Lung cancer screening with ultra-low dose CT using full iterative reconstruction

Improved vascular depiction and image quality through deep learning reconstruction of CT hepatic arteriography during transcatheter arterial chemoembolization

PURPOSE

To evaluate the effect of deep learning reconstruction (DLR) on vascular depiction, tumor enhancement, and image quality of computed tomography hepatic arteriography (CTHA) images acquired during transcatheter arterial chemoembolization (TACE).

METHODS

Institutional review board approval was obtained. Twenty-seven patients (18 men and 9 women, mean age, 75.7 years) who underwent CTHA immediately before TACE were enrolled. All images were reconstructed using three reconstruction algorithms: hybrid-iterative reconstruction (hybrid-IR), DLR with mild strength (DLR-M), and DLR with strong strength (DLR-S). Vascular depiction, tumor enhancement, feeder visualization, and image quality of CTHA were quantitatively and qualitatively assessed by two radiologists and compared between the three reconstruction algorithms.

RESULTS

The mean signal-to-noise ratios (SNR) of sub-segmental arteries and sub-sub-segmental arteries, and the contrast-to-noise ratio (CNR) of tumors, were significantly higher on DLR-S than on DLR-M and hybrid-IR (P < 0.001). The mean qualitative score for sharpness of sub-segmental and sub-sub-segmental arteries was significantly better on DLR-S than on DLR-M and hybrid-IR (P < 0.001). There was no significant difference in the feeder artery detection rate of automated feeder artery detection software among three reconstruction algorithms (P = 0.102). The contrast, continuity, and confidence level of feeder artery detection was significantly better on DLR-S than on DLR-M (P = 0.013, 0.005, and 0.001) and hybrid-IR (P < 0.001, P = 0.002, and P < 0.001). The weighted kappa values between two readers for qualitative scores of feeder artery visualization were 0.807-0.874. The mean qualitative scores for sharpness, granulation, and diagnostic acceptability of CTHA were better on DLR-S than on DLR-M and hybrid-IR (P < 0.001).

CONCLUSIONS

DLR significantly improved the SNR of small hepatic arteries, the CNR of tumor, and feeder artery visualization on CTHA images. DLR-S seems to be better suited to routine CTHA in TACE than does hybrid-IR.

Lung Cancer Screening with Low-Dose CT: What We Have Learned in Two Decades of ITALUNG and What Is Yet to Be Addressed

The ITALUNG trial started in 2004 and compared lung cancer (LC) and other-causes mortality in 55-69 years-aged smokers and ex-smokers who were randomized to four annual chest low-dose CT (LDCT) or usual care. ITALUNG showed a lower LC and cardiovascular mortality in the screened subjects after 13 years of follow-up, especially in women, and produced many ancillary studies. They included recruitment results of a population-based mimicking approach, development of software for computer-aided diagnosis (CAD) and lung nodules volumetry, LDCT assessment of pulmonary emphysema and coronary artery calcifications (CAC) and their relevance to long-term mortality, results of a smoking-cessation intervention, assessment of the radiations dose associated with screening LDCT, and the results of biomarkers assays. Moreover, ITALUNG data indicated that screen-detected LCs are mostly already present at baseline LDCT, can present as lung cancer associated with cystic airspaces, and can be multiple. However, several issues of LC screening are still unaddressed. They include the annual vs. biennial pace of LDCT, choice between opportunistic or population-based recruitment. and between uni or multi-centre screening, implementation of CAD-assisted reading, containment of false positive and negative LDCT results, incorporation of emphysema. and CAC quantification in models of personalized LC and mortality risk, validation of ultra-LDCT acquisitions, optimization of the smoking-cessation intervention. and prospective validation of the biomarkers.

