Skip Navigation LinksHome > Current Issue > Iterative Image Reconstruction Techniques: Cardiothoracic Co...
Journal of Thoracic Imaging:
doi: 10.1097/RTI.0000000000000041
Review Article

Iterative Image Reconstruction Techniques: Cardiothoracic Computed Tomography Applications

Cho, Young Jun MD*,†; Schoepf, U. Joseph MD*; Silverman, Justin R. BA*; Krazinski, Aleksander W. BA*; Canstein, Christian MS*,‡; Deak, Zsuzsanna MD§; Grimm, Jochen MD∥,¶; Geyer, Lucas L. MD*,§

Free Access
Article Outline
Collapse Box

Author Information

*Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC

Siemens Healthcare Sector, Malvern, PA

Department of Radiology, Konyang University College of Medicine, Daejeon, Korea

§Department of Clinical Radiology, University Hospitals LMU, Munich, Germany

Departments of Medical Radiology

Legal Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland

Christian Canstein is an employee of Siemens Healthcare; Lucas L. Geyer is on the speaker’s bureau for GE Healthcare, Germany; U. Joseph Schoepf is a medical consultant to Bayer-Schering, Bracco, General Electric, Medrad, Siemens, and TeraRecon and receives research support from Bayer-Schering, Bracco, General Electric, Medrad, and Siemens. The remaining authors declare no conflicts of interest.

Reprints: U. Joseph Schoepf, MD, Heart & Vascular Center, Medical University of South Carolina, Ashley River Tower, MSC 226, 25 Courtenay Dr, Charleston, SC 29401 (e-mail: schoepf@musc.edu).

Collapse Box

Abstract

Iterative image reconstruction algorithms provide significant improvements over traditional filtered back projection in computed tomography (CT). Clinically available through recent advances in modern CT technology, iterative reconstruction enhances image quality through cyclical image calculation, suppressing image noise and artifacts, particularly blooming artifacts. The advantages of iterative reconstruction are apparent in traditionally challenging cases—for example, in obese patients, those with significant artery calcification, or those with coronary artery stents. In addition, as clinical use of CT has grown, so have concerns over ionizing radiation associated with CT examinations. Through noise reduction, iterative reconstruction has been shown to permit radiation dose reduction while preserving diagnostic image quality. This approach is becoming increasingly attractive as the routine use of CT for pediatric and repeated follow-up evaluation grows ever more common. Cardiovascular CT in particular, with its focus on detailed structural and functional analyses, stands to benefit greatly from the promising iterative solutions that are readily available.

Although the number of computed tomography (CT) examinations has increased markedly over the last decade, there are growing concerns over individual and collective radiation, along with the assumed increase in radiation-induced lung cancer risk.1–3 Reduction of radiation dose is particularly necessary for patients undergoing multiple CT studies, those undergoing CT screening studies, and pediatric patients.4 To date, various strategies in the field of cardiothoracic CT have been used in an effort to minimize radiation dose.

The filtered back projection reconstruction algorithm (FBP), currently the most widely used CT reconstruction method, is associated with fundamental limits on radiation dose reduction. With FBP, tube current reductions increase image noise, as fewer photons reach the detector. Image noise also increases at higher spatial resolutions.5–7 To preserve image quality, increased image noise is often compensated for by utilizing higher x-ray tube settings.

Iterative reconstruction has recently experienced a renaissance, becoming a hot topic in the field of CT imaging because it can overcome the image quality–radiation dose trade-off. The principles of iterative reconstruction are not new, having been proposed several decades ago as a solution to the quantum noise associated with FBP.4,8 However, until recently, iterative techniques have primarily been applied in positron emission tomography, an imaging modality with more noise and fewer projection data.9,10 In contrast, the clinical application of this technique to the large data sets of CT was limited because of the relatively time-consuming nature of this reconstruction and its demand for extensive computer processing.

With the advent of modern computer processing and the evolution of more efficient algorithms, iterative reconstruction speed has entered the realm of clinical expediency for CT. Statistical iterative reconstruction was the first fast-turnaround technique, which was introduced to avoid the long reconstruction times of theoretical iterative reconstruction.11,12 Utilizing a single statistical correction model, this technique had limited clinical adoption because of a perceived plastic-like appearance and unfamiliar image impressions resulting from aggressive noise reduction.

At this point, every major CT vendor has developed at least 1 iterative reconstruction algorithm: Adaptive Statistical Iterative Reconstruction (ASIR) and Model-Based Iterative Reconstruction (MBIR or Veo) by General Electric (Milwaukee, WI), iDose and iDose4 by Philips (Einthoven, the Netherlands), Iterative Reconstruction in Image Space (IRIS) and Sinogram Affirmed Iterative Reconstruction (SAFIRE) by Siemens (Forchheim, Germany), and Adaptive Iterative Dose Reduction (AIDR) and AIDR 3D by Toshiba (Tochigi, Japan). The remainder of this article will focus on the advantages and disadvantages of these reconstruction techniques in various settings.

Back to Top | Article Outline

VARIOUS ITERATIVE RECONSTRUCTION TECHNIQUES

Unfortunately, the inner workings of iterative reconstruction algorithms are often difficult to access in detail. In an effort to protect their interests and investments, most major CT vendors provide limited information on their technologies. The various iterative reconstruction algorithms of major CT vendors use different approaches, with differences in the ease of implementation, image reconstruction speed, and image reconstruction quality. There are 3 general categories of iterative reconstruction algorithms, distinguished by their approaches to reconstruction: calculations performed in image-data space only, calculations in raw-data space only, and those that combine these 2 efforts—involving both raw and image data (Table 1).13

TABLE 1
TABLE 1
Image Tools
Back to Top | Article Outline
ASIR

ASIR also uses information arising from an initial raw data image and performs iterative reconstruction from both raw data and image-data space. ASIR reduces image noise by comparing the acquired image with a statistical noise model. Furthermore, to improve image confidence, this iterative algorithm is typically used as a blend of FBP and ASIR with adjustable increments ranging from 0% to 100%.

