The Role of Iterative Reconstruction Techniques in Cardiovascular CT

Nance, John W.; Schoepf, U. Joseph; Ebersberger, Ullrich

doi:10.1007/s40134-013-0023-y

Cited by 7 publications

(7 citation statements)

References 57 publications

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“…The reduction in image noise and improved SNR and CNR with unchanged CT attenuation allow image acquisition at lower radiation doses when AIDR 3D is planned as opposed to FBP/QDS+. [ 22 ] Iterative reconstruction may be used for noise reduction to improve the image quality while maintaining the same radiation dose as FBP/QDS+, or reducing the delivered dose while maintaining an equivalent image quality. [ 23 – 27 ]…”

Section: Discussionmentioning

confidence: 99%

Impact of iterative reconstruction vs. filtered back projection on image quality in 320-slice CT coronary angiography

et al. 2017

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Iterative reconstruction has been shown to reduce image noise compared with traditional filtered back projection with quantum denoising software (FBP/QDS+) in CT imaging but few comparisons have been made in the same patients without the influence of interindividual factors. The objective of this study was to investigate the impact of adaptive iterative dose reduction in 3-dimensional (AIDR 3D) and FBP/QDS+-based image reconstruction on image quality in the same patients.We randomly selected 100 patients enrolled in the coronary evaluation using 320-slice CT study who underwent CT coronary angiography using prospectively electrocardiogram triggered image acquisition with a 320-detector scanner. Both FBP/QDS+ and AIDR 3D reconstructions were performed using original data. Studies were blindly analyzed for image quality by measuring the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Image quality was assessed qualitatively using a 4-point scale.Median age was 63 years (interquartile range [IQR]: 56–71) and 72% were men, median body mass index 27 (IQR: 24–30) and median calcium score 222 (IQR: 11–644). For all regions of interest, mean image noise was lower for AIDR 3D vs. FBP/QDS+ (31.69 vs. 34.37, P ≤ .001). SNR and CNR were significantly higher for AIDR 3D vs. FBP/QDS+ (16.28 vs. 14.64, P < .001 and 19.21 vs. 17.06, P < .001, respectively). Subjective (qualitative) image quality scores were better using AIDR 3D vs. FBP/QDS+ with means of 1.6 and 1.74, respectively (P ≤ .001).Assessed in the same individuals, iterative reconstruction decreased image noise and raised SNR/CNR as well as subjective image quality scores compared with traditional FBP/QDS+ in 320-slice CT coronary angiography at standard radiation doses.

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Section: Discussionmentioning

confidence: 99%

Impact of iterative reconstruction vs. filtered back projection on image quality in 320-slice CT coronary angiography

et al. 2017

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“…). As the pixel size is larger than 1 mm, photon starvation is expected to be reduced due to a better signal‐to‐noise ratio …”

Section: Methodsmentioning

confidence: 99%

“…As the pixel size is larger than 1 mm, photon starvation is expected to be reduced due to a better signal-to-noise ratio. 24,25 In order to read the correct density for high-density materials from the CT numbers, the extended range (over 3071 HU) option was enabled for image acquisition. However, the effect of the 1-mm copper plate, with a mass density of 8.9 g/cm 3 , inside the EPID detector produced some artifacts and partial volume effects, leading to incorrectly assigned CT numbers.…”

Section: B Ct Images Of the Epidmentioning

confidence: 99%

A portal dosimetry dose prediction method based on collapsed cone algorithm using the clinical beam model

et al. 2017

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The method developed here can be easily implemented into clinic, as neither additional modeling of the clinical energy nor an independent image prediction algorithm are necessary. The main advantage of this method is that portal dose prediction is calculated with the same algorithm and beam model used for patient dose distribution calculation. This method was independently validated with an ionization chamber matrix.

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“…Several single-and multi-vendor studies have reported advantages of iterative reconstruction techniques over FBP with improved image quality at 23-76% lower radiation dose burden [51]. Conversely, some studies have reported that CT images generated from iterative reconstruction methods are too smooth (less noisy) and, therefore, not superior to FBP [52,57,58].…”

Section: Iterative Reconstructionmentioning

confidence: 99%

“…Compared to HI-RT, MI-RT have greater noise reduction and artifact reduction potential, and therefore, can enable a greater reduction in radiation dose associated with CT scanning. Several studies have reported that MI-RT can reduce radiation dose further than HI-RT and FBP while maintaining image quality for several routine and non-routine clinical indications and body regions in both adult and pediatric patients [57][58][59]. Although most studies have found better image quality at lower radiation dose with iterative reconstruction techniques compared to FBP, some investigators have described a loss of image resolution and low-contrast resolution at low radiation dose [60,61].…”

Section: Iterative Reconstructionmentioning

confidence: 99%

Artificial intelligence in image reconstruction: The change is here

Singh

Wang

et al. 2020

Physica Medica

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Innovations in CT have been impressive among imaging and medical technologies in both the hardware and software domain. The range and speed of CT scanning improved from the introduction of multidetector-row CT scanners with wide-array detectors and faster gantry rotation speeds. To tackle concerns over rising radiation doses from its increasing use and to improve image quality, CT reconstruction techniques evolved from filtered back projection to commercial release of iterative reconstruction techniques, and recently, of deep learning (DL)based image reconstruction. These newer reconstruction techniques enable improved or retained image quality versus filtered back projection at lower radiation doses. DL can aid in image reconstruction with training data without total reliance on the physical model of the imaging process, unique artifacts of PCD-CT due to charge sharing, K-escape, fluorescence x-ray emission, and pulse pileups can be handled in the data-driven fashion. With sufficiently reconstructed images, a well-designed network can be trained to upgrade image quality over a practical/clinical threshold or define new/killer applications. Besides, the much smaller detector pixel for PCD-CT can lead to huge computational costs with traditional model-based iterative reconstruction methods whereas deep networks can be much faster with training and validation. In this review, we present techniques, applications, uses, and limitations of deep learning-based image reconstruction methods in CT.

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The Role of Iterative Reconstruction Techniques in Cardiovascular CT

Cited by 7 publications

References 57 publications

Impact of iterative reconstruction vs. filtered back projection on image quality in 320-slice CT coronary angiography

Impact of iterative reconstruction vs. filtered back projection on image quality in 320-slice CT coronary angiography

A portal dosimetry dose prediction method based on collapsed cone algorithm using the clinical beam model

Artificial intelligence in image reconstruction: The change is here

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