Ultra-Low Dose CT Chest in Acute COVID-19 Pneumonia: A Pilot Study from India

The rapid increase in the number of CT acquisitions during the COVID-19 pandemic raised concerns about increased radiation exposure to patients and the resultant radiation-induced health risks. It prompted researchers to explore newer CT techniques like ultra-low dose CT (ULDCT), which could improve patient safety. Our aim was to study the utility of ultra-low dose CT (ULDCT) chest in the evaluation of acute COVID-19 pneumonia with standard-dose CT (SDCT) chest as a reference standard. This was a prospective study approved by the institutional review board. 60 RT-PCR positive COVID-19 patients with valid indication for CT chest underwent SDCT and ULDCT. ULDCT and SDCT were compared in terms of objective (noise and signal-to-noise ratio) and subjective (noise, sharpness, artifacts and diagnostic confidence) image quality, various imaging patterns of COVID-19, CT severity score and effective radiation dose. The sensitivity, specificity, positive and negative predictive value, and diagnostic accuracy of ULDCT for detecting lung lesions were calculated by taking SDCT as a reference standard. The mean age of subjects was 47.2 ± 10.7 years, with 66.67% being men. 90% of ULDCT scans showed no/minimal noise and sharp images, while 93.33% had image quality of high diagnostic confidence. The major imaging findings detected by SDCT were GGOs (90%), consolidation (76.67%), septal thickening (60%), linear opacities (33.33%), crazy-paving pattern (33.33%), nodules (30%), pleural thickening (30%), lymphadenopathy (30%) and pleural effusion (23.33%). Sensitivity, specificity and diagnostic accuracy of ULDCT for detecting most of the imaging patterns were 100% (p < 0.001); except for GGOs (sensitivity: 92.59%, specificity: 100%, diagnostic accuracy: 93.33%), consolidation (sensitivity: 100%, specificity: 71.43%, diagnostic accuracy: 93.33%) and linear opacity (sensitivity: 90.00%, specificity: 100%, diagnostic accuracy: 96.67%). CT severity score (range: 15-25) showed 100% concordance on SDCT and ULDCT, while effective radiation dose was 4.93 ± 1.11 mSv and 0.26 ± 0.024 mSv, respectively. A dose reduction of 94.38 ± 1.7% was achieved with ULDCT. Compared to SDCT, ULDCT chest yielded images of reasonable and comparable diagnostic quality with the advantage of significantly reduced radiation dose; thus, it can be a good alternative to SDCT in the evaluation of COVID-19 pneumonia.

Can optimized model-based iterative reconstruction improve the contrast of liver lesions in CT?

BACKGROUND

Computed tomography is a standard imaging procedure for the detection of liver lesions, such as metastases, which can often be small and poorly contrasted, and therefore hard to detect. Advances in image reconstruction have shown promise in reducing image noise and improving low-contrast detectability.

PURPOSE

To examine a novel, specialized, model-based iterative reconstruction (MBIR) technique for improved low-contrast liver lesion detection.

MATERIAL AND METHODS

Patient images with reported poorly contrasted focal liver lesions were retrospectively reconstructed with the low-contrast attenuating algorithm (FIRST-LCD) from primary raw data. Liver-to-lesion contrast, signal-to-noise, and contrast-to-noise ratios for background and liver noise for each lesion were compared for all three FIRST-LCD presets with the established hybrid iterative reconstruction method (AIDR-3D). An additional visual conspicuity score was given by two experienced radiologists for each lesion.

RESULTS

A total of 82 lesions in 57 examinations were included in the analysis. All three FIRST-LCD algorithms provided statistically significant increases in liver-to-lesion contrast, with FIRST_MILD showing the largest increase (40.47 HU in AIDR-3D; 45.84 HU in FIRST_MILD; P < 0.001). Substantial improvement was shown in contrast-to-noise metrics. Visual analysis of the lesions shows decreased lesion visibility with all FIRST methods in comparison to AIDR-3D, with FIRST_STR showing the closest results (P < 0.001).

CONCLUSION

Objective image metrics show promise for MBIR methods in improving the detectability of low-contrast liver lesions; however, subjective image quality may be perceived as inferior. Further improvements are necessary to enhance image quality and lesion detection.