Back to Top | Article Outline
MBIR or Veo

MBIR is a maximum likelihood reconstruction that models statistics as well as the CT system geometry, including the tube, detector, focal spot of the tube, and slice thickness. However, because of the highly complex algorithm, this iterative reconstruction technique requires significantly more processing than most others, with reconstruction times ranging from 10 to 90 minutes depending on scan volume.13

Back to Top | Article Outline
iDose and iDose4

iDose and iDose4 perform iterative reconstruction in both raw and image-data space. iDose removes noise from the raw data with the use of a Poisson denoising algorithm. The reconstructed image is then transferred to image space, where it is compared with an optimal noise-free anatomic model to further reduce image noise. iDose4 is a further advanced version of the iDose algorithm.

Back to Top | Article Outline
IRIS

In an effort to reduce reconstruction times, the IRIS process performs raw-data reconstruction only once in the raw-data space, performing subsequent rounds of noise reduction in the image-data space. IRIS generates an initial master image from the raw data containing all relevant information of the raw data. Image noise from the master image propagates into the image-data space. The consecutive iterative processing loops occur in the image-data space. In addition, a noise model that is derived directly from the raw data is applied during a regularization step for image noise reduction. Through this regularization step, a corrected image is consecutively compared with the original image to generate an updated image, adding to the previous data set before the next iteration is performed. Reference to the original image allows iterative processing loops to remove image noise without degrading image sharpness.

Back to Top | Article Outline
SAFIRE

SAFIRE also uses a master image arising from raw data similar to IRIS. However, whereas IRIS performs all consecutive correction loops in the image-data space, SAFIRE uses 2 different correction pathways—1 in raw-data space and another in image-data space. With model-based forward projection, the data are reprojected into the raw-data space, allowing for correction of geometric imperfections in the initial reconstruction. The detected imperfection is corrected through adjusted repetition of the original raw data reconstruction, resulting in reduction of artifacts from the data. This process is repeated multiple times depending on the examination type. In image-data space, the second loop performs consecutive noise detection and subtraction from the current data set. Image noise and artifacts are effectively removed for superior image quality through these 2 different correction loops.

Back to Top | Article Outline
AIDR and AIDR 3D

With AIDR, image noise is removed through correction loops in the image-data space with comparison of reconstructed images with a preestablished noise model. The final iterative image and the original image are mixed to create the AIDR image, allowing the preservation of image quality at reduced radiation doses. AIDR 3D has recently received Food and Drug Administration clearance. This iterative reconstruction algorithm is performed in both raw data and image-data space.

Back to Top | Article Outline

CLINICAL APPLICATION OF ITERATIVE RECONSTRUCTION

Evaluation of Image Noise

Image noise is one of the principal determinants of CT imaging with a direct influence on image quality.14,15 Several reports on iterative reconstruction have consistently demonstrated significant improvement in image quality with decreased noise as compared with FBP.5,12,16–19 In one such study, coronary CT angiography (cCTA) was reconstructed at half-dose with SAFIRE in comparison with full-dose FBP5; mean image noise was significantly lower with half-dose SAFIRE than with full-dose FBP when measured as the standard deviation of CT number at the ascending aorta, pulmonary trunk, interventricular septum, and left ventricular cavity. Although image quality scores demonstrated no significant difference between the 2 reconstruction algorithms, there was an incremental improvement in the diagnostic accuracy of significant coronary artery stenosis with iterative reconstruction in comparison with coronary angiography as a reference standard. An example of a cardiac CT with full-dose SAFIRE reconstruction is provided in Figure 1.

FIGURE 1
FIGURE 1
Image Tools

In another study, 18 consecutive patients underwent an 80 kV cCTA with hybrid iterative reconstruction iDose and FBP.16 Attenuation, image noise, and contrast to noise ratio (CNR) were measured by placing a region of interest in the ascending aorta, the proximal right coronary artery, and the left main coronary artery. Coronary artery attenuation did not significantly differ between iDose and FBP reconstruction. However, the quantitative image quality (ie, image noise and CNR) and the qualitative image quality (ie, graininess, vessel sharpness, and streak artifacts) were significantly improved with iDose in comparison with FBP. In a phantom study with AIDR,20 image noise was decreased by 40% as compared with FBP. In a patient study with AIDR,20 the quantitative and qualitative measurements of image noise were also significantly improved compared with FBP. Also at pulmonary CTA, Pontana et al21 reported a significant improvement in signal to noise ratio (SNR) and CNR using SAFIRE.

Image noise decreases with additional reconstruction iterations; however, diagnostic confidence can be impaired by further iterative reconstruction.12,16 One cCTA study with ASIR12 demonstrated that, as the ASIR percentage was increased, noise significantly decreased. Although ASIR at 100% provided the lowest image noise, the mean relative image quality score was lower than that of FBP. Very high levels of ASIR resulted in an artificial appearance because of a noticeably different noise texture and smoothed borders. Rather, 40% and 60% ASIR were found to significantly improve image quality and the proportion of interpretability compared with FBP reconstruction.

Back to Top | Article Outline
Evaluation of Spatial Resolution

In traditional FBP, image noise is increased as spatial resolution is improved. However, iterative reconstruction theoretically allows decoupling of spatial resolution and image noise.11,19,20,22,23 This is verified with several phantom studies. In a dedicated phantom study using single-source and dual-source cardiac protocols with an artificial electrocardiograph (ECG)-triggered second-generation dual-source CT, Ghetti et al24 reported that IRIS preserved spatial resolution while allowing noise reduction in comparison with FBP. In a phantom study using AIDR, Gervaise et al20 confirmed that, although AIDR generated a mean image noise reduction of 40% compared with FBP, there was no significant difference in spatial resolution. Hara et al,11 however, observed a different result in a phantom study with ASIR. In Hara’s low-dose CT study with ASIR, spatial resolution of a high-contrast object was significantly lower than that of a routine-dose FBP image. However, the diagnostic value of the image was not affected by this limitation. In contrast, Noel et al25 demonstrated a preserved modulation transfer function (MTF)—an indicator of spatial resolution measured in a phantom—applying the iDose algorithm to a dose-reduced CT protocol, although image noise was substantially lower compared with FBP reconstructions. In addition, the contrast detail was comparable between the low-dose iDose and the full-dose FBP reconstruction. Park et al19 performed a cCTA evaluation of 81 patients, comparing IRIS with FBP based on MTF 50% for quantitative assessment of spatial resolution. Larger MTF 50% values indicated greater spatial resolution. Park and colleagues found MTF 50% to be significantly increased with IRIS compared with FBP, although the difference was small. Such decoupling of spatial resolution and image noise through iterative reconstruction is particularly beneficial to cCTA evaluation in cases with heavy coronary calcification, coronary stents, and excess adipose tissue, where high spatial and temporal resolution are critical yet often suffer.5

