Continuous Versus Sequential Acquisition Head Computed Tomography: A Phantom and Clinical Image Quality Comparative Study

Deep learning-based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms

Objectives

To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT).

Methods

Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired t tests.

Results

For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively.

Conclusions

With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times.

Key Points

• Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction.

• Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction.

• Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-020-07668-x.

Comparison of physical image qualities and artifact indices for head computed tomography in the axial and helical scan modes

This study aimed to validate the clinically demonstrated equivalency of the axial and helical scan modes (AS and HS, respectively) for head computed tomography (CT) using physical image quality measures and artifact indices (AIs). Two 64-row multi-detector row CT systems (CT-A and CT-B) were used for comparing AS and HSs with detector rows of 64 and 32. The modulation transfer function (MTF), noise power spectrum (NPS), and slice sensitivity profile were measured using a CT dose index corresponding to clinical use. The system performance function (SPF) was calculated as MTF²/NPS. The AI of streak artifacts in the skull base was measured using an image obtained of a head phantom, while the AI of motion artifacts was measured from images obtained during the head phantom was in motion. For CT-A, the 50%MTFs were 7% to 9% higher in the HS than the AS, and the higher MTFs of HS associated NPS increases. For CT-B, the MTFs and NPSs were almost equivalent between the AS and HS, respectively. Consequently, the SPFs of AS and HS were nearly identical for both CT systems. For both CT systems, the skull base AI did not differ significantly between AS and HS, while the motion AIs of HS were significantly better than of AS. The superior motion AI in the HS indicated the effectiveness of HS on moving patients.

Optimized computed tomography protocol for assessment of dentition in alpacas

OBJECTIVE To determine the optimal protocol for acquisition of CT images of the dentition in alpacas. ANIMALS 3 healthy adult male alpacas. PROCEDURES Each alpaca was anesthetized with an IM injection of a combination of ketamine, xylazine, and butorphanol and positioned in sternal recumbency on the CT couch with its legs folded in a natural cush position and its head positioned within the isocenter of the gantry of a 64-slice CT scanner. Images were acquired by means of 6 protocols (sequential and helical modes at slice thicknesses of 1.25, 2.5, and 5 mm). Five images (2 molar, 2 premolar, and mandibular incisor teeth) were selected from each protocol for evaluation by 3 veterinary radiologists. For each image, tooth root visibility and sharpness and image noise artifact were subjectively evaluated on a 3-point scoring system. RESULTS Slice thickness significantly affected tooth root visibility and tooth root sharpness but did not affect image noise artifact. Acquisition mode significantly affected tooth root visibility and tooth root sharpness as well as image noise artifact. Tooth root visibility and sharpness did not differ significantly between the helical and sequential images when the slice thickness was 1.25 mm. Image noise artifact was greater for helical images than sequential images but did not differ by slice thickness within either acquisition mode. CONCLUSIONS AND CLINICAL RELEVANCE Results indicated that for a 64-slice CT scanner, the optimal protocol for the acquisition of CT images of the dentition in alpacas was a sequential scan with a slice thickness of 1.25 mm.