Pulmonary nodule volumetric accuracy of a deep learning-based reconstruction algorithm in low-dose computed tomography: A phantom study

PURPOSE

To compare the image properties and pulmonary nodule volumetric accuracies among deep learning-based reconstruction (DLR), filtered back projection (FBP), and hybrid iterative reconstruction (hybrid IR) in low-dose computed tomography (LDCT).

METHODS

A multipurpose chest phantom containing artificial spherical pulmonary nodules with 5-, 8-, 10-, and 12-mm diameters and Hounsfield units (HUs) of -630 and +100 HU was scanned 20 times at a standard dose, based on a low-dose screening CT trial, and at 1/2, 1/6, and 1/12 of the standard dose. To assess noise reduction performance and volumetric accuracy, the standard deviations (SDs) of the pixel values and volumetric percentage errors (PEs) were compared among FBP, hybrid IR, and DLR. The noise non-stationarity index (NNSI) was calculated from 20 image replicates and compared among FBP, hybrid IR, and DLR to evaluate noise stationarity.

RESULTS

The SD reduction rates for FBP in hybrid IR and DLR were 62 %-85 % and 79 %-90 %, respectively. For the four nodules with +100 HU, the PE of all reconstruction methods was <±25 % (not clinically relevant). For the four nodules with -630 HU, the PEs were equivalent or lower for hybrid IR and DLR than for FBP, and the PE difference between hybrid IR and DLR ranged from 0 % to 7%. The NNSI was significantly higher for DLR than for FBP and hybrid IR (p < 0.01).

CONCLUSIONS

Greater noise suppression was achieved with DLR than with hybrid IR without compromising nodule volumetric accuracy in LDCT despite the representative noise non-stationarity.

Single CT Appointment for Double Lung and Colorectal Cancer Screening: Is the Time Ripe?

Annual screening of lung cancer (LC) with chest low-dose computed tomography (CT) and screening of colorectal cancer (CRC) with CT colonography every 5 years are recommended by the United States Prevention Service Task Force. We review epidemiological and pathological data on LC and CRC, and the features of screening chest low-dose CT and CT colonography comprising execution, reading, radiation exposure and harm, and the cost effectiveness of the two CT screening interventions. The possibility of combining chest low-dose CT and CT colonography examinations for double LC and CRC screening in a single CT appointment is then addressed. We demonstrate how this approach appears feasible and is already reasonable as an opportunistic screening intervention in 50–75-year-old subjects with smoking history and average CRC risk. In addition to the crucial role Computer Assisted Diagnosis systems play in decreasing the test reading times and the need to educate radiologists in screening chest LDCT and CT colonography, in view of a single CT appointment for double screening, the following uncertainties need to be solved: (1) the schedule of the screening CT; (2) the effectiveness of iterative reconstruction and deep learning algorithms affording an ultra-low-dose CT acquisition technique and (3) management of incidental findings. Resolving these issues will imply new cost-effectiveness analyses for LC screening with chest low dose CT and for CRC screening with CT colonography and, especially, for the double LC and CRC screening with a single-appointment CT.

Usefulness of model-based iterative reconstruction in low-dose lumbar spine CT after spine surgery with metal implant: a phantom study

BACKGROUND

The major problems of computed tomography (CT) imaging include radiation exposure and severe artifacts caused by operative implants.

PURPOSE

To evaluate the usefulness of the metal artifact reduction algorithm and model-based iterative reconstruction (MBIR) in postoperative low-dose (LD) spine CT.

MATERIAL AND METHODS

A CT torso phantom was scanned at standard-dose (SD) and LD settings. The CT images were reconstructed by three methods: hybrid iterative reconstruction (HIR); metal artifact reduction; and MBIR. The radiation dose of the phantom imaging was evaluated by volume CT dose index (mGy), dose length product (DLP, mGy × cm), and effective dose (mSv). The image quality of the six images was visually evaluated and analyzed using Scheffe's paired comparison method. The average preference of each method was calculated based on the comparative scores. The task transfer function (TTF) and noise power spectrum for HIR and MBIR were also measured.