Back to Top | Article Outline
Coronary Calcification and Stenting

Although advanced cCTA permits higher image quality and faster image acquisition, coronary stents and heavy coronary calcification cause readers to hesitate when determining the degree of coronary stenosis.26,27 The primary reason for this is the presence of blooming artifacts due to beam-hardening and partial-volume effects. Blooming artifacts, one of the most discussed topics in cCTA, cause high-density objects to appear thicker than they actually are; this occurs because of the limited spatial and point-spread resolution of traditional CT algorithms.28–31 Blooming artifacts arising from heavy coronary calcification lead to overestimation of coronary artery stenosis, thereby prompting unnecessary studies such as coronary catheterization and myocardial perfusion.32–37 Blooming artifacts arising from metallic stent struts cause a significant overestimation of the degree of in-stent restenosis and underestimation of the stent lumen, resulting in false-positive findings—especially with high-density metal alloys or smaller diameters (<3.0 mm).38–41

There are several approaches to solving these problems in CT. One technique for minimizing blooming artifacts is the use of high-kV imaging. However, this approach can increase the radiation dose to the patient. Another approach is through improvement of spatial resolution and the use of sharper, higher-resolution reconstruction algorithms. However, these techniques also increase image noise, potentially requiring increased radiation dose to compensate. Stent composition is an important factor in blooming artifacts; high atomic number materials, such as gold or tantalum, cause severe blooming artifacts compared with low-density metals, such as magnesium or cobalt-chromium alloy.31,42

Recently, several reports have shown that iterative reconstruction algorithms can reduce image noise and blooming artifacts.5,23,39,43,44 In 55 cCTA examinations with heavy coronary calcification (average Agatston calcium score, 710), coronary calcium volume, analyzed using a threshold-based volumetric software tool (Volume Analysis, version VE31A; Siemens), was significantly lower with iterative reconstruction than with FBP. An example of cCTA with IRIS reconstruction is provided in Figure 2. An example of SAFIRE reconstruction is provided in Figure 3. In addition, mean image noise was significantly lower with iterative reconstruction than with FBP, and qualitative image quality was significantly higher. When compared with coronary catheterization as reference standard, diagnostic accuracy of significant coronary stenosis in patients with heavy coronary calcification significantly improved with iterative reconstruction compared with FBP reconstruction.43 In another study with 37 implanted stents reconstructed at full and half radiation dose with FBP and SAFIRE, stent volume measurement was used to assess the extent of stent blooming. Their results showed the highest mean stent volume at half-dose FBP, which gradually decreased with full-dose FBP, half-dose SAFIRE, and full-dose SAFIRE. Although these differences did not reach statistical significance, the results demonstrated the effect of iterative reconstruction for decreasing stent blooming. In addition, iterative reconstruction showed significantly less in-stent image noise and a higher SNR when compared with full-dose FBP reconstruction. An example of Veo reconstruction of ex vivo stent imaging is provided in Figure 4. Qualitative image quality scores were also significantly higher in full-dose iterative reconstruction compared with full-dose and half-dose FBP reconstruction.39 An example of SAFIRE reconstruction of a stent is provided in Figure 5. This is in line with the results of Oda et al45 and Funama et al46 who have shown that the combination of a high-resolution kernel with an iDose algorithm offers substantial improvements in stent visualization and the detection of in-stent stenosis.

FIGURE 2
FIGURE 2
Image Tools
FIGURE 3
FIGURE 3
Image Tools
FIGURE 4
FIGURE 4
Image Tools
FIGURE 5
FIGURE 5
Image Tools

Iterative reconstruction improves the reliability of disease severity differentiation and reduces the occurrence of false-positive findings in coronaries with heavy calcification or stenting through a more effective suppression of beam-hardening and blooming artifacts compared with FBP at constant x-ray tube settings.39,43,44 Several additional artifacts, such as scatter and motion, can also be reduced using iterative reconstruction.47

Back to Top | Article Outline
High-resolution Chest CT

Besides dose-reduced protocols for chest CT, the use of the iterative reconstruction technique allows for an improved depiction of anatomic and pathologic imaging findings. In a study comprising 24 patients, substantial better visualization of small normal lung structures (eg, interlobular septa, centrilobular arteries, small bronchi and bronchioles) as well as pathologic patterns (eg, reticular pattern, small nodules, changes in lung attenuation) was achieved by using ASIR, particularly in combination with a specific high-detail reconstruction kernel.48 In addition, the sensitivity for detection of pulmonary nodules using a computer-aided detection software can be significantly increased with increasing levels of ASIR compared with FBP. However, the number of false-positive findings per scan increased with higher influence of iterative reconstruction.49 Within the same context, Willemink et al50 showed no significant impact on volume measurements of solid pulmonary nodules using the iDose algorithm. Therefore, the clinical value of improved image quality through iterative reconstruction techniques remains to be seen. However, regarding the potential dose reduction, lung-screening trials using dose-reduced CT protocols appear to be a promising application of iterative reconstructions in chest imaging.51,52

Back to Top | Article Outline
Obesity

Despite advances in CT technology, the use of cCTA in obese patients is still challenging because of noise-impaired image quality.53–57 Image noise in relation to obesity can be explained by quantum noise and Compton scattering. Quantum noise is typically inversely proportional to the square root of the delivered dose.15,58 As soft-tissue absorption of low-energy x-ray photons in obese patients is increased, a reduced number of photons reach the detector; this decreases the SNR.15,55,59 Compton scattering, in simple terms, is the alteration of an x-ray photon’s trajectory following interaction with a substrate electron, with a comparable decrease in the frequency of the photon. This angular displacement causes the photon to proceed toward a different coordinate on the detector, leading to erroneous data. As body volume increases, x-ray scattering will increase, resulting in a deterioration in CT image quality.14,15 In addition, the degree of arterial enhancement can be further impaired in CTA of obese patients because of higher cardiac output, higher central blood volume, and beam-hardening artifacts.55,59,60

