Three dimensional dose distribution comparison of simple and complex acquisition trajectories in dedicated breast CT

Shah, Jainil P.; Mann, Steven; McKinley, Randolph L.; Tornai, Martin P.

doi:10.1118/1.4923169

Cited by 16 publications

(16 citation statements)

References 52 publications

(89 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Modulation transfer function (MTF) and sampling studies [7] were performed in the past using tilted trajectories in addition to x-ray scatter [25] and dose distribution [9] studies. We have recently also developed a tilt capable, hybrid SPECT-CT system where each modality is independently capable of traversing 3D orbits [28].…”

Section: Discussionmentioning

confidence: 99%

“…In order to make a robust measurement of HU uniformity, three uniform cylinders of different sizes (15, 12.5, 10 cm diameters and 20 cm height) filled with three different water-methanol mixtures (Table 1) were scanned using the dedicated breast CT system extensively described in previous publications [9, 10, 18]. The PMMA cylinders with uniform 3 mm thick walls around and on bottom were placed at the center of the rotational axis (70 cm SID) with the fluid level above the top of the 32 degree x-ray cone beam, and 240 total projections were acquired over 360 azimuthal angles.…”

Section: Methodsmentioning

confidence: 99%

“…For tilted trajectories, as the polar tilt angle of the system changes, the angle of incidence of the x-ray beam changes, thereby changing the path lengths of the x-rays incident on the target volume [28, 30]. This results in differences in the dose distribution [9], and scatter distribution [10], which in turn would affect the reconstructed absolute attenuation coefficients and thereby the HU values.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Characterization of CT Hounsfield Units for 3D acquisition trajectories on a dedicated breast CT system

Shah

Mann

McKinley

et al. 2018

XST

Self Cite

View full text Add to dashboard Cite

Hounsfield Units (HU) are used clinically in differentiating tissue types in a reconstructed CT image, and therefore the HU accuracy of a system is important, especially when using multiple sources, novel detector and non-traditional trajectories. Dedicated clinical breast CT (BCT) systems therefore should be similarly evaluated. In this study, uniform cylindrical phantoms filled with various uniform density fluids were used to characterize differences in HU values between simple circular and complex 3D (saddle) orbits. Based on ACR recommendations, the HU accuracy, center-to-edge variability within a slice, and overall variability within the reconstructed volume were characterized for simple and complex acquisitions possible on a single versatile BCT system. Results illustrate the statistically significantly better performance of the saddle orbit, especially close to the chest and nipple regions of what would clinically be a pendant breast volume. The incomplete cone beam acquisition of a simple circular orbit causes shading artifacts near the nipple, due to insufficient sampling, rendering a major portion of the scanned phantom unusable, whereas the saddle orbit performs exceptionally well and provides a tighter distribution of HU values throughout the reconstructed volumes. This study further establishes the advantages of using 3D acquisition trajectories for breast CT as well as other applications by demonstrating the robustness of HU values throughout large reconstructed volumes.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Characterization of CT Hounsfield Units for 3D acquisition trajectories on a dedicated breast CT system

Shah

Mann

McKinley

et al. 2018

XST

Self Cite

View full text Add to dashboard Cite

show abstract

“…A modulation transfer function (MTF) and sampling studies were performed in the past using tilted trajectories. 54 X-ray scatter, 55 dose distribution, 56 and Hounsfield unit (HU) accuracy 57 of tilted trajectories have also been investigated on stand-alone devices, but none of these studies have been performed after the inclusion of the SPECT system in the hybrid device. The SPECT subsystem remained unchanged from previous versions of the system and has been evaluated by our group extensively; 40,42,58,59 therefore, these measurements were not repeated.…”

Section: Discussionmentioning

confidence: 99%

Implementation and CT sampling characterization of a third-generation SPECT–CT system for dedicated breast imaging

