Pediatric Imaging Research Articles

Pediatric Head and Neck Imaging

Pediatric Head and Neck Imaging

Potential benefits of photon counting detector computed tomography in pediatric imaging.

Photon counting detector (PCD) CT represents the newest advance in CT technology, with improved radiation dose efficiency, increased spatial resolution, inherent spectral imaging capabilities, and the ability to eliminate electronic noise. Its design fundamentally differs from conventional energy integrating detector CT because photons are directly converted to electrical signal in a single step. Rather than converting X-rays to visible light and having an output signal that is a summation of energies, PCD directly counts each photon and records its individual energy information. The current commercially available PCD-CT utilizes a dual-source CT geometry, which allows 66 ms cardiac temporal resolution and high-pitch (up to 3.2) scanning. This can greatly benefit pediatric patients by facilitating high quality fast scanning to allow sedation-free imaging. The energy-resolving nature of the utilized PCDs allows "always-on" dual-energy imaging capabilities, such as the creation of virtual monoenergetic, virtual non-contrast, virtual non-calcium, and other material-specific images. These features may be combined with high-resolution imaging, made possible by the decreased size of individual detector elements and the absence of interelement septa. This work reviews the foundational concepts associated with PCD-CT and presents examples to highlight the benefits of PCD-CT in the pediatric population.

Tubular structures segmentation of pediatric abdominal-visceral ceCT images with renal tumors: Assessment, comparison and improvement

Renal tubular structures, such as ureters, arteries and veins, are very important for building a complete digital 3D anatomical model of a patient. However, they can be challenging to segment from ceCT images due to their elongated shape, diameter variation and intra- and inter-patient contrast heterogeneity. This task is even more difficult in pediatric and pathological subjects, due to high inter-subject anatomical variations, potential presence of tumors, small volume of these structures compared to the surrounding, and small available labeled datasets.Given the limited literature on methods dedicated to children, and in order to find inspirational approaches, a complete assessment of state-of-the-art methods for the segmentation of renal tubular structures on ceCT images on adults is presented. Then, these methods are tested and compared on a private pediatric and pathological dataset of 79 abdominal-visceral ceCT images with arteriovenous phase acquisitions. To the best of our knowledge, both assessment and comparison in this specific case are novel.Eventually, we also propose a new loss function which leverages for the first time the use of vesselness functions on the predicted segmentation. We show that the combination of this loss function with state-of-the-art methods improves the topological coherence of the segmented tubular structures.22The code is available at: https://github.com/Giammarco07/DeePRAC_project.

Joint Cranial Bone Labeling and Landmark Detection in Pediatric CT Images Using Context Encoding.

Image segmentation, labeling, and landmark detection are essential tasks for pediatric craniofacial evaluation. Although deep neural networks have been recently adopted to segment cranial bones and locate cranial landmarks from computed tomography (CT) or magnetic resonance (MR) images, they may be hard to train and provide suboptimal results in some applications. First, they seldom leverage global contextual information that can improve object detection performance. Second, most methods rely on multi-stage algorithm designs that are inefficient and prone to error accumulation. Third, existing methods often target simple segmentation tasks and have shown low reliability in more challenging scenarios such as multiple cranial bone labeling in highly variable pediatric datasets. In this paper, we present a novel end-to-end neural network architecture based on DenseNet that incorporates context regularization to jointly label cranial bone plates and detect cranial base landmarks from CT images. Specifically, we designed a context-encoding module that encodes global context information as landmark displacement vector maps and uses it to guide feature learning for both bone labeling and landmark identification. We evaluated our model on a highly diverse pediatric CT image dataset of 274 normative subjects and 239 patients with craniosynostosis (age 0.63 ± 0.54 years, range 0-2 years). Our experiments demonstrate improved performance compared to state-of-the-art approaches.

Update on Pediatric Spine Imaging.

The spine is often difficult to evaluate clinically in children, increasing the importance of diagnostic imaging to detect a wide variety of spinal disorders ranging from congenital abnormalities to severe infections. Clinical history and physical examination can help determine whether imaging is needed and which imaging technique would be best. The most common cause for back pain, even in children, is muscular strain/spasm that does not require any imaging. However, red flags such as pain at age < 5 years, constant pain, night pain, radicular pain, pain lasting > 4 weeks, or an abnormal neurologic examination may require further investigation. Imaging can be of great value for diagnosis but must be interpreted along with the clinical history, physical examination, and laboratory findings to achieve an accurate diagnosis. We discuss imaging for the most common and/or important spine pathologies in children: congenital and developmental pathologies, trauma, infectious processes, inflammatory causes, and tumors.