The superiority of iterative reconstruction techniques with regard to image quality and the potential for radiation dose reduction has been verified already by several reports.16–19,48,61,62 However, fewer studies have compared the CT image quality of iterative reconstruction with that of FBP in obese patients.19,63 In a study by Wang and colleagues with 109 consecutive patients using IRIS and FBP reconstruction, the study population was divided into 3 groups according to body mass index (BMI): normal patients were defined as those having a BMI of 18.50 to 24.99 kg/m2, overweight patients as those having a BMI of 25.00 to 29.99 kg/m2, and obese as those with a BMI≥30.00 kg/m2. IRIS demonstrated an incremental improvement in subjective image quality in all BMI groups, compared with FBP. In obese and overweight patients, the number of assessable coronary segments was increased with IRIS. Image noise, SNR, and CNR were also significantly improved in all BMI groups when using IRIS, compared with FBP. When IRIS and FBP were compared for cCTA with coronary catheterization as the referenced standard, diagnostic accuracy, specificity, and positive predictive value for detection of significant stenosis were significantly improved with IRIS (over FBP) in obese and overweight patients.

Moreover, iterative reconstruction has provided a different degree of dose reduction dependent on the level of obesity. Several studies using iterative reconstruction reported radiation dose reductions relative to obesity compared with traditional FBP.11,19 In a clinical study, a total of 33 obese patients (average BMI 43) who underwent pulmonary CTA were compared with 33 individuals controls (average BMI 22). Compared with FBP, the application of the iDose algorithm revealed a significant decrease in noise and increase in the CNR and also provided a subjective assessment of image quality.64 Regarding radiation exposure, it has been shown that the potential dose reduction percentage increases as BMI decreases. In a study cohort of 12 patients with iterative reconstruction and FBP, although patients with a BMI<20 exhibited CT dose index (CTDI) and dose-length product reductions of 65%, patients with a BMI≥25 had reductions of 29% to 35%.11 In a more recent and larger study consisting of 162 patients, iterative reconstruction in patient groups with BMI <25 and ≥25 demonstrated effective radiation doses of 40% and 51%, respectively, compared with routine-dose CT using FBP.19 Wang et al63 also reported similar results in a study involving obese patients using SAFIRE. Effective radiation dose at 100 kV cCTA with SAFIRE was 4.41±0.83 mSv and significantly lower with comparable image quality, compared with 8.83±1.74 mSv at 120 kV cCTA with FBP. An example of a cCTA of an obese patient is provided in Figure 6.

FIGURE 6
FIGURE 6
Image Tools
Back to Top | Article Outline
Radiation Dose Reduction

With advances in CT technology, the following strategies have been applied to minimize radiation dose during a cardiothoracic CT scan: automated tube current modulation,65–69 noise reduction filter,70,71 low tube voltage,72–79 reduction of z-axis scan length,73,80,81 ECG pulsing,82 heart rate adaptive pitch,83 sequential ECG triggering,78,84 high-pitch acquisition,85 and reduction in the duration of padding.86–89

The FBP reconstruction algorithm has fundamental limitations for radiation dose reduction because of the resultant image noise. Several recent studies involving iterative reconstruction consistently support that iterative reconstruction algorithms permit significant radiation dose reductions while maintaining superior image quality compared with FBP reconstruction.5,11,16,18,19,24,90–96

In a phantom study, Hara et al11 reported that the image noise of 30% ASIR with half radiation dose was equivalent to that of a full-dose FBP image. Figure 7 provides an example of a Veo reconstruction of a chest CT; the clear reduction in image noise suggests great potential for radiation dose reduction. Moscariello et al5 reported that the effective radiation dose with half-dose iterative reconstruction of cCTA was estimated at 3.2±2.1 mSv, whereas that of full-dose FBP reconstruction was estimated at 6.4±4.3 mSv; however, half-dose iterative reconstruction showed significantly lower image noise and higher image quality compared with full-dose FBP reconstruction. Oda et al16 also reported that the effective radiation dose with iterative reconstruction at 80-kV low-dose cCTA was 4.7±0.4 mSv (range, 4.2 to 5.4 mSv). Funama et al18 also reported that, although use of 80-kV cCTA with iterative reconstruction decreased the radiation dose by 76% compared with 120-kV cCTA with FBP, image quality between the 2 reconstruction algorithms was comparable. Pontana et al93 reported that iterative reconstruction of chest CT reduced the radiation dose by 35% compared with FBP reconstruction. In cCTA, a potential dose reduction of approximately 22%97 could be achieved using AIDR, which can be further increased to 50% to 64% by the implementation of AIDR 3D.98,99 However, data on radiation dose are usually based on comparisons between the so-called low-dose protocols and allegedly standard CT protocols, which can substantially vary between institutions. We also want to emphasize the possible gap between parameters of quantitative and qualitative image quality, which comes into effect when assessing potential dose reduction. In this context, Chen et al100 have evaluated the resolution and noise characteristics of 2 iterative algorithms (IRIS, SAFIRE) compared with FBP in a systematic phantom study. Depending on the size and contrast of a phantom lesion, the authors concluded a potential dose reduction of about 20% to 30% using IRIS and up to 50% to 60% using SAFIRE. In addition, Husarik et al101 also underlined that qualitative assessment of image quality did not reflect the promising results of quantitative image characteristics. Performing a nonclinical investigation of a liver phantom, the authors concluded a potential dose reduction of about 50% by using MBIR compared with FBP while combining subjective and objective assessment. However, diagnostic capability has to be taken into account when transferring ex vivo results into clinical practice. Pickhardt et al102 have recently shown the potential discrepancy between diagnostic performance and parameters of image quality. Despite improved subjective image quality and decreased image noise, substantially less abdominal lesions were detected by using a dose-reduced CT protocol with MBIR (mean CTDIvol 3.47 mGy) compared with a standard-dose CT protocol with FBP (mean CTDIvol 12.75 mGy). Although the authors emphasize a clinically important aspect, these relationships and specific impact on cardiac CT have to be investigated separately. Moreover, these results suggest that the clinical capabilities of iterative reconstruction algorithms do not necessarily reflect their underlying computational effort.

