Pediatric head computed tomography with advanced modeled iterative reconstruction: focus on image quality and reduction of radiation dose

Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans

In this retrospective study, we aimed to assess the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast head computed tomography (CT) images. In total, 152 adult head CT scans (77 female, 75 male; mean age 69.4 ± 18.3 years) obtained from three different CT scanners using different protocols between March and April 2021 were included. CT images were reconstructed using filtered-back projection (FBP), iterative reconstruction (IR), and post-processed using a deep learning-based algorithm (PS). Post-processing significantly reduced noise in FBP-reconstructed images (up to 15.4% reduction) depending on the protocol, leading to improvements in signal-to-noise ratio of up to 19.7%. However, when deep learning-based post-processing was applied to FBP images compared to IR alone, the differences were inconsistent and partly non-significant, which appeared to be protocol or site specific. Subjective assessments showed no significant overall improvement in image quality for all reconstructions and post-processing. Inter-rater reliability was low and preferences varied. Deep learning-based denoising software improved objective image quality compared to FBP in routine head CT. A significant difference compared to IR was observed for only one protocol. Subjective assessments did not indicate a significant clinical impact in terms of improved subjective image quality, likely due to the low noise levels in full-dose images.

Pediatric low-dose head CT: Image quality improvement using iterative model reconstruction

PURPOSE

To evaluate the differences in pediatric non-contrast low-dose head computed tomography (CT) between filtered-back projection and iterative model reconstruction using objective and subjective image quality evaluation.

METHODS

A retrospective study evaluated children undergoing low-dose non-contrast head CT. All CT scans were reconstructed using both filtered-back projection and iterative model reconstruction. Objective image quality analysis was performed using contrast and signal-to-noise ratios for the supra- and infratentorial brain regions of identical regions of interest on the two reconstruction methods. Two experienced pediatric neuroradiologists evaluated subjective image quality, visibility of structures, and artifacts.

RESULTS

We evaluated 233 low-dose brain CT scans of 148 pediatric patients. There was a ∼2-fold improvement in the contrast-to-noise ratio between gray and white matter in the infra- and supratentorial regions (p < 0.001) using iterative model reconstruction compared to filtered-back projection. The white and gray matter signal-to-noise ratio improved more than 2-fold using iterative model reconstruction (p < 0.001). Furthermore, radiologists graded anatomical details, gray-white matter differentiation, beam hardening artifacts, and image quality using iterative model reconstructions as superior to filtered-back projection reconstructions.

CONCLUSION

Iterative model reconstructions had better contrast-to-noise and signal-to-noise ratios with fewer artifacts in pediatric CT brain scans using low-dose radiation protocols. This image quality improvement was demonstrated in the supra- and infratentorial regions. This method thus comprises an important tool for reducing children's exposure while maintaining diagnostic capability.

[Imaging after trauma in clinics and practice for children and adolescents : Part 1 of the results of a nationwide online survey of the Pediatric Traumatology Section of the German Trauma Society]

BACKGROUND

The indication for radiography should strictly follow the ALARA (as low as reasonably achievable) principle in pediatric and adolescent trauma patients. The effect of radiation on the growing sensitive tissue of these patients should not be disregarded.

QUESTION

The Pediatric Traumatology Section (SKT) of the German Trauma Society (DGU) wanted to clarify how the principle is followed in trauma care.

METHODS

An online survey was open for 10 weeks. Target groups were trauma surgeons, pediatric surgeons, general surgeons, and orthopedic surgeons.

RESULTS

From Nov. 15, 2019, to Feb. 29, 2020, 788 physicians participated: branch office 20.56%, MVZ 4.31%, hospital 75.13%; resident 16.62%, senior 38.07%, chief 22.59%. By specialist qualification, the distribution was: 38.34% surgery, 33.16% trauma surgery, 36.66% special trauma surgery, 70.34% orthopedics and trauma surgery, 18.78% pediatric surgery. Frequency of contact with fractures in the above age group was reported as 37% < 10/month, 27% < 20/M, 36% > 20/M. About 52% always request radiographs in 2 planes after acute trauma. X-ray of the opposite side for unclear findings was rejected by 70%. 23% use sonography regularly in fracture diagnosis. In polytrauma children and adolescents, whole-body CT is never used in 18%, rarely in 50%, and standard in 14%.

