Respiratory lung motion analysis using a nonlinear motion correction technique for respiratory-gated lung perfusion SPECT images

Ue, Hidenori; Haneishi, Hideaki; Iwanaga, Hideyuki; Suga, Kazuyoshi

doi:10.1007/s12149-007-0005-3

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…This lung motion is natural according to the general analysis of lung motions. [9][10][11] Using the reference landmark point pairs and the estimated displacement vectors, the distances between the corresponding landmarks were measured before registration, and after applying affine registration and three different deformable registrations ͑TGF, CGFfixed, and CGFadapted͒. Among three methods, the proposed method, CGFadapted, showed the shortest error distance, while the distance of TGF was the longest.…”

Section: Ivc Resultsmentioning

confidence: 99%

“…Lung registration between computed tomography ͑CT͒ scans acquired at different breathing phases is essential for clinical applications such as treatment planning for lung tumors, 1-5 quantitative assessments of obstructive lung disease, [6][7][8] and lung motion analyses. [9][10][11] Rigid or affine registration can model translational mismatches and global expansion of the lungs. However, it does not have enough degrees of freedom to model the local deformation of the lungs during respiration.…”

Section: Introductionmentioning

confidence: 99%

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Deformable lung registration between exhale and inhale CT scans using active cells in a combined gradient force approach

2010

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Section: Ivc Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deformable lung registration between exhale and inhale CT scans using active cells in a combined gradient force approach

2010

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“…Motion correction of respiratory-gated SPECT images of the lung has also been reported. End-exhalation and end-inspiration respiratory gated 99 m -Tc-MAA lung perfusion images may be non-rigidly registered and added together to obtain a less noisy image (Ue et al 2006(Ue et al , 2007. Cardiac-gated SPECT acquisition, synchronized with the cardiac cycle via an ECG signal, produces a set of images of the myocardium at different phases of the cardiac cycle, each with reduced motion artifacts (but increased noise) compared to the ungated image.…”

Section: Spectmentioning

confidence: 99%

Motion estimation and correction in SPECT, PET and CT

Kyme¹,

Fulton²

2021

Phys. Med. Biol.

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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 deep learning methods may have a unique role to play in this context.

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“…In particular, significant research has focused on the registration of different phases of the breathing cycle from four dimensional CT images (4DCT) or full inhale/exhale breath-hold CT image volumes. In the context of radiotherapy, estimated respiratory motion maps have been applied to evaluate or improve treatment delivery strategies (Guerrero, G. Zhang, et al 2005; Boldea et al 2008) or to estimate local ventilation distributions (Guerrero, Sanders, et al 2006; Ue et al 2007; Reinhardt et al 2008; Ding et al 2010; Kipritidis et al 2015). A very limited number of studies involved the registration of CT images potentially showing RT response.…”

Section: Introductionmentioning

confidence: 99%

Biomechanical deformable image registration of longitudinal lung CT images using vessel information

et al. 2016

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Spatial correlation of lung tissue across longitudinal images, as the patient responds to treatment, is a critical step in adaptive radiotherapy. The goal of this work is to expand a biomechanical model-based deformable registration algorithm (Morfeus) to achieve accurate registration in the presence of significant anatomical changes. Six lung cancer patients previously treated with conventionally fractionated radiotherapy were retrospectively evaluated. Exhale CT scans were obtained at treatment planning and following three weeks of treatment. For each patient, the planning CT was registered to the follow-up CT using Morfeus, a biomechanical model-based deformable registration algorithm. To model the complex response of the lung, an extension to Morfeus has been developed: An initial deformation was estimated with Morfeus consisting of boundary conditions on the chest wall and incorporating a sliding interface with the lungs. It was hypothesized that the addition of boundary conditions based on vessel tree matching would provide a robust reduction of the residual registration error. To achieve this, the vessel trees were segmented on the two images by thresholding a vesselness image based on the Hessian matrix’s eigenvalues. For each point on the reference vessel tree centerline, the displacement vector was estimated by applying a variant of the Demons registration algorithm between the planning CT and the deformed follow-up CT. An expert independently identified corresponding landmarks well distributed in the lung to compute Target Registration Errors (TRE). The TRE was: 5.8±2.9, 3.4±2.3 and 1.6±1.3 mm after rigid registration, Morfeus and Morfeus with boundary conditions on the vessel tree, respectively. In conclusion, the addition of boundary conditions on the vessels significantly improved the accuracy in modeling the response of the lung and tumor over the course of radiotherapy. Minimizing and modeling these geometrical uncertainties will enable future plan adaptation strategies.

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Respiratory lung motion analysis using a nonlinear motion correction technique for respiratory-gated lung perfusion SPECT images

Abstract: A difference in the motion between normal lungs and lungs with a ventilation obstruction was detected by the proposed method. This method is effective for evaluating obstructive pulmonary diseases such as pulmonary emphysema and diffuse panbronchiolitis.

Cited by 7 publications

References 14 publications

Deformable lung registration between exhale and inhale CT scans using active cells in a combined gradient force approach

Deformable lung registration between exhale and inhale CT scans using active cells in a combined gradient force approach

Motion estimation and correction in SPECT, PET and CT

Biomechanical deformable image registration of longitudinal lung CT images using vessel information

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