FIGURE 7
FIGURE 7
Image Tools

Most importantly, iterative reconstruction can be applied along with other image acquisition techniques, such as prospective ECG triggering or traditional retrospective ECG gating, providing additional dose reduction to existing dose-reduction strategies.5,43 Therefore, iterative reconstruction can provide a low-dose CT protocol for pediatric patients and obese patients and in the case of multiple follow-up or multiphasic studies.4,12

Back to Top | Article Outline

DISADVANTAGES

A disadvantage of earlier-generation iterative reconstruction has been the prohibitively long computational processing time compared with FBP. In recent times, advanced iterative reconstruction techniques have reduced processing times as a result of the use of computationally faster algorithms.4 At the time of writing this paper, the mean duration for data reconstruction of SAFIRE is longer than that of FBP, with reconstruction speeds of approximately 20 frames/s in SAFIRE and 40 frames/s in FBP.5,95 However, its clinical utility is not affected. As mentioned above, Veo is even more time-consuming because of a different iterative approach, which currently limits its implementation into clinical routine. Reported reconstruction times for data sets obtained over a scan length of 140 to 400 mm are 20 to 60 minutes, which equals roughly 0.2 to 0.5 frames/s.101 However, as computational power continues to evolve, the processing time of iterative reconstruction is expected to decrease even further.

Another disadvantage of the current iterative reconstruction technologies is that, in cases of highly iterative reconstruction, artificial-looking, noise-free images are produced with significantly smoothed borders, which can decrease diagnostic confidence. This appearance due to highly iterative reconstruction can usually be controlled by the amount (ie, strength) of iterative reconstruction,4 depending on the available algorithm. For instance, Gervaise et al20 reported no artificial-looking images with the use of AIDR—an algorithm that does not allow for the choice between different strength levels by the user. However, as more powerful iterative reconstruction algorithms emerge, noise will be reduced more efficiently and with better preservation of an image’s natural appearance.4,12,16

Back to Top | Article Outline

CONCLUSIONS

Iterative reconstruction can allow radiation dose reduction while maintaining superior image quality. Iterative reconstruction also provides improvement of certain artifacts (eg, blooming) in comparison with the FBP algorithm. Given what we have seen, the future prospects of iterative reconstruction are bright, with the expectation of it becoming an even more robust and easy-to-apply method with the emergence of more effective iterative reconstruction algorithms. Currently, these reconstruction techniques demonstrate powerful noise reduction and thereby permit further reduction in x-ray tube current/voltage for lower radiation doses, reducing patient risk and improving diagnostic outcomes.

Back to Top | Article Outline

REFERENCES

1. Berrington de Gonzalez A, Mahesh M, Kim KP, et al .Projected cancer risks from computed tomographic scans performed in the United States in 2007.Arch Intern Med. 2009; 169:2071–2077.

2. Smith-Bindman R, Lipson J, Marcus R, et al .Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer.Arch Intern Med. 2009; 169:2078–2086.

3. Sodickson A, Baeyens PF, Andriole KP, et al .Recurrent CT, cumulative radiation exposure, and associated radiation-induced cancer risks from CT of adults.Radiology. 2009; 251:175–184.

4. Silva AC, Lawder HJ, Hara A, et al .Innovations in CT dose reduction strategy: application of the adaptive statistical iterative reconstruction algorithm.Am J Roentgenol. 2010; 194:191–199.

5. Moscariello A, Takx RA, Schoepf UJ, et al .Coronary CT angiography: image quality, diagnostic accuracy, and potential for radiation dose reduction using a novel iterative image reconstruction technique-comparison with traditional filtered back projection.Eur Radiol. 2011; 21:2130–2138.

6. Nuyts J, De Man B, Dupont P, et al .Iterative reconstruction for helical CT: a simulation study.Phys Med Biol. 1998; 43:729–737.

7. Liu YJ, Zhu PP, Chen B, et al .A new iterative algorithm to reconstruct the refractive index.Phys Med Biol. 2007; 52:L5–13.

8. Brooks RA, Di Chiro G .Theory of image reconstruction in computed tomography.Radiology. 1975; 117:561–572.

9. Knesaurek K, Machac J, Vallabhajosula S, et al .A new iterative reconstruction technique for attenuation correction in high-resolution positron emission tomography.Eur J Nucl Med. 1996; 23:656–661.

10. Liow JS, Strother SC, Rehm K, et al .Improved resolution for PET volume imaging through three-dimensional iterative reconstruction.J Nucl Med. 1997; 38:1623–1631.

11. Hara AK, Paden RG, Silva AC, et al .Iterative reconstruction technique for reducing body radiation dose at CT: feasibility study.Am J Roentgenol. 2009; 193:764–771.

12. Leipsic J, Labounty TM, Heilbron B, et al .Adaptive statistical iterative reconstruction: assessment of image noise and image quality in coronary CT angiography.Am J Roentgenol. 2010; 195:649–654.

13. Nelson RC, Feuerlein S, Boll DT .New iterative reconstruction techniques for cardiovascular computed tomography: how do they work, and what are the advantages and disadvantages? J Cardiovasc Comput Tomogr. 2011; 5:286–292.

14. Brooks RA, Di Chiro G .Principles of computer assisted tomography (CAT) in radiographic and radioisotopic imaging.Phys Med Biol. 1976; 21:689–732.

15. Paul NS, Kashani H, Odedra D, et al .The influence of chest wall tissue composition in determining image noise during cardiac CT.Am J Roentgenol. 2011; 197:1328–1334.

16. Oda S, Utsunomiya D, Funama Y, et al .A hybrid iterative reconstruction algorithm that improves the image quality of low-tube-voltage coronary CT angiography.Am J Roentgenol. 2012; 198:1126–1131.

17. Marin D, Nelson RC, Schindera ST, et al .Low-tube-voltage, high-tube-current multidetector abdominal CT: improved image quality and decreased radiation dose with adaptive statistical iterative reconstruction algorithm—initial clinical experience.Radiology. 2010; 254:145–153.

18. Funama Y, Taguchi K, Utsunomiya D, et al .Combination of a low-tube-voltage technique with hybrid iterative reconstruction (iDose) algorithm at coronary computed tomographic angiography.J Comput Assist Tomogr. 2011; 35:480–485.