RESULTS

The respective radiation-dose-related parameters of the SD and LD conditions were: volume CT dose index = 10.2 and 1.2 mGy; DLP = 277.9 and 33.9 mGy × cm; and effective dose = 4.2 and 0.5 mSv. The average preference for diagnostic acceptability of MBIR at LD was not significantly different from the other reconstructions of SD data. MBIR successfully reduced metal artifacts in the LD condition. The 10% TTF was higher for HIR at SD and higher for MBIR at LD.

CONCLUSION

MBIR is useful for LD spine CT after spine surgery with metal implant.

What is the point of innovation in patient dose monitoring?

The Medical Futurist says that radiology is one of the fastest growing and developing areas of medicine, and therefore this might be the speciality in which we can expect to see the largest steps in development. So why do they think that, and does it apply to dose monitoring? The move from retrospective dose evaluation to a proactive dose management approach represents a serious area of research. Indeed, artificial intelligence and machine learning are consistently being integrated into best-in-class dose management software solutions. The development of clinical analytics and dashboards are already supporting operators in their decision-making, and these optimisations - if taken beyond a single machine, a single department, or a single health network - have the potential to drive real and lasting change. The question is for whom exactly are these innovations being developed? How can the patient know that their scan has been performed to the absolute best that the technology can deliver? Do they know or even care how much their lifetime risk for developing cancer has changed post examination? Do they want a personalised size-specific dose estimate or perhaps an individual organ dose assessment to share on Instagram? Let's get real about the clinical utility and regulatory application of dose monitoring, and shine a light on the shared responsibility in applying the technology and the associated innovations.

Deep Learning Reconstruction at CT: Phantom Study of the Image Characteristics

OBJECTIVES

Noise, commonly encountered on computed tomography (CT) images, can impact diagnostic accuracy. To reduce the image noise, we developed a deep-learning reconstruction (DLR) method that integrates deep convolutional neural networks into image reconstruction. In this phantom study, we compared the image noise characteristics, spatial resolution, and task-based detectability on DLR images and images reconstructed with other state-of-the art techniques.

METHODS

We scanned a phantom harboring cylindrical modules with different contrast on a 320-row detector CT scanner. Phantom images were reconstructed with filtered back projection, hybrid iterative reconstruction, model-based iterative reconstruction, and DLR. The standard deviation of the CT number and the noise power spectrum were calculated for noise characterization. The 10% modulation-transfer function (MTF) level was used to evaluate spatial resolution; task-based detectability was assessed using the model observer method.

RESULTS

On images reconstructed with DLR, the noise was lower than on images subjected to other reconstructions, especially at low radiation dose settings. Noise power spectrum measurements also showed that the noise amplitude was lower, especially for low-frequency components, on DLR images. Based on the MTF, spatial resolution was higher on model-based iterative reconstruction image than DLR image, however, for lower-contrast objects, the MTF on DLR images was comparable to images reconstructed with other methods. The machine observer study showed that at reduced radiation-dose settings, DLR yielded the best detectability.

CONCLUSION

On DLR images, the image noise was lower, and high-contrast spatial resolution and task-based detectability were better than on images reconstructed with other state-of-the art techniques. DLR also outperformed other methods with respect to task-based detectability.

Comparison of ultra-low dose chest CT scanning protocols for the detection of pulmonary nodules: a phantom study

PURPOSE

To test ultra-low-dose computed tomography (ULDCT) scanning protocols for the detection of pulmonary nodules (PN).

METHODS

A chest phantom containing 19 solid and 11 subsolid PNs was scanned on a third-generation dual-source computed tomography (CT) scanner. Five ULDCT scans (Sn100kVp and 120, 70, 50, 30, and 20 reference mAs, using tube current modulation), reconstructed with iterative reconstruction (IR) algorithm at strength levels 2, 3, 4, and 5, were compared with standard CT (120kVp, 150 reference mAs, using tube current modulation). PNs were subjectively assessed according to a 4-point scale: 0, nondetectable nodule; 1, detectable nodule, very unlikely to be correctly measured; 2, detectable nodule, likely to be correctly measured; 3, PN quality equal to standard of reference. PN scores were analysed according to the Lung Imaging Reporting and Data System (Lung-RADS), simulating detection of nodules at baseline and incidence screening round.