ACR Accreditation of a Novel Linac-Based kV-CBCT System on a High-Speed Ring-Gantry

An advanced kV-CBCT imaging system mounted on a high-speed ring-gantry Linac has recently been introduced, offering improved imaging hardware and reconstruction software to produce high quality CBCT images usable for treatment planning. Imaging performance of the system was assessed using the American College of Radiology (ACR) accreditation designed for diagnostic CT systems, additionally comparing its results to pre-existing diagnostic CT and Linac-based CBCT systems. All imaging protocols on the novel imaging system were scanned with the ACR head phantom and evaluated using ACR recommended testing and passing criteria. ACR image quality parameters include contrast-to-noise ratio (CNR), spatial resolution, HU accuracy, image scaling, and HU uniformity. CNR ≥1.0 for adult head and body, ≥0.7 for pediatric head, and ≥0.4 for pediatric body imaging protocols pass ACR criteria. Spatial resolution ≥6 line-pairs/cm for head and ≥5 line-pairs/cm for body protocols pass ACR criteria. HU accuracy passing criteria includes acrylic (110 to 135 HU), air (-1005 to -970 HU), bone (850 to 970 HU), polyethylene (-107 to -84 HU), and water (-7 to 7 HU). Image scaling measurements with errors ≤5% and HU uniformity maximum differences ≤5 HU pass ACR criteria. For machine cross-comparisons, adult Head and Pelvis imaging protocols were acquired on an existing diagnostic CT and 2 Linac-based CBCT systems (1 traditional C-arm and 1 ring-gantry) and analyzed with the same ACR methodology and passing criteria, with passing rates compared. On the novel imaging system, all standard patient size imaging protocols using 125 kVp (Head, H&N, Thorax, Thorax Slow, Breast, Abdomen, Pelvis, Pediatric Head, and Pediatric Abdomen) passed all ACR criteria; while 2 larger patient focused imaging protocols using 140 kVp (Abdomen Large and Pelvis Large) produced minor deviations on HU uniformity (maximum differences of 5 - 7 HU) but passed all other ACR criteria. The novel system matched passing rates of a diagnostic CT simulator, and outperformed pre-existing Linac based CBCT imaging systems. This newly developed advanced imaging system produces high quality images, meeting diagnostic CT ACR recommendations and far surpassing the CBCT image quality currently available on pre-existing Linacs.

Comparison of average Water Equivalent diameter values between CTContour and vendor-specific estimates in CT dosimetry

PurposeThis study aimed to compare the average Water Equivalent Diameter (WED) values obtained from CTContour, an open-source program for Size-Specific Dose Estimate (SSDE) and WED calculation, and vendor-specific values provided by Philips scanners. MethodsA random sample of 50 adult and 50 paediatric abdomen-pelvis protocol CT images from Philips scanners were chosen at our Hospital and analysed using CTContour, and extracting average WED values from Philips from the images DICOM headers. The average WED values from the two methods were compared via Bland-Altman analysis to assess their agreement and reliability. ResultsThe average WED values obtained from CTContour were found to be slightly lower than those obtained from the vendor-specific calculations, with mean disagreements of −5.62% and −2.88% for the adult and paediatric datasets, respectively, with both methods providing clinically acceptable estimations of average WED. There was no statistically significant correlation between body habitus and the level of disagreement between methods. ConclusionsThis study demonstrates that CTContour can provide average WED measurements comparable to the vendor-specific calculations for SSDE and WED in CT dosimetry. Differences between programs are likely due to inherent differences in the methods employed to estimate WED automatically. Further research is warranted to validate these results for additional CT protocols beyond abdomen-pelvis studies.

Brain structural covariance network features are robust markers of early heavy alcohol use.

Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies. Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years). Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected. Graph theory metrics of segregation and integration were used to summarize SCN. Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar. Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

Diagnostic Excellence in Pediatric Spine Imaging: Using Contextualized Imaging Protocols.

Contextual design and selection of MRI protocols is critical for making an accurate diagnosis given the wide variety of clinical indications for spine imaging in children. Here, we describe our pediatric spine imaging protocols in detail, tailored to specific clinical questions.