DISCUSSION

The analysis shows that there is no uniform radiological management of children and adolescents with fractures among the respondents.

CONCLUSION

Comparing the results of the survey with the consensus findings of the SKT recently published in this journal, persuasion is still needed to change the use of radiography in primary diagnosis.

Adaptation of Deep Learning Image Reconstruction for Pediatric Head CT: A Focus on the Image Quality

Purpose

To assess the effect of deep learning image reconstruction (DLIR) for head CT in pediatric patients.

Materials and Methods

We collected 126 pediatric head CT images, which were reconstructed using filtered back projection, iterative reconstruction using adaptive statistical iterative reconstruction (ASiR)-V, and all three levels of DLIR (TrueFidelity; GE Healthcare). Each image set group was divided into four subgroups according to the patients' ages. Clinical and dose-related data were reviewed. Quantitative parameters, including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and qualitative parameters, including noise, gray matter-white matter (GM-WM) differentiation, sharpness, artifact, acceptability, and unfamiliar texture change were evaluated and compared.

Results

The SNR and CNR of each level in each age group increased among strength levels of DLIR. High-level DLIR showed a significantly improved SNR and CNR (p < 0.05). Sequential reduction of noise, improvement of GM-WM differentiation, and improvement of sharpness was noted among strength levels of DLIR. Those of high-level DLIR showed a similar value as that with ASiR-V. Artifact and acceptability did not show a significant difference among the adapted levels of DLIR.

Conclusion

Adaptation of high-level DLIR for the pediatric head CT can significantly reduce image noise. Modification is needed while processing artifacts.

Application of Different Levels of Advanced Modeling Iterative Reconstruction in Brain CT Scanning

BACKGROUND

Advanced Modeling Iterative Reconstruction (ADMIRE) algorithm has five intensity levels; it is important to study which algorithm is better for brain CT scanning.

OBJECTIVE

The aim of the study is to compare the influence of different strength levels of ADMIRE and traditional Filtered Back Projection (FBP) on image quality in brain CT scanning.

METHODS

60 patients were retrospectively selected, and the data from each of these patients' brains were reconstructed by four different reconstruction methods (FBP, ADMIRE1, ADMIRE3, and ADMIRE5). A five-point Likert Scale was implemented to evaluate the subjective image quality. Image noise, CT value of brain tissue , signal-to-noise ratio (SNR) of gray white matter, contrast-to-noise ratio (CNR), and beam hardening artifact index (AI) of the posterior fossa, were measured for evaluating the objective image quality. Finally, the differences between the subjective and objective evaluations were compared.

RESULTS

There were no statistical differences observed in CT values of gray matter and white matter between the four groups (all P >0.05). The image noise gradually decreased with the increase of ADMIRE algorithm level. The AI exhibited no statistical difference between the four groups (F =0.793, P =0.499), but it tended to decrease slightly with the increase of ADMIRE algorithm level. Compared to other groups (all p <0.001), the ADMIRE5 group demonstrated the best objective image quality. Nevertheless, the highest subjective score was observed in the ADMIRE3 group, which exhibited significant differences with other images (all P <0.001).

CONCLUSION

ADMIRE algorithm can clearly improve image quality, but it cannot significantly improve the linear sclerosis artifacts in the posterior cranial fossa. Based on the subjective evaluation of image quality, ADMIRE3 algorithm is recommended in brain CT scanning.

Pitfalls in the interpretation of pediatric head CTs: what the emergency radiologist needs to know

Pediatric radiology studies can be some of the most anxiety-inducing imaging examinations encountered in practice. This can be in part due to the wide range of normal anatomic appearances inherent to the pediatric population that create potential interpretive pitfalls for radiologists. The pediatric head is no exception; for instance, the inherent greater water content within the neonatal brain compared to older patients could easily be mistaken for cerebral edema, and anatomic variant calvarial sutures can be mistaken for skull fractures. This article reviews potential pitfalls emergency radiologists may encounter in practice when interpreting pediatric head CTs, including trauma, extra-axial fluid collections, intra-axial hemorrhage, and ventriculoperitoneal shunt complications.