RESULTS

For the baseline round, there were 17 Lung-RADS 2, 4 Lung-RADS 3, 8 Lung-RADS 4A, and 1 Lung-RADS 4B PNs. They were detectable in any ULDCT protocol, with the exception of 1 nondetectable part-solid nodule in 1 scanning protocol (120 reference mAs; IR strength: 3). For the incidence round, there were 4 Lung-RADS 2, 14 Lung-RADS 3, 2 Lung-RADS 4A, and 10 Lung-RADS 4B PNs. Ten were nondetectable in at least one ULDCT dataset; however, they were at least detectable in ULDCT with 70 reference mAs (IR strength: 4 and 5).

CONCLUSIONS

ULDCT scanning protocols allowing the detection of PNs can be proposed for the purpose of lung cancer screening.

Implementation of lung cancer screening at the national level: Polish example

In Poland the national demonstration lung cancer screening program is about to be started in 2019. We share our concerns and discussing most important topics to be resolved while preparing such a program. The decisions made are virtually based on available scientific data and the results of two randomized controlled trials but also on the personal experience gained during the lung cancer screening studies performed in Poland. The most important and comprehensive guidelines and statements, both European and American, have been searched to find an optimal solution adjusted to the Polish national circumstances-as we assume that should be done in each country implementing such a program.

Dose estimation of ultra-low-dose chest CT to different sized adult patients

OBJECTIVES

To evaluate the effect of patient size on radiation dose for standard CT (SD-CT), ultra-low-dose CT (ULD-CT) and two-view digital radiography (DR).

METHODS

Dosimeters were distributed within the lungs of chest phantoms representing males of 65 kg and 82 kg (body mass indices 23 and 29). In contrast to SD-CT and DR which include automatic exposure control (AEC), the ULD scan employs a fixed mAs value. The phantoms were exposed to SD, ULD and DR while recording lung doses. Projected dose data were calculated from the phantoms. The resulting exposure settings were used in Monte Carlo programs to determine the effective dose for a standard-sized (BMI 24.2) adult male (170 cm/70 kg) and female (160 cm/59 kg). Patients previously examined by both ULD- and SD-CT were identified to determine post hoc size-specific dose estimates (SSDEs).

RESULTS

ULD-CT dose was inversely related to patient size; average lung doses summarised in terms of patient size BMI_23/29 are 5.2/8.1 (SD-CT), 0.56/0.35 (ULD-CT) and 0.05/0.13 mGy (DR), while the effective doses for these techniques on a standard-sized male were 2.9, 0.16 and 0.03 mSv and 2.3, 0.247 and 0.024 mSv for a standard-sized female respectively. SSDEs for 15 patients (averages: BMI 26, range 18-37) averaged 5.5 mGy (3.6-10) for SD-CT and 0.35 mGy (0.42-0.27) for ULD-CT.

CONCLUSIONS

The effective doses for a standard-sized male and female examined by ULD-CT are (respectively) ~ 6%/~ 11% of SD-CT and ~ 5/~ 10 times higher than DR. ULD-CT gave a lower radiation dosage to larger patients than DR. AEC is warranted in ULD-CT for improved dose consistency.

KEY POINTS

• For standard-sized patients, ULD-CT dose level is ~ 6%/~ 11% of SD-CT, and ~ 5/~ 10 times higher than DR. For larger patients, ULD-CT is currently being used clinically at lower dose levels than DR. • Using ULD-CT should greatly reduce the risk of late effects from ionising radiation. • AEC in ULD-CT is desirable for increased consistency in patient dose.

Radiologist performance in the detection of lung cancer using CT

AIM

To measure the level of radiologists' performance in lung cancer detection, and to explore radiologists' performance in cancer specialised and non-specialised centres.