AUTOMATIC DETECTION OF FACIAL LOCATIONS TO MEASURE FACIAL ASYMMETRY AFTER PAEDIATRIC RADIOTHERAPY

Abstract AIMS Children receiving radiotherapy for head and neck tumours, such as Head and Neck Rhabdomyosarcoma (HN- RMS), often experience facial asymmetry later in life. Here, we present a convolutional neural network (CNN) that will enable the automatic detection of facial anatomical landmarks on MR images taken during follow-up examinations. This model will facilitate quantitative tracking of facial asymmetry, thus revealing the centres of growth that are most affected by radiation, with the ultimate goal of defining precise dose tolerance levels. METHOD A dataset of 61 paediatric MRI images was used to train separate models that infer the locations of four facial landmarks. These locations had previously been manually labelled by two independent observers. Before training, the images were normalised and registered to optimise model performance. The model was judged to be of acceptable accuracy if the distances between the observed landmark (gold-standard location) and the location predicted by the trained models were smaller than the inter-observer distances of each facial landmark. RESULTS The model predicted landmarks which were closer to the gold-standard location than the inter-observer locations. In detail: • Optic chiasm: 0.81 ± 0.45 mm (inter-observer 1.49 mm). • Nasion: 0.70 ± 0.62 mm (inter-observer 3.24 mm). • Left Ectoconchion: 1.22 ± 0.44 mm (inter-observer 3.58 mm). • Right Ectoconchion: 0.80 ± 0.82 mm (inter-observer 4.04 mm). CONCLUSIONS This research shows promising potential for a CNN to accurately locate head and neck landmarks in paediatric MRI images. This tool can label craniofacial landmarks in large clinical datasets quicker and more accurately than can be achieved through manual techniques.

Review of standard paediatric neuroradiology MRI protocols from 12 UK tertiary paediatric hospitals: is there much variation between centres?

To investigate how magnetic resonance imaging (MRI) examinations are protocolled in tertiary paediatric neuroradiology centres around the UK for some of the more common presentations encountered in paediatric neuroradiology, and to identify any variations of note. All 19 UK tertiary paediatric neuroradiology centres registered with the British Society of Neuroradiologists-Paediatric Group were contacted and asked if they could provide a copy of their standard MRI protocols. Twelve responded (63%) and 10 of the more common presentations were selected and the standard acquired sequences obtained at each participating centre were compared. Where available the collated protocols were also compared against current published guidance. The basic sequences carried out by centres around the UK are similar; however, there are lots of variations overall. The only standardised protocol currently being implemented nationally in paediatric imaging is that for brain tumours. Otherwise, chosen protocols are generally dependent on the preferences and technical capabilities of individual centres. Suggested published protocols also exist for non-accidental injury (NAI), multiple sclerosis, epilepsy, and head and neck imaging. The differences in MRI protocolling depend in part on technical capabilities and in part on the experience and preferences of the paediatric neuroradiologists at each centre. For most presentations, there is no consensus as to what constitutes the perfect protocol. The present results will be useful for specialist centres who may wish to review their current protocols, and for more generalist centres to use as a reference to guide their MRI protocolling.

The Development of Human Cortical Scene Processing

Decades of research have uncovered the neural basis of place (or “scene”) processing in adulthood, revealing a set of three regions that respond selectively to visual scene information, each hypothesized to support distinct functions within scene processing (e.g., recognizing a particular kind of place versus navigating through it). Despite this considerable progress, surprisingly little is known about how these cortical regions develop. Here we review the limited evidence to date, highlighting the first few studies exploring the origins of cortical scene processing in infancy and the several studies addressing when the scene regions reach full maturity, unfortunately with inconsistent findings. This inconsistency likely stems from common pitfalls in pediatric functional magnetic resonance imaging, and accordingly, we discuss how these pitfalls may be avoided. Furthermore, we point out that almost all studies to date have focused only on general scene selectivity and argue that greater insight could be gleaned by instead exploring the more distinct functions of each region as well as their connectivity. Finally, with this last point in mind, we offer a novel hypothesis that scene regions supporting navigation (including the occipital place area and retrosplenial complex) mature later than those supporting scene categorization (including the parahippocampal place area).