19. Park EA, Lee W, Kim KW, et al .Iterative reconstruction of dual-source coronary CT angiography: assessment of image quality and radiation dose.Int J Cardiovasc Imaging. 2012; 28:1775–1786.

20. Gervaise A, Osemont B, Lecocq S, et al .CT image quality improvement using Adaptive Iterative Dose Reduction with wide-volume acquisition on 320-detector CT.Eur Radiol. 2012; 22:295–301.

21. Pontana F, Pagniez J, Duhamel A, et al .Reduced-dose low-voltage chest CT angiography with Sinogram-affirmed Iterative Reconstruction versus Standard-Dose Filtered Back Projection.Radiology. 2013; 267:609–618.

22. Thibault JB, Sauer KD, Bouman CA, et al .A three-dimensional statistical approach to improved image quality for multislice helical CT.Med Phys. 2007; 34:4526–4544.

23. Van Gompel G, Van Slambrouck K, Defrise M, et al .Iterative correction of beam hardening artifacts in CT.Med Phys. 2011; 38:suppl 1 S36–S49.

24. Ghetti C, Ortenzia O, Serreli G .CT iterative reconstruction in image space: a phantom study.Phys Med. 2012; 28:161–165.

25. Noel PB, Fingerle AA, Renger B, et al .Initial performance characterization of a clinical noise-suppressing reconstruction algorithm for MDCT.Am J Roentgenol. 2011; 197:1404–1409.

26. Stein PD, Beemath A, Kayali F, et al .Multidetector computed tomography for the diagnosis of coronary artery disease: a systematic review.Am J Med. 2006; 119:203–216.

27. Catalan P, Leta R, Hidalgo A, et al .Ruling out coronary artery disease with noninvasive coronary multidetector CT angiography before noncoronary cardiovascular surgery.Radiology. 2011; 258:426–434.

28. Leber AW, Knez A, von Ziegler F, et al .Quantification of obstructive and nonobstructive coronary lesions by 64-slice computed tomography: a comparative study with quantitative coronary angiography and intravascular ultrasound.J Am Coll Cardiol. 2005; 46:147–154.

29. Brodoefel H, Burgstahler C, Tsiflikas I, et al .Dual-source CT: effect of heart rate, heart rate variability, and calcification on image quality and diagnostic accuracy.Radiology. 2008; 247:346–355.

30. Cordeiro MA, Lima JA .Atherosclerotic plaque characterization by multidetector row computed tomography angiography.J Am Coll Cardiol. 2006; 47:C40–47.

31. Mahnken AH .CT imaging of coronary stents: past, present, and future.ISRN Cardiol. 2012; 2012:139823

32. Zhang S, Levin DC, Halpern EJ, et al .Accuracy of MDCT in assessing the degree of stenosis caused by calcified coronary artery plaques.Am J Roentgenol. 2008; 191:1676–1683.

33. Meijs MF, Meijboom WB, Prokop M, et al .Is there a role for CT coronary angiography in patients with symptomatic angina? Effect of coronary calcium score on identification of stenosis.Int J Cardiovasc Imaging. 2009; 25:847–854.

34. Cademartiri F, Mollet NR, Lemos PA, et al .Impact of coronary calcium score on diagnostic accuracy for the detection of significant coronary stenosis with multislice computed tomography angiography.Am J Cardiol. 2005; 95:1225–1227.

35. Zhang LJ, Wu SY, Wang J, et al .Diagnostic accuracy of dual-source CT coronary angiography: the effect of average heart rate, heart rate variability, and calcium score in a clinical perspective.Acta Radiol. 2010; 51:727–740.

36. Scheller B, Hehrlein C, Bocksch W, et al .Treatment of coronary in-stent restenosis with a paclitaxel-coated balloon catheter.N Engl J Med. 2006; 355:2113–2124.

37. Lloyd-Jones D, Adams R, Carnethon M, et al .Heart disease and stroke statistics—2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee.Circulation. 2009; 119:480–486.

38. Sheth T, Dodd JD, Hoffmann U, et al .Coronary stent assessability by 64 slice multi-detector computed tomography.Catheter Cardiovasc Interv. 2007; 69:933–938.

39. Ebersberger U, Tricarico F, Schoepf UJ, et al .CT evaluation of coronary artery stents with iterative image reconstruction: improvements in image quality and potential for radiation dose reduction.Eur Radiol. 2012; 23:125–132.

40. Kerl JM, Schoepf UJ, Vogl TJ, et al .In vitro evaluation of metallic coronary artery stents with 64-MDCT using an ECG-gated cardiac phantom: relationship between in-stent visualization, stent type, and heart rate.Am J Roentgenol. 2010; 194:W256–262.

41. Kumbhani DJ, Ingelmo CP, Schoenhagen P, et al .Meta-analysis of diagnostic efficacy of 64-slice computed tomography in the evaluation of coronary in-stent restenosis.Am J Cardiol. 2009; 103:1675–1681.

42. Maintz D, Burg MC, Seifarth H, et al .Update on multidetector coronary CT angiography of coronary stents: in vitro evaluation of 29 different stent types with dual-source CT.Eur Radiol. 2009; 19:42–49.

43. Renker M, Nance JW Jr, Schoepf UJ, et al .Evaluation of heavily calcified vessels with coronary CT angiography: comparison of iterative and filtered back projection image reconstruction.Radiology. 2011; 260:390–399.

44. Renker M, Ramachandra A, Schoepf UJ, et al .Iterative image reconstruction techniques: applications for cardiac CT.J Cardiovasc Comput Tomogr. 2011; 5:225–230.

45. Oda S, Utsunomiya D, Funama Y, et al .Improved coronary in-stent visualization using a combined high-resolution kernel and a hybrid iterative reconstruction technique at 256-slice cardiac CT-Pilot study.Eur J Radiol. 2013; 82:288–295.

46. Funama Y, Oda S, Utsunomiya D, et al .Coronary artery stent evaluation by combining iterative reconstruction and high-resolution kernel at coronary CT angiography.Acad Radiol. 2012; 19:1324–1331.

47. Boas FE, Fleischmann D .Evaluation of two iterative techniques for reducing metal artifacts in computed tomography.Radiology. 2011; 259:894–902.