MATERIALS AND METHODS

Thirty radiologists read 60 chest computed tomography (CT) examinations. Thirty cases had surgically or biopsy-proven lung cancer and 30 were cancer-free cases. The cancer cases were validated by four expert radiologists who located the malignant lung nodules. Reader performance was evaluated by calculating sensitivity, location sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC). In addition, sensitivity at fixed specificity (0.794) was computed from each reader's estimated ROC curve.

RESULTS

The radiologists had a mean sensitivity of 0.749, sensitivity at fixed specificity of 0.744, location sensitivity of 0.666, specificity of 0.81 and AUC of 0.846. Radiologists in the specialised and non-specialised cancer centres had the following (specialised, non-specialised) pairs of values: sensitivity=(0.80, 0.719); sensitivity for fixed 0.794 specificity=(0.752, 0.740); location sensitivity=(0.712, 0.637); specificity=(0.794, 0.82) and AUC=(0.846, 0.846).

CONCLUSION

The efficacy of radiologists was comparable to other studies. Furthermore, AUC outcomes were similar for specialised and non-specialised cancer centre radiologists, suggesting they have similar discriminatory ability and that the higher sensitivity and lower specificity for specialised-centre radiologists can be attributed to them being less conservative in interpreting case images.

Chest CT Scan at Radiation Dose of a Posteroanterior and Lateral Chest Radiograph Series: A Proof of Principle in Lymphangioleiomyomatosis

BACKGROUND

Given the rising utilization of medical imaging and the risks of radiation, there is increased interest in reducing radiation exposure. The objective of this study was to evaluate, as a proof of principle, CT scans performed at radiation doses equivalent to that of a posteroanterior and lateral chest radiograph series in the cystic lung disease lymphangioleiomyomatosis (LAM).

METHODS

From November 2016 to May 2018, 105 consecutive subjects with LAM received chest CT scans at standard and ultra-low radiation doses. Standard and ultra-low-dose images, respectively, were reconstructed with routine iterative and newer model-based iterative reconstruction. LAM severity can be quantified as cyst score (percentage of lung occupied by cysts), an ideal benchmark for validating CT scans performed at a reduced dose compared with a standard dose. Cyst scores were quantified using semi-automated software and evaluated by linear correlation and Bland-Altman analysis.

RESULTS

Overall, ultra-low-dose CT scans represented a 96% dose reduction, with a median dose equivalent to 1 vs 22 posteroanterior and lateral chest radiograph series (0.14 mSv; 5th-95th percentile, 0.10-0.20 vs standard dose 3.4 mSv; 5th-95th percentile, 1.5-7.4; P < .0001). The mean difference in cyst scores between ultra-low- and standard-dose CT scans was 1.1% ± 2.0%, with a relative difference in cyst score of 11%. Linear correlation coefficient was excellent at 0.97 (P < .0001).

CONCLUSIONS

In LAM chest CT scan at substantial radiation reduction to doses equivalent to that of a posteroanterior and lateral chest radiograph series provides cyst score quantification similar to that of standard-dose CT scan.

TRIAL REGISTRY

ClinicalTrials.gov; Nos.: NCT00001465 and NCT00001532; URL: www.clinicaltrials.gov.

Low-dose CT examination for rib fracture evaluation: A pilot study

The aim of this study was to assess the applicability of low-dose thoracic computed tomography (CT) in the diagnosis of rib fractures.A total of 37 trauma patients were selected for CT scanning using a noise index (NI) model. Each patient was scanned at both NI = 11 and NI = 26, while the other scanning parameters were kept the same. The scanning dose length product (DLP) and effective dose (ED) were recorded after each examination. Two radiologists diagnosed the rib fractures by degree (I, II, III, and IV) using Bone Reading software and axial images. Image quality was scored by 2 experienced radiologists using a 5-point scale. The numbers and degrees of rib fractures for different NIs were recorded and tested using the Chi-squared test. The interobserver differences were determined by kappa statistics.The CTDIvols and EDs for NI = 11 and NI = 26 were 9.82 ± 4.78, 5.75 ± 2.75, and 2.14 ± 1.19 and 1.24 ± 0.73, respectively; the latter was decreased by 78.2% and 78.4% relative to the former. Low-dose thoracic CT was feasible for the auxiliary diagnosis of rib fractures using Bone Reading software (P > .05). There was perfect interobserver concordance in terms of diagnostic acceptability (kappa = 0.931, 0.905).The use of an appropriate low-dose CT scanning technique is satisfactory for the assessment and diagnosis of rib fractures.