Testing the performance, adequacy, and applicability of an artificial intelligence model for pediatric pneumonia diagnosis

BackgroundCommunity-acquired Pneumonia (CAP) is a common childhood infectious disease. Deep learning models show promise in X-ray interpretation and diagnosis, but their validation should be extended due to limitations in the current validation workflow. To extend the standard validation workflow we propose doing a pilot test with the next characteristics. First, the assumption of perfect ground truth (100% sensitive and specific) is unrealistic, as high intra and inter-observer variability have been reported. To address this, we propose using Bayesian latent class models (BLCA) to estimate accuracy during the pilot. Additionally, assessing only the performance of a model without considering its applicability and acceptance by physicians is insufficient if we hope to integrate AI systems into day-to-day clinical practice. Therefore, we propose employing explainable artificial intelligence (XAI) methods during the pilot test to involve physicians and evaluate how well a Deep Learning model is accepted and how helpful it is for routine decisions as well as analyze its limitations by assessing the etiology. This study aims to apply the proposed pilot to test a deep Convolutional Neural Network (CNN)-based model for identifying consolidation in pediatric chest-X-ray (CXR) images already validated using the standard workflow. MethodsFor the standard validation workflow, a total of 5856 public CXRs and 950 private CXRs were used to train and validate the performance of the CNN model. The performance of the model was estimated assuming a perfect ground truth. For the pilot test proposed in this article, a total of 190 pediatric chest-X-ray (CXRs) images were used to test the CNN model support decision tool (SDT). The performance of the model on the pilot test was estimated using extensions of the two-test Bayesian Latent-Class model (BLCA). The sensitivity, specificity, and accuracy of the model were also assessed. The clinical characteristics of the patients were compared according to the model performance. The adequacy and applicability of the SDT was tested using XAI techniques. The adequacy of the SDT was assessed by asking two senior physicians the agreement rate with the SDT. The applicability was tested by asking three medical residents before and after using the SDT and the agreement between experts was calculated using the kappa index. ResultsThe CRXs of the pilot test were labeled by the panel of experts into consolidation (124/176, 70.4%) and no-consolidation/other infiltrates (52/176, 29.5%). A total of 31/176 (17.6%) discrepancies were found between the model and the panel of experts with a kappa index of 0.6. The sensitivity and specificity reached a median of 90.9 (95% Credible Interval (CrI), 81.2–99.9) and 77.7 (95% CrI, 63.3–98.1), respectively. The senior physicians reported a high agreement rate (70%) with the system in identifying logical consolidation patterns. The three medical residents reached a higher agreement using SDT than alone with experts (0.66±0.1 vs. 0.75±0.2). ConclusionsThrough the pilot test, we have successfully verified that the deep learning model was underestimated when a perfect ground truth was considered. Furthermore, by conducting adequacy and applicability tests, we can ensure that the model is able to identify logical patterns within the CXRs and that augmenting clinicians with automated preliminary read assistants could accelerate their workflows and enhance accuracy in identifying consolidation in pediatric CXR images.

Quantitative diffusion-weighted MRI response assessment in rhabdomyosarcoma: an international retrospective study on behalf of the European paediatric Soft tissue sarcoma Study Group Imaging Committee

ObjectiveTo investigate the feasibility of diffusion-weighted magnetic resonance imaging (DW-MRI) as a predictive imaging marker after neoadjuvant chemotherapy in patients with rhabdomyosarcoma.Material and methodsWe performed a multicenter retrospective study including pediatric, adolescent and young adult patients with rhabdomyosarcoma, Intergroup Rhabdomyosarcoma Study group III/IV, treated according to the European paediatric Soft tissue sarcoma Study Group (EpSSG) RMS2005 or MTS2008 studies. DW-MRI was performed according to institutional protocols. We performed two-dimensional single-slice tumor delineation. Areas of necrosis or hemorrhage were delineated to be excluded in the primary analysis. Mean, median and 5th and 95th apparent diffusion coefficient (ADC) were extracted.ResultsOf 134 included patients, 82 had measurable tumor at diagnosis and response and DW-MRI scans of adequate quality and were included in the analysis. Technical heterogeneity in scan acquisition protocols and scanners was observed. Mean ADC at diagnosis was 1.1 (95% confidence interval [CI]: 1.1–1.2) (all ADC expressed in * 10−3 mm2/s), versus 1.6 (1.5–1.6) at response assessment. The 5th percentile ADC was 0.8 (0.7–0.9) at diagnosis and 1.1 (1.0–1.2) at response. Absolute change in mean ADC after neoadjuvant chemotherapy was 0.4 (0.3–0.5). Exploratory analyses for association between ADC and clinical parameters showed a significant difference in mean ADC at diagnosis for alveolar versus embryonal histology. Landmark analysis at nine weeks after the date of diagnosis showed no significant association (hazard ratio 1.3 [0.6–3.2]) between the mean ADC change and event-free survival.ConclusionA significant change in the 5th percentile and the mean ADC after chemotherapy was observed. Strong heterogeneity was identified in DW-MRI acquisition protocols between centers and in individual patients.Graphical

Evaluation of Artifact Appearance and Burden in Pediatric Brain Tumor MR Imaging with Compressed Sensing in Comparison to Conventional Parallel Imaging Acceleration.