The optimisation of paediatric CT examinations in Scotland: phase one; benchmarking current performance

To benchmark the dose from paediatric head and chest examinations on computed tomography (CT) scanners throughout Scotland, to identify scanners that may require optimisation and to provide optimisation advice based on the protocols from better performing scanners. Anthropomorphic phantoms corresponding to 1, 5 and 10 year olds were sent to 50 CT scanners around Scotland. Head and chest examinations were undertaken by local staff using local techniques on each scanner with each phantom, and details of the protocols used were recorded. Computed tomography dose index (CTDI)_voland dose length product (DLP) were recorded post-scan. There is a significant variation in performance throughout Scotland. For head examinations, the highest DLP is 13 times the lowest for an equivalent sized phantom. For chest examinations, the highest is 128 times the lowest for an equivalent sized phantom. The wide range of CT dose measurements indicates the potential for variation in image quality across Scotland. Feedback has been provided to all participating sites on their individual results compared to the national data set. Specific feedback was provided where relevant on potential considerations for optimisation. Scanners that may be undertaking paediatric CT head and chest examinations in a sub-optimal manner throughout Scotland have been identified along with those aspects of a scan protocol that are most likely to lead to sub-optimal performance.

Application of a deep learning image reconstruction (DLIR) algorithm in head CT imaging for children to improve image quality and lesion detection

BACKGROUND

To evaluate the performance of a Deep Learning Image Reconstruction (DLIR) algorithm in pediatric head CT for improving image quality and lesion detection with 0.625 mm thin-slice images.

METHODS

Low-dose axial head CT scans of 50 children with 120 kV, 0.8 s rotation and age-dependent 150-220 mA tube current were selected. Images were reconstructed at 5 mm and 0.625 mm slice thickness using Filtered back projection (FBP), Adaptive statistical iterative reconstruction-v at 50% strength (50%ASIR-V) (as reference standard), 100%ASIR-V and DLIR-high (DL-H). The CT attenuation and standard deviation values of the gray and white matters in the basal ganglia were measured. The clarity of sulci/cisterns, boundary between white and gray matters, and overall image quality was subjectively evaluated. The number of lesions in each reconstruction group was counted.

RESULTS

The 5 mm FBP, 50%ASIR-V, 100%ASIR-V and DL-H images had a subjective score of 2.25 ± 0.44, 3.05 ± 0.23, 2.87 ± 0.39 and 3.64 ± 0.49 in a 5-point scale, respectively with DL-H having the lowest image noise of white matter at 2.00 ± 0.34 HU; For the 0.625 mm images, only DL-H images met the diagnostic requirement. The 0.625 mm DL-H images had similar image noise (3.11 ± 0.58 HU) of the white matter and overall image quality score (3.04 ± 0.33) as the 5 mm 50% ASIR-V images (3.16 ± 0.60 HU and 3.05 ± 0.23). Sixty-five lesions were recognized in 5 mm 50%ASIR-V images and 69 were detected in 0.625 mm DL-H images.

CONCLUSION

DL-H improves the head CT image quality for children compared with ASIR-V images. The 0.625 mm DL-H images improve lesion detection and produce similar image noise as the 5 mm 50%ASIR-V images, indicating a potential 85% dose reduction if current image quality and slice thickness are desired.

Hydrocephalus: Ventricular Volume Quantification Using Three-Dimensional Brain CT Data and Semiautomatic Three-Dimensional Threshold-Based Segmentation Approach

OBJECTIVE

To evaluate the usefulness of the ventricular volume percentage quantified using three-dimensional (3D) brain computed tomography (CT) data for interpreting serial changes in hydrocephalus.