Neointimal formation after carotid artery stenting: phantom and clinical evaluation of model-based iterative reconstruction (MBIR)

OBJECTIVES

The objective of this study was to investigate the usefulness of model-based iterative reconstruction (IR) for detecting neointimal formations after carotid artery stenting.

METHODS

In a cervical phantom harbouring carotid artery stents, we placed simulated neointimal formations measuring 0.40, 0.60, 0.80 and 1.00 mm along the stent wall. The thickness of in-stent neointimal formations was measured on images reconstructed with filtered-back projection (FBP), hybrid IR (AIDR 3D), and model-based IR (FIRST). The clinical study included 43 patients with carotid stents. Cervical computed tomography (CT) images obtained on a 320-slice scanner were reconstructed with AIDR 3D and FIRST. Five blinded observers visually graded the likelihood of neointimal formations on AIDR 3D and AIDR 3D plus FIRST images. Carotid ultrasound images were the reference standard. We analysed results of visual grading by using a Jack-knife type receiver observer characteristics analysis software.

RESULTS

In the phantom study, the difference between the measured and the true diameter of the neointimal formations was smaller on FIRST than FBP or AIDR 3D images. In the clinical study, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of AIDR 3D were 58%, 88%, 83%, 67% and 73%, respectively. For AIDR 3D plus FIRST images they were 84%, 78%, 80%, 82% and 81%, respectively. The mean area under the curve was significantly higher on AIDR 3D plus FIRST than AIDR 3D images (0.82 vs 0.72; p < 0.01).

CONCLUSIONS

The model-based IR algorithm helped to improve diagnostic performance for the detection of neointimal formations after carotid artery stenting.

KEY POINTS

• Neointimal formations can be visualised more accurately with model-based IR. • Model-based IR improves the detection of neointimal formations after carotid artery stenting. • Model-based IR is suitable for follow up after carotid artery stenting.

Diagnostic accuracy of in-stent restenosis using model-based iterative reconstruction at coronary CT angiography: initial experience

OBJECTIVE

The purpose of our study was to compare the diagnostic performance of coronary CT angiography (CTA) subjected to model-based iterative reconstruction (IR) or hybrid IR to rule out coronary in-stent restenosis.

METHODS

We enrolled 16 patients who harboured 22 coronary stents. They underwent coronary CTA on a 320-slice CT scanner. The images were reconstructed with hybrid IR (AIDR 3D) and model-based IR (FIRST) algorithms. We calculated the stent lumen attenuation increase ratio and measured the visible stent lumen diameter. Two blinded observers visually graded the likelihood of in-stent restenosis (lesions ≥ 50%) on hybrid IR and FIRST images.

RESULTS

The stent lumen attenuation increase ratio on FIRST- was lower than on AIDR 3D images (0.20 vs 0.32). The ratio of the visible- compared to the true stent lumen diameter was higher on FIRST- than AIDR 3D images (52.5 vs 47.5%). Invasive coronary angiography identified five stents (22.7%) with significant in-stent restenosis. The use of FIRST improved the sensitivity (60 vs 100%), positive (75.0 vs 83.3%) and negative predictive value (88.9 vs 100%) and the accuracy (86.4 vs 95.5%) for the detection of in-stent restenosis. Specificity was 94.1% for both reconstruction methods.

CONCLUSION

The model-based IR algorithm may improve diagnostic performance for the detection of in-stent restenosis. Advances in knowledge: Compared to hybrid IR, the new model-based IR algorithm reduced blooming artefacts and improved the image quality. It can be expected to improve diagnostic performance for the detection of in-stent restenosis on coronary CTA images.