Clinical magnetic resonance imaging (MRI) aims for the highest possible image quality, while balancing the need for acceptable examination time, reasonable signal-to-noise ratio (SNR), and lowest artifact burden. With a recently introduced imaging acceleration technique, compressed sensing, the acquisition speed and image quality of pediatric brain tumor exams can be improved. However, little attention has been paid to its impact on method-related artifacts in pediatric brain MRI. This study assessed the overall artifact burden and artifact appearances in a standardized pediatric brain tumor MRI by comparing conventional parallel imaging acceleration with compressed sensing. This showed that compressed sensing resulted in fewer physiological artifacts in the FLAIR sequence, and a reduction in technical artifacts in the 3D T1 TFE sequences. Only a slight difference was noted in the T2 TSE sequence. A relatively new range of artifacts, which are likely technique-related, was noted in the 3D T1 TFE sequences. In conclusion, by equipping a basic pediatric brain tumor protocol for 3T MRI with compressed sensing, the overall burden of common artifacts can be reduced. However, attention should be paid to novel compressed-sensing-specific artifacts.

"Seeing is believing": myth or maxim? Mimics of pathology on paediatric chest imaging studies.

Appreciating the importance of correct interpretation of radiological studies, the need for space for further education and experience, and the recognition that interpretative errors often occur, we present below a case series highlighting situations where all may not be as it first appears.

Pediatric imaging of neuroblastoma-From classic to atypical

Neuroblastoma (NB) is the most common extracranial solid tumour in children, accounting for approximately eight percent of paediatric cancer and fifteen percent of pediatric cancer deaths. Reaching a correct and timely diagnosis is extremely important, especially in aggressive NB. Prognosis and treatment are based on tumour biology and genetics, patient age and perhaps most importantly, disease stage. The current staging system relies heavily on imaging, and this pictorial essay provides a description of the radiological features of NB, from classic to atypical, and the modalities used to help confirm the diagnosis and staging of NB. Our aim is to provide radiologists with a comprehensive collection of presentations of NB, to help improve diagnostic accuracy and decrease time to diagnose.

Myocardial Functional Imaging in Pediatric Nuclear Cardiology.

The role of nuclear medicine in pediatric cardiology has grown rapidly over the years, providing useful functional and prognostic information and playing a complementary role to morphological imaging in the evaluation of myocardial perfusion, cardiovascular inflammation and infections, and cardiac sympathetic innervation. The aim of this narrative review is to summarize and highlight the most important evidence on pediatric nuclear cardiology, describing clinical applications and the possibilities, advantages, and limitations of nuclear medicine techniques. Moreover, a special focus will be given to the minimization of radiation exposure in pediatric nuclear cardiology imaging, a critical topic in children.

Global pediatric radiology education: responding to training-level specific needs.

Pediatric radiology education for pediatric and radiology trainees and practicing professionals must be adapted to the target audience. In efforts to narrow the gaps in global pediatric radiology education, the Children's Hospital of Philadelphia's radiology department's Global Outreach and Education Program has designed specific interventions and curricula according to different levels of training and desired expertise. Our initiatives include an online "Introduction to Pediatric Imaging" lecture series for pediatrics residents in Botswana, Ethiopia and Vietnam; access to a learning management system (Outreach RADIAL) for radiology residents; case-based review sessions for pediatric radiology fellows; and in-person seminars for professionals from Eastern Europe and Africa. Here, we highlight our global education efforts to encourage other departments to take a similar systematic approach to outreach activities.

Dual-Energy CT for Pediatric Thoracic Imaging: A Review.

Dual-energy CT has expanded the potential of thoracic imaging in both children and adults. Data processing allows material- and energy-specific reconstructions, which improve material differentiation and tissue characterization compared with single-energy CT. Material-specific reconstructions include iodine, virtual unenhanced, perfusion blood volume, and lung vessel images, which can improve assessment of vascular, mediastinal, and parenchymal abnormalities. The energy-specific reconstruction algorithm allows virtual monoenergetic reconstructions, including low-energy images to increase iodine conspicuity and high-energy images to reduce beam-hardening and metal artifacts. This review highlights dual-energy CT principles, hardware, and postprocessing algorithms; the clinical applications of dual-energy CT; and the potential benefits of photon counting (the most recently introduced iteration of spectral imaging) in pediatric thoracic imaging.