Detectability of Head and Neck Cancer via New Computed Tomography Reconstruction Tools including Iterative Reconstruction and Metal Artifact Reduction

Investigation of radiology professionals' awareness of CT head artifacts

OBJECTIVE

Computerized tomography (CT) artifacts can happen for various causes. It is critical to understand these artifacts because they can mimic disease or reduce image quality to non-diagnostic levels. CT artifacts can be characterized according to their underlying cause. This study aims to evaluate and compare the understanding of CT head artifacts between radiographers and radiography interns, supplemented by insights from a select group of participating radiologists.

RESULTS

A cross-sectional survey study included 150 participants. All participants' average knowledge score of CT head artifact was good (77.81%). The most correctly identified CT head artifacts were the metal artifact (86%), ring artifact (84.7%), and motion artifact (81.3%). The beam hardening artifact was correctly identified less frequently (62%). There is significant difference in the recognition of motion artifacts among the participants (P = 0.001) knowledge of CT head image artifacts improved significantly with more experienced participants (P = 0.001), where participants with less than 10 months of experience had a higher rate of incorrect responses (85 incorrect vs. 31 correct). The recognition of these artifacts improves with experience and advanced age. Understanding these artifacts is essential to avoid misdiagnosis of various diseases.

Design and Validation of a Multimodal Diffuse Reflectance and Spatially Offset Raman Spectroscopy System for In Vivo Applications

We report on the development of a multimodal spectroscopy system, combining diffuse reflectance spectroscopy (DRS) and spatially offset Raman spectroscopy (SORS). A fiber optic probe was designed with spatially offset source-detector fibers to collect subsurface measurements for each modality, as well as ball lens-coupled fibers for superficial measurements. The system acquires DRS, zero-offset Raman spectroscopy (RS) and SORS with good signal-to-noise ratio. Measurements on chicken breast tissue demonstrate that both DRS and RS can acquire spectra from similar depths within tissue. Measurements acquired from the skin of a human volunteer demonstrate distinct Raman peaks at 937 and 1755 cm-1 that were unique to the zero-offset ball lens configuration and 718 and 1089 cm-1 for the spatially offset setting. We also identified Raman peaks corresponding to melanin that were prominent in the superficial measurements obtained with the ball lens-coupled fibers but not in the spatially offset fibers.

Impact of Combined Deep Learning Image Reconstruction and Metal Artifact Reduction Algorithm on CT Image Quality in Different Scanning Conditions for Maxillofacial Region with Metal Implants: A Phantom Study

This study aims to investigate the impact of combining deep learning image reconstruction (DLIR) and metal artifacts reduction (MAR) algorithms on the quality of CT images with metal implants under different scanning conditions. Four images of the maxillofacial region in pigs were taken using different metal implants for evaluation. The scans were conducted at three different dose levels (CTDIvol: 20/10/5 mGy). The images were reconstructed using three different methods: filtered back projection (FBP), adaptive statistical iterative reconstruction with Veo at a 50% level (AV50), and DLIR at three levels (low, medium, and high). Regions of interest (ROIs) were identified in various tissues (near/far/reference fat, muscle, bone) both with and without metal implants and artifacts. Parameters such as standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and metal artifact index (MAI) were calculated. Additionally, two experienced radiologists evaluated the subjective image quality (IQ) using a 5-point Likert scale. (1) Both observers rated MAR generated significantly lower artifact scores than non-MAR in all types of tissues (P < 0.01), except for the far shadow and bloom in bone (phantoms 1, 3, 4) and the far bloom in muscle (phantom 3) without significant differences (P = 1.0). (2) Under the same scanning condition, DLIR at three levels produced a smaller SD than those of FBP and AV50 (P < 0.05). (3) Compared to FBP and AV50, DLIR denoted a better reduction of MAI and improvement of SNR and CNR (P < 0.05) for most tissues between the four phantoms. (4) Subjective overall IQ was superior with the increasement of DLIR level (P < 0.05) and both observers agreed that DLIR produced better artifact reductions compared with FBP and AV50. The combination of DLIR and MAR algorithms can enhance image quality, significantly reduce metal artifacts, and offer high clinical value.

Iterative Metal Artifact Reduction in Head and Neck CT Facilitates Tumor Visualization of Oral and Oropharyngeal Cancer Obscured by Artifacts From Dental Hardware

RATIONALE AND OBJECTIVES

The purpose of this study was to evaluate the diagnostic utility of iterative metal artifact reduction (iMAR) in computed tomography (CT)-imaging of oral and oropharyngeal cancers when obscured by dental hardware artifacts and to determine the most appropriate iMAR settings for this purpose.

MATERIALS AND METHODS

The study retrospectively enrolled 27 patients (8 female, 19 male; mean age 64±12.7years) with histologically confirmed oral or oropharyngeal cancer obscured by dental artifacts in contrast-enhanced CT. Raw CT data were reconstructed with ascending iMAR strengths (levels 1/2/3/4/5) and one reconstruction without iMAR (level 0). For subjective analysis, two blinded radiologists rated tumor visualization and artifact severity on a five-point Likert scale. For objective analysis, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artifact index (AI) were determined.

RESULTS

iMAR reconstructions improved the subjective image quality of tumor edge and contrast, and the objective parameters of tumor SNR and CNR, reaching their optimum at iMAR levels 4 and 5 (P<.001). AI decreased with iMAR reconstructions reaching its minimum at iMAR level 5 (P<.001). Tumor detection rates increased 2.4-fold with iMAR 5, 2.1-fold with iMAR 4, and 1.9-fold with iMAR 3 compared to reconstructions without iMAR. Disadvantages such as algorithm-induced artifacts increased significantly with higher iMAR strengths (P<.05), reaching a maximum with iMAR 5.

CONCLUSION

iMAR significantly improves CT imaging of oral and oropharyngeal cancers, as confirmed by both subjective and objective measures, with best results at highest iMAR strengths.

Ultra-High-Resolution CT of the Head and Neck with Deep Learning Reconstruction-Assessment of Image Quality and Radiation Exposure and Intraindividual Comparison with Normal-Resolution CT

OBJECTIVES

To assess the benefits of ultra-high-resolution CT (UHR-CT) with deep learning-based image reconstruction engine (AiCE) regarding image quality and radiation dose and intraindividually compare it to normal-resolution CT (NR-CT).

METHODS

Forty consecutive patients with head and neck UHR-CT with AiCE for diagnosed head and neck malignancies and available prior NR-CT of a different scanner were retrospectively evaluated. Two readers evaluated subjective image quality using a 5-point Likert scale regarding image noise, image sharpness, artifacts, diagnostic acceptability, and assessability of various anatomic regions. For reproