Image Quality Analysis of Limited Angle Tomography Using the Shift-Variant Data Loss Model

Taubmann

et al. 2017

Preprint

Self Cite

This paper addresses streak reduction in limited angle tomography. Although the iterative reweighted total variation (wTV) algorithm reduces small streaks well, it is rather inept at eliminating large ones since total variation (TV) regularization is scale-dependent and may regard these streaks as homogeneous areas. Hence, the main purpose of this paper is to reduce streak artifacts at various scales. We propose the scale-space anisotropic total variation (ssaTV) algorithm in two different implementations. The first implementation (ssaTV-1) utilizes an anisotropic gradient-like operator which uses 2 • s neighboring pixels along the streaks' normal direction at each scale s. The second implementation (ssaTV-2) makes use of anisotropic down-sampling and up-sampling operations, similarly oriented along the streaks' normal direction, to apply TV regularization at various scales. Experiments on numerical and clinical data demonstrate that both ssaTV algorithms reduce streak artifacts more effectively and efficiently than wTV, particularly when using multiple scales.

“…As a side remark, the difference images (Figs. 7(d)-(f)) reveal that all three algorithms fail to recover the top boundary where most data is missing [43].…”

Section: A Numerical Resultsmentioning

confidence: 97%

“…Compared with the structures in the vertical direction (Fig. 11), those in the horizontal direction are relatively more difficult to reconstruct because no horizontal X-rays pass through the object in the 10 • − 170 • case [43].…”

Section: Discussionmentioning

confidence: 99%

Scale-Space Anisotropic Total Variation for Limited Angle Tomography

Taubmann

et al. 2017

Preprint

Self Cite

“…The distortion of markers outside the FOV fundamentally originates from limited angular X-rays passing through them. In limited angle tomography (Quinto, 2007;Huang et al, 2016), boundaries tangent to available X-rays are well preserved in reconstructed images (see Fig. 3.6 in (Frikel, 2013)).…”

Section: Direct Method: Detection From Distorted Markersmentioning

confidence: 98%

Fiducial marker recovery and detection from severely truncated data in navigation assisted spine surgery

Fan¹,

Kreher²,

Keil³

et al. 2021

Preprint

Self Cite

Fiducial markers are commonly used in navigation assisted minimally invasive spine surgery (MISS) and they help transfer image coordinates into real world coordinates. In practice, these markers might be located outside the field-of-view (FOV), due to the limited detector sizes of C-arm cone-beam computed tomography (CBCT) systems used in intraoperative surgeries. As a consequence, reconstructed markers in CBCT volumes suffer from artifacts and have distorted shapes, which sets an obstacle for navigation. In this work, we propose two fiducial marker detection methods: direct detection from distorted markers (direct method) and detection after marker recovery (recovery method). For direct detection from distorted markers in reconstructed volumes, an efficient automatic marker detection method using two neural networks and a conventional circle detection algorithm is proposed. For marker recovery, a task-specific learning strategy is proposed to recover markers from severely truncated data. Afterwards, a conventional marker detection algorithm is applied for position detection. The two methods are evaluated on simulated data and real data, both achieving a marker registration error smaller than 0.2 mm. Our experiments demonstrate that the direct method is capable of detecting distorted markers accurately and the recovery method with task-specific learning has high robustness and generalizability on various data sets. In addition, the task-specific learning is able to reconstruct other structures of interest accurately, e.g. ribs for image-guided needle biopsy, from severely truncated data, which empowers CBCT systems with new potential applications.

“…An experiment for showing the capability on distorted marker detection is conducted. The markers with 2 mm radius size are placed in different distances to the FOV boundary, and the missing angles, which reflect the amount of missing data, are calculated based on the theory of limited angle tomography 28,29 . The scores for the distorted markers are listed in Table 1.…”

Section: Related Workmentioning

confidence: 99%

Fiducial marker recovery and detection from severely truncated data in navigation‐assisted spine surgery

et al. 2022

Self Cite

Purpose Fiducial markers are commonly used in navigation‐assisted minimally invasive spine surgery and they help transfer image coordinates into real‐world coordinates. In practice, these markers might be located outside the field‐of‐view (FOV) of C‐arm cone‐beam computed tomography (CBCT) systems used in intraoperative surgeries, due to the limited detector sizes. As a consequence, reconstructed markers in CBCT volumes suffer from artifacts and have distorted shapes, which sets an obstacle for navigation. Methods In this work, we propose two fiducial marker detection methods: direct detection from distorted markers (direct method) and detection after marker recovery (recovery method). For direct detection from distorted markers in reconstructed volumes, an efficient automatic marker detection method using two neural networks and a conventional circle detection algorithm is proposed. For marker recovery, a task‐specific data preparation strategy is proposed to recover markers from severely truncated data. Afterwards, a conventional marker detection algorithm is applied for position detection. The networks in both methods are trained based on simulated data. For the direct method, 6800 images and 10 000 images are generated, respectively, to train the U‐Net and ResNet50. For the recovery method, the training set includes 1360 images for FBPConvNet and Pix2pixGAN. The simulated data set with 166 markers and four cadaver cases with real fiducials are used for evaluation. Results The two methods are evaluated on simulated data and real cadaver data. The direct method achieves 100% detection rates within 1 mm detection error on simulated data with normal truncation and simulated data with heavier noise, but only detect 94.6% markers in extremely severe truncation case. The recovery method detects all the markers successfully in three test data sets and around 95% markers are detected within 0.5 mm error. For real cadaver data, both methods achieve 100% marker detection rates with mean registration error below 0.2 mm. Conclusions Our experiments demonstrate that the direct method is capable of detecting distorted markers accurately and the recovery method with the task‐specific data preparation strategy has high robustness and generalizability on various data sets. The task‐specific data preparation is able to reconstruct structures of interest outside the FOV from severely truncated data better than conventional data preparation.