48. Prakash P, Kalra MK, Ackman JB, et al .Diffuse lung disease: CT of the chest with adaptive statistical iterative reconstruction technique.Radiology. 2010; 256:261–269.

49. Yanagawa M, Honda O, Kikuyama A, et al .Pulmonary nodules: effect of adaptive statistical iterative reconstruction (ASIR) technique on performance of a computer-aided detection (CAD) system-comparison of performance between different-dose CT scans.Eur J Radiol. 2012; 81:2877–2886.

50. Willemink MJ, Borstlap J, Takx RA, et al .The effects of computed tomography with iterative reconstruction on solid pulmonary nodule volume quantification.PLoS One. 2013; 8:e58053

51. Yamada Y, Jinzaki M, Tanami Y, et al .Model-based iterative reconstruction technique for ultralow-dose computed tomography of the lung: a pilot study.Invest Radiol. 2012; 47:482–489.

52. Yang WJ, Yan FH, Liu B, et al .Can Sinogram-Affirmed Iterative (SAFIRE) Reconstruction improve imaging quality on low-dose lung CT screening compared with traditional Filtered Back Projection (FBP) Reconstruction? J Comput Assist Tomogr. 2013; 37:301–305.

53. Yoshimura N, Sabir A, Kubo T, et al .Correlation between image noise and body weight in coronary CTA with 16-row MDCT.Acad Radiol. 2006; 13:324–328.

54. Raff GL, Gallagher MJ, O’Neill WW, et al .Diagnostic accuracy of noninvasive coronary angiography using 64-slice spiral computed tomography.J Am Coll Cardiol. 2005; 46:552–557.

55. Alkadhi H, Scheffel H, Desbiolles L, et al .Dual-source computed tomography coronary angiography: influence of obesity, calcium load, and heart rate on diagnostic accuracy.Eur Heart J. 2008; 29:766–776.

56. Brodoefel H, Tsiflikas I, Burgstahler C, et al .Cardiac dual-source computed tomography: effect of body mass index on image quality and diagnostic accuracy.Invest Radiol. 2008; 43:712–718.

57. Alkadhi H, Stolzmann P, Scheffel H, et al .Radiation dose of cardiac dual-source CT: the effect of tailoring the protocol to patient-specific parameters.Eur J Radiol. 2008; 68:385–391.

58. Silverman JD, Paul NS, Siewerdsen JH .Investigation of lung nodule detectability in low-dose 320-slice computed tomography.Med Phys. 2009; 36:1700–1710.

59. Schindera ST, Tock I, Marin D, et al .Effect of beam hardening on arterial enhancement in thoracoabdominal CT angiography with increasing patient size: an in vitro and in vivo study.Radiology. 2010; 256:528–535.

60. Bae KT, Seeck BA, Hildebolt CF, et al .Contrast enhancement in cardiovascular MDCT: effect of body weight, height, body surface area, body mass index, and obesity.Am J Roentgenol. 2008; 190:777–784.

61. Flicek KT, Hara AK, Silva AC, et al .Reducing the radiation dose for CT colonography using adaptive statistical iterative reconstruction: a pilot study.Am J Roentgenol. 2010; 195:126–131.

62. Gosling O, Loader R, Venables P, et al .A comparison of radiation doses between state-of-the-art multislice CT coronary angiography with iterative reconstruction, multislice CT coronary angiography with standard filtered back-projection and invasive diagnostic coronary angiography.Heart. 2010; 96:922–926.

63. Wang R, Schoepf UJ, Wu R, et al .Image quality and radiation dose of low dose coronary CT angiography in obese patients: sinogram affirmed iterative reconstruction versus filtered back projection.Eur J Radiol. 2012; 81:3141–3145.

64. Kligerman S, Mehta D, Farnadesh M, et al .Use of a hybrid iterative reconstruction technique to reduce image noise and improve image quality in obese patients undergoing computed tomographic pulmonary angiography.J Thorac Imaging. 2013; 28:49–59.

65. Mulkens TH, Bellinck P, Baeyaert M, et al .Use of an automatic exposure control mechanism for dose optimization in multi-detector row CT examinations: clinical evaluation.Radiology. 2005; 237:213–223.

66. Smith AB, Dillon WP, Lau BC, et al .Radiation dose reduction strategy for CT protocols: successful implementation in neuroradiology section.Radiology. 2008; 247:499–506.

67. Graser A, Wintersperger BJ, Suess C, et al .Dose reduction and image quality in MDCT colonography using tube current modulation.Am J Roentgenol. 2006; 187:695–701.

68. Lee CH, Goo JM, Ye HJ, et al .Radiation dose modulation techniques in the multidetector CT era: from basics to practice.Radiographics. 2008; 28:1451–1459.

69. McCollough CH, Bruesewitz MR, Kofler JM Jr .CT dose reduction and dose management tools: overview of available options.Radiographics. 2006; 26:503–512.

70. Kalra MK, Maher MM, Blake MA, et al .Detection and characterization of lesions on low-radiation-dose abdominal CT images postprocessed with noise reduction filters.Radiology. 2004; 232:791–797.

71. Kalra MK, Maher MM, Sahani DV, et al .Low-dose CT of the abdomen: evaluation of image improvement with use of noise reduction filters pilot study.Radiology. 2003; 228:251–256.

72. Hausleiter J, Martinoff S, Hadamitzky M, et al .Image quality and radiation exposure with a low tube voltage protocol for coronary CT angiography results of the PROTECTION II Trial.JACC Cardiovasc Imaging. 2010; 3:1113–1123.

73. LaBounty TM, Earls JP, Leipsic J, et al .Effect of a standardized quality-improvement protocol on radiation dose in coronary computed tomographic angiography.Am J Cardiol. 2010; 106:1663–1667.

74. Sigal-Cinqualbre AB, Hennequin R, Abada HT, et al .Low-kilovoltage multi-detector row chest CT in adults: feasibility and effect on image quality and iodine dose.Radiology. 2004; 231:169–174.

75. Abada HT, Larchez C, Daoud B, et al .MDCT of the coronary arteries: feasibility of low-dose CT with ECG-pulsed tube current modulation to reduce radiation dose.Am J Roentgenol. 2006; 186:S387–390.