MATERIALS AND METHODS

Intracranial and ventricular volumes were quantified using the semiautomatic 3D threshold-based segmentation approach for 113 brain CT examinations (age at brain CT examination ≤ 18 years) in 38 patients with hydrocephalus. Changes in ventricular volume percentage were calculated using 75 serial brain CT pairs (time interval 173.6 ± 234.9 days) and compared with the conventional assessment of changes in hydrocephalus (increased, unchanged, or decreased). A cut-off value for the diagnosis of no change in hydrocephalus was calculated using receiver operating characteristic curve analysis. The reproducibility of the volumetric measurements was assessed using the intraclass correlation coefficient on a subset of 20 brain CT examinations.

RESULTS

Mean intracranial volume, ventricular volume, and ventricular volume percentage were 1284.6 ± 297.1 cm³, 249.0 ± 150.8 cm³, and 19.9 ± 12.8%, respectively. The volumetric measurements were highly reproducible (intraclass correlation coefficient = 1.0). Serial changes (0.8 ± 0.6%) in ventricular volume percentage in the unchanged group (n = 28) were significantly smaller than those in the increased and decreased groups (6.8 ± 4.3% and 5.6 ± 4.2%, respectively; p = 0.001 and p < 0.001, respectively; n = 11 and n = 36, respectively). The ventricular volume percentage was an excellent parameter for evaluating the degree of hydrocephalus (area under the receiver operating characteristic curve = 0.975; 95% confidence interval, 0.948-1.000; p < 0.001). With a cut-off value of 2.4%, the diagnosis of unchanged hydrocephalus could be made with 83.0% sensitivity and 100.0% specificity.

CONCLUSION

The ventricular volume percentage quantified using 3D brain CT data is useful for interpreting serial changes in hydrocephalus.

Deep learning-based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V)

PURPOSE

To compare the image quality of brain computed tomography (CT) images reconstructed with deep learning-based image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V).

METHODS

Sixty-two patients underwent routine noncontrast brain CT scans and datasets were reconstructed with 30% ASIR-V and DLIR with three selectable reconstruction strength levels (low, medium, high). Objective parameters including CT attenuation, noise, noise reduction rate, artifact index of the posterior cranial fossa, and contrast-to-noise ratio (CNR) were measured at the levels of the centrum semiovale and basal ganglia. Subjective parameters including gray matter-white matter differentiation, sharpness, and overall diagnostic quality were also assessed and compared with the interobserver agreement.

RESULTS

There was a gradual reduction in the image noise and artifact index of the posterior cranial fossa as the strength levels of DLIR increased (all P < 0.001) compared with that of ASIR-V. CNR in both the centrum semiovale and basal ganglia levels also improved from the low to high strength levels of DLIR compared with that of ASIR-V (all P < 0.001). DLIR images with medium and high strength levels demonstrated the best subjective image quality scores among the reconstruction datasets. There was moderate to good interobserver agreement for the subjective image quality assessments with ASIR-V and DLIR.

CONCLUSION

On routine brain CT scans, optimized protocols with DLIR allowed significant reduction of noise and artifacts with improved subjective image quality compared with ASIR-V.

Can QuickBrain MRI replace CT as first-line imaging for select pediatric head trauma?

OBJECTIVE

The current standard of care for initial neuroimaging in injured pediatric patients suspected of having traumatic brain injury is computed tomography (CT) that carries risks associated with radiation exposure. The primary objective of this trial was to evaluate the ability of a QuickBrain MRI (qbMRI) protocol to detect clinically important traumatic brain injuries in the emergency department (ED). The secondary objective of this trial was to compare qbMRI to CT in identifying radiographic traumatic brain injury.

METHODS

This was a prospective study of trauma patients less than 15 years of age with suspected traumatic brain injury at a level 1 pediatric trauma center in Portland, Oregon between August 2017 and March 2019. All patients in whom a head CT was deemed clinically necessary were approached for enrollment to also obtain a qbMRI in the acute setting. Clinically important traumatic brain injury was defined as the need for neurological surgery procedure, intubation, pediatric intensive care unit stay greater than 24 hours, a total hospital length of stay greater than 48 hours, or death.