Shah

Mann

McKinley³

et al. 2017

J. Med. Imag

Self Cite

View full text Add to dashboard Cite

Stand-alone cone beam computed tomography (CT) and single-photon emission computed tomography (SPECT) systems capable of complex acquisition trajectories have previously been developed for breast imaging. Fully three-dimensional (3-D) motions of SPECT systems provide views into the chest wall and throughout the entire volume. The polar tilting capability of the CBCT system has shown improvement in sampling close to the chest wall, while eliminating cone beam artifacts. Here, a single hybrid SPECT-CT system, with each individual modality capable of independently traversing complex trajectories around a common pendant breast volume, was developed. We present the practical implementation of this design and preliminary results of the CT system. The fully 3-D SPECT was nested inside the suspended CT gantry and oriented perpendicular to the CT source-detector pair. Both subsystems were positioned on a rotation stage, with the combined polar and azimuthal motions enabling spherical trajectories. Six trajectories were used for initial evaluation of the tilt capable CT system. The developed system can achieve polar tilt angles with a [Formula: see text] positioning error and no hysteresis. Initial imaging results demonstrate that additional off-axis projection views of various geometric resolution phantoms facilitate more complete sampling, more consistent attenuation value recovery, and markedly improved reconstructions. This system could have various applications in diagnostic or therapeutic breast imaging.

show abstract

“…Accordingly, several generations and designs of bCT devices exist. [1][2][3][4] Reducing and resolving artifacts known to arise in bCT is critical for improving imaging for breast cancer detection.…”

Section: Introductionmentioning

confidence: 99%

Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum‐likelihood polynomial fitting approach

Ghazi¹,

Hernandez

Abbey

et al. 2019

Medical Physics

View full text Add to dashboard Cite

Purpose The purpose of this work was twofold: (a) To provide a robust and accurate method for image segmentation of dedicated breast CT (bCT) volume data sets, and (b) to improve Hounsfield unit (HU) accuracy in bCT by means of a postprocessing method that uses the segmented images to correct for the low‐frequency shading artifacts in reconstructed images. Methods A sequential and iterative application of image segmentation and low‐order polynomial fitting to bCT volume data sets was used in the interleaved correction (IC) method. Image segmentation was performed through a deep convolutional neural network (CNN) with a modified U‐Net architecture. A total of 45 621 coronal bCT images from 111 patient volume data sets were segmented (using a previously published segmentation algorithm) and used for neural network training, validation, and testing. All patient data sets were selected from scans performed on four different prototype breast CT systems. The adipose voxels for each patient volume data set, segmented using the proposed CNN, were then fit to a three‐dimensional low‐order polynomial. The polynomial fit was subsequently used to correct for the shading artifacts introduced by scatter and beam hardening in a method termed “flat fielding.” An interleaved utilization of image segmentation and flat fielding was repeated until a convergence criterion was satisfied. Mathematical and physical phantom studies were conducted to evaluate the dependence of the proposed algorithm on breast size and the distribution of fibroglandular tissue. In addition, a subset of patient scans (not used in the CNN training, testing or validation) were used to investigate the accuracy of the IC method across different scanner designs and beam qualities. Results The IC method resulted in an accurate classification of different tissue types with an average Dice similarity coefficient > 95%, precision > 97%, recall > 95%, and F1‐score > 96% across all tissue types. The flat fielding correction of bCT images resulted in a significant reduction in either cupping or capping artifacts in both mathematical and physical phantom studies as measured by the integral nonuniformity metric with an average reduction of 71% for cupping and 30% for capping across different phantom sizes, and the Uniformity Index with an average reduction of 53% for cupping and 34% for capping. Conclusion The validation studies demonstrated that the IC method improves Hounsfield Units (HU) accuracy and effectively corrects for shading artifacts caused by scatter contamination and beam hardening. The postprocessing approach described herein is relevant to the broad scope of bCT devices and does not require any modification in hardware or existing scan protocols. The trained CNN parameters and network architecture are available for interested users.

show abstract

Three dimensional dose distribution comparison of simple and complex acquisition trajectories in dedicated breast CT

Cited by 16 publications

References 52 publications

Characterization of CT Hounsfield Units for 3D acquisition trajectories on a dedicated breast CT system

Characterization of CT Hounsfield Units for 3D acquisition trajectories on a dedicated breast CT system

Implementation and CT sampling characterization of a third-generation SPECT–CT system for dedicated breast imaging

Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum‐likelihood polynomial fitting approach

Contact Info

Product

Resources

About