Image-based iterative compensation of motion artifacts in computed tomography

Motion estimation and correction in SPECT, PET and CT

Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and X-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion, but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art machine learning methods may have a unique role to play in this context.

Periradicular infiltration of the lumbar spine: is iterative reconstruction software necessary to establish ultra-low-dose protocols? A quantitative and qualitative approach

PURPOSE

Computed tomography (CT)-guided periradicular infiltration therapy has emerged as an effective treatment option for patients with low back pain. Concern about radiation exposure requires approaches allowing significant dose reduction. The purpose of this study is to evaluate the need for iterative reconstruction software in CT-guided periradicular infiltration therapy with an ultra-low-dose protocol.

MATERIALS AND METHODS

One hundred patients underwent CT-guided periradicular infiltration therapy of the lumbar spine using an ultra-low-dose protocol with adaptive iterative dose reduction 3D (AIDR 3D) for image reconstruction. In addition, images were reconstructed with filtered back-projection (FBP). Four experienced raters evaluated both reconstruction types for conspicuity of anatomical and instrumental features important for ensuring safe patient treatment. Image noise was measured as a quantitative marker of image quality.

RESULTS

Interrater agreement was good for both AIDR 3D (Kendall's W = 0.83) and FBP (0.78) reconstructions. Readers assigned the same scores for all features and both reconstruction algorithms in 81.3% of cases. Image noise was significantly lower (average SD of 60.07 vs. 99.54, p < 0.05) for AIDR 3D-reconstructed images.

CONCLUSION

Although it significantly lowers image noise, iterative reconstruction software is not mandatory to achieve adequate image quality with an ultra-low-dose CT protocol for guiding periradicular infiltration therapy of the lumbar spine.