistical analyses on SUV_max are performed using repeated measures one-way ANOVA followed by Turkey’s multiple comparisons tests (n = 78). Statistical analyses on SNR are performed using Friedman tests followed by Dunnett’s multiple comparisons tests (n = 51). Asterisk represents two-tailed adjusted p value, with ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001. See also Figure S6 and Table S5.