76. Leschka S, Stolzmann P, Schmid FT, et al .Low kilovoltage cardiac dual-source CT: attenuation, noise, and radiation dose.Eur Radiol. 2008; 18:1809–1817.

77. Heyer CM, Mohr PS, Lemburg SP, et al .Image quality and radiation exposure at pulmonary CT angiography with 100- or 120-kVp protocol: prospective randomized study.Radiology. 2007; 245:577–583.

78. Stolzmann P, Leschka S, Scheffel H, et al .Dual-source CT in step-and-shoot mode: noninvasive coronary angiography with low radiation dose.Radiology. 2008; 249:71–80.

79. Pflederer T, Rudofsky L, Ropers D, et al .Image quality in a low radiation exposure protocol for retrospectively ECG-gated coronary CT angiography.Am J Roentgenol. 2009; 192:1045–1050.

80. Hausleiter J, Meyer T .Tips to minimize radiation exposure.J Cardiovasc Comput Tomogr. 2008; 2:325–327.

81. Mayo JR, Leipsic JA .Radiation dose in cardiac CT.Am J Roentgenol. 2009; 192:646–653.

82. Weustink AC, Mollet NR, Pugliese F, et al .Optimal electrocardiographic pulsing windows and heart rate: effect on image quality and radiation exposure at dual-source coronary CT angiography.Radiology. 2008; 248:792–798.

83. Weustink AC, Neefjes LA, Kyrzopoulos S, et al .Impact of heart rate frequency and variability on radiation exposure, image quality, and diagnostic performance in dual-source spiral CT coronary angiography.Radiology. 2009; 253:672–680.

84. Shuman WP, Branch KR, May JM, et al .Prospective versus retrospective ECG gating for 64-detector CT of the coronary arteries: comparison of image quality and patient radiation dose.Radiology. 2008; 248:431–437.

85. Sommer WH, Albrecht E, Bamberg F, et al .Feasibility and radiation dose of high-pitch acquisition protocols in patients undergoing dual-source cardiac CT.Am J Roentgenol. 2010; 195:1306–1312.

86. Hausleiter J, Meyer T, Hadamitzky M, et al .Radiation dose estimates from cardiac multislice computed tomography in daily practice: impact of different scanning protocols on effective dose estimates.Circulation. 2006; 113:1305–1310.

87. Earls JP, Berman EL, Urban BA, et al .Prospectively gated transverse coronary CT angiography versus retrospectively gated helical technique: improved image quality and reduced radiation dose.Radiology. 2008; 246:742–753.

88. Husmann L, Valenta I, Gaemperli O, et al .Feasibility of low-dose coronary CT angiography: first experience with prospective ECG-gating.Eur Heart J. 2008; 29:191–197.

89. Maruyama T, Takada M, Hasuike T, et al .Radiation dose reduction and coronary assessability of prospective electrocardiogram-gated computed tomography coronary angiography: comparison with retrospective electrocardiogram-gated helical scan.J Am Coll Cardiol. 2008; 52:1450–1455.

90. Leipsic J, Labounty TM, Heilbron B, et al .Estimated radiation dose reduction using adaptive statistical iterative reconstruction in coronary CT angiography: the ERASIR study.Am J Roentgenol. 2010; 195:655–660.

91. Bittencourt MS, Schmidt B, Seltmann M, et al .Iterative reconstruction in image space (IRIS) in cardiac computed tomography: initial experience.Int J Cardiovasc Imaging. 2011; 27:1081–1087.

92. Fleischmann D, Boas FE .Computed tomography—old ideas and new technology.Eur Radiol. 2011; 21:510–517.

93. Pontana F, Duhamel A, Pagniez J, et al .Chest computed tomography using iterative reconstruction vs filtered back projection (Part 2): image quality of low-dose CT examinations in 80 patients.Eur Radiol. 2011; 21:636–643.

94. Schindera ST, Diedrichsen L, Muller HC, et al .Iterative reconstruction algorithm for abdominal multidetector CT at different tube voltages: assessment of diagnostic accuracy, image quality, and radiation dose in a phantom study.Radiology. 2011; 260:454–462.

95. Baumueller S, Winklehner A, Karlo C, et al .Low-dose CT of the lung: potential value of iterative reconstructions.Eur Radiol. 2012; 22:2597–2606.

96. May MS, Wust W, Brand M, et al .Dose reduction in abdominal computed tomography: intraindividual comparison of image quality of full-dose standard and half-dose iterative reconstructions with dual-source computed tomography.Invest Radiol. 2011; 46:465–470.

97. Tomizawa N, Nojo T, Akahane M, et al .AdaptiveIterative Dose Reduction in coronary CT angiography using 320-row CT: assessment of radiation dose reduction and image quality.J Cardiovasc Comput Tomogr. 2012; 6:318–324.

98. Chen MY, Steigner ML, Leung SW, et al .Simulated 50% radiation dose reduction in coronary CT angiography using adaptive iterative dose reduction in three-dimensions (AIDR3D).Int J Cardiovasc Imaging. 2013; 29:1167–1175.

99. Yamada Y, Jinzaki M, Hosokawa T, et al .Dose reduction in chest CT: comparison of the adaptive iterative dose reduction 3D, adaptive iterative dose reduction, and filtered back projection reconstruction techniques.Eur J Radiol. 2012; 81:4185–4195.

100. Chen B, Richard S, Christianson O, et al .CT performance as a variable function of resolution, noise, and task property for iterative reconstructions.SPIE. 2012; 8313:83131K

101. Husarik DB, Marin D, Samei E, et al .Radiation dose reduction in abdominal computed tomography during the late hepatic arterial phase using a model-based iterative reconstruction algorithm: how low can we go? Invest Radiol. 2012; 47:468–474.

102. Pickhardt PJ, Lubner MG, Kim DH, et al .Abdominal CT with Model-Based Iterative Reconstruction (MBIR): initial results of a prospective trial comparing ultralow-dose with standard-dose imaging.Am J Roentgenol. 2012; 199:1266–1274.

Keywords:

iterative image reconstruction; cardiac computed tomography; image noise; artifacts; radiation dose

Copyright © 2014 by Lippincott Williams & Wilkins

Login

Search for Similar Articles
You may search for similar articles that contain these same keywords or you may modify the keyword list to augment your search.