Computed tomography recent history and future perspectives

Hsieh, Jiang; Flohr, Thomas

doi:10.1117/1.jmi.8.5.052109

Cited by 64 publications

(45 citation statements)

References 47 publications

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“…The second part of Equation (3) states that we can simply add a normally distributed noise with its variance equal to the measured detector signal, s(c, r, v). The first part of Equation (3) indicates that under such a condition, the variance of the added noise, 𝛾 2 n (c, r, v), is identical to the variance of the original detector measurement, γ 2 (c, r, v). If we denote by f n (x, y, z) the reconstructed image of the simulated 50% dose, the difference image, d n (x, y, z), should exhibit identical noise magnitude as the original reconstructed image f(x, y, z), where:…”

Section: Methodsmentioning

confidence: 99%

“…Radiation dose reduction has been the focus of many studies in recent years. Various approaches have been investigated, ranging from hardware, software, data acquisition, image reconstruction, and post‐processing 1–3 . Although these approaches differ significantly in terms of methodology, they share a common goal: to maintain or improve image quality and clinical performance while reducing the radiation exposure to patients.…”

Section: Introductionmentioning

confidence: 99%

“…Various approaches have been investigated, ranging from hardware, software, data acquisition, image reconstruction, and postprocessing. [1][2][3] Although these approaches differ significantly in terms of methodology, they share a common goal: to maintain or improve image quality and clinical performance while reducing the radiation exposure to patients. Various image quality performance metrics are used, including physical, psychophysical, and observer performance tests.…”

Section: Introductionmentioning

confidence: 99%

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A novel simulation‐driven reconstruction approach for x‐ray computed tomography

Hsieh¹

2022

Medical Physics

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Radiation dose reduction is critical to the success of x-ray computed tomography (CT). Many advanced reconstruction techniques have been developed over the years to combat noise resulting from the low-dose CT scans. These algorithms rely on accurate local estimation of the image noise to determine reconstruction parameters or to select inferencing models. Because of the difficulties in the noise estimation for heterogeneous objects, the performance of many algorithms is inconsistent and suboptimal. Here, we propose a novel approach to overcome such shortcoming. Method: By injecting appropriate amount of noise in the CT raw data, a computer simulation approach is capable of accurately estimating the local statistics of the raw data and the local noise in the reconstructed images. This information is then used to guide the noise-reduction process during the reconstruction. As an initial implementation, a scaling map is generated based on the noise predicted from the simulation and the noise estimated from existing reconstruction algorithms. Images generated with existing algorithms are subsequently modified based on the scaling map. In this study, both iterative reconstruction (IR) and deep learning image reconstruction (DLIR) algorithms are evaluated. Results: Phantom experiments were conducted to evaluate the performance of the simulation-based noise estimation in terms of the standard deviation and noise power spectrum. Quantitative results have demonstrated that the noise measured from the original image matches well with the noise estimated from the simulation. Clinical datasets were utilized to further confirm the accuracy of the proposed approach under more challenging conditions. To validate the performance of the proposed reconstruction approach, clinical scans were used. Performance comparison was carried out qualitatively and quantitatively. Two existing advanced reconstruction techniques, IR and DLIR, were evaluated against the proposed approach. Results have shown that the proposed approach outperforms existing IR and DLIR algorithms in terms of noise suppression and, equally importantly, noise uniformity across the entire imaging volume. Visual assessment of the images also reveals that the proposed approach does not endure noise texture issues faced by some of the existing reconstruction algorithms today. Conclusion:Phantom and clinical results have demonstrated superior performance of the proposed approach with regard to noise reduction as well as noise homogeneity. Visual inspection of the noise texture further confirms the clinical utility of the proposed approach. Future enhancements on the current implementation are explored regarding image quality and computational efficiency.Because of the limited scope of this paper,detailed investigation on these enhancement features will be covered in a separate report.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel simulation‐driven reconstruction approach for x‐ray computed tomography

Hsieh¹

2022

Medical Physics

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“…Newer technologies that drove these advances, such as spiral or helical, multislice, wide-cone, dual-energy, and photon counting CT, are discussed in a companion paper in this JMI Special Section. 71 12 Concluding Comments On October 11, 1979, almost exactly 8 years after the first patient's CT scan at Atkinson-Morley Hospital, it was announced that the Nobel Prize in Physiology or Medicine would be jointly awarded to Allan Cormack and Godfrey Hounsfield for the "development of computer-assisted tomography." 72 The announcement reported: "It is no exaggeration to state that no other method within x-ray diagnostics within such a short period of time has led to such remarkable advances in research and in a multitude of applications."…”

Section: Competition Cost Containment and Consolidationmentioning

confidence: 99%

How CT happened: the early development of medical computed tomography

2021

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“…In "How CT happened: the early development of medical computed tomography," Schulz and co-authors give a compelling account of the development of the 1971 scanner and the first generations of scanners that followed, recounting stories both familiar and unfamiliar. 1 Hsieh and Flohr carry the story to the present day in "Computed tomography recent history and future perspectives," ending with a look forward to emerging technologies like photon counting CT. 2 In "From EMI to AI: a brief history of commercial CT reconstruction algorithms," Crawford and La Rivière survey the computational side of CT over the last fifty years, focusing on four key eras: the algorithmic developments around the 1971 scanner, the optimization and rise to dominance of filtered backprojection (FBP) algorithm, the need for modified FBP to accommodate spiral and cone-beam scanners, and finally the rise of iterative and artificial intelligence (AI) algorithms in recent years. 3 Fahrig and co-authors consider application-specific CT scanners built around wide, 2D digital detectors mounted on C-arms and other novel cone-beam geometries that have brought CT technologies into operating and procedure rooms.…”

mentioning

confidence: 99%