RESULTS

A total of 73 patients underwent both CT and qbMRI. The median age was 4 years (interquartile range [IQR] = 1-10 years). Twenty-two patients (30%) of patients had a clinically important traumatic brain injury, and of those, there were 2 deaths (9.1%). QbMRI acquisition time had a median of 4 minutes and 52 seconds (IQR = 3 minutes 49 seconds-5 minutes 47 seconds). QbMRI had sensitivity for detecting clinically important traumatic brain injury of 95% (95% confidence interval [CI] = 77%-99%). For any radiographic injury, qbMRI had a sensitivity of 89% (95% CI = 78%-94%).

CONCLUSION

Our results suggest that qbMRI has good sensitivity to detect clinically important traumatic brain injuries. Further multi-institutional, prospective trials are warranted to either support or refute these findings.

Relation between age and CT radiation doses: Dose trends in 705 pediatric head CT

PURPOSE

To evaluate the relationship between patient age and radiation doses associated with routine pediatric head CT performed with automatic tube potential selection and tube current modulation techniques.

METHODS

We obtained patient demographics, scan parameters, and radiation dose descriptors (CT dose index volume -CTDIvol and dose length product -DLP) associated with consecutive routine head CT in 705 children (mean age 6.9 ± 5 years). Children were scanned on one of the three multidetector-row CTs (64-128 slices, Siemens) over 6 months period in a tertiary hospital. All head CT exams were performed in helical scan mode using automatic tube potential selection (Care kV) and automatic tube current modulation (Care Dose 4D) techniques. The information was obtained from a radiation dose monitoring software. Data were analyzed using linear correlation and analysis of variance.

RESULTS

Most age-wise median CTDIvol (9-27 mGy; 703/705 pediatric head CT, >99 %) from our institution were lower than the European Diagnostic Reference Levels (EDRL, CTDIvol 24-50 mGy) but median DLP (151-586 mGy cm) from 201/705 children (28 %) was higher than the EDRL (DLP 300-650 mGy cm). Unlike the age-stratified EDRL, a combination of automatic tube potential selection and tube current modulation for pediatric head results in a significant linear correlation between radiation doses and patient age (r2 = 0.66, p < 0.001).

CONCLUSIONS

Radiation doses for head CT change linearly with children's age. Despite lower CTDIvol and DLP for most children, longer scan length resulted in higher DLP for some pediatric head CT compared to the corresponding EDRL; this result underscores the need to promote clear guidelines for technologists operating CT.

Cerebral computed tomographic angiography using third-generation reconstruction algorithm provides improved image quality with lower contrast and radiation dose

PURPOSE

We hypothesized that cerebral CT angiogram performed using third-generation reconstruction algorithm and lower contrast dose-low-kVp technique (LD-CTA) will provide better image quality when compared with regular contrast dose CTA at 120 kVp using a sinogram-affirmed iterative reconstruction algorithm (ND-CTA).

METHODS

Retrospective imaging review of 100 consecutive patients (50 each in LD- and ND-CTA groups). Two readers independently assessed the subjective image quality across multiple vascular segments on a Likert-like scale. Differences in contrast dose, CT dose index (CTDI), and dose length product (DLP) were compared using Mann-Whitney U test. Fisher's exact test was used to compare subjective image quality. Similarly, contrast- and signal-to-noise ratios (CNR and SNR) were compared in the mid-M1 MCA vessels bilaterally and the mid-basilar artery using Mann-Whitney U test. Interclass correlation coefficient (ICC) was calculated for the SNR/CNR values.

RESULTS

Both observers showed excellent correlation in subjective image quality (mean percentage agreement of 95.2% for group 1 versus 89.2% for group 2). LD-CTA group showed better SNR and CNR (p < 0.0001) for both MCA vessels and the mid-basilar artery. Interclass correlation coefficient showed moderate correlation (0.51-0.63) between readers. LD-CTA group also used lower contrast (49 cc versus 97 cc in ND-CTA) and had lower radiation exposure (DLP/CTDI for both groups 268.3/80.7 vs 519.5/36.08, both < 0.0001).

CONCLUSION

Next-generation reconstruction algorithm and low-kV scanning significantly improved image quality on cerebral CTA images despite lower contrast dose and, in addition, have lower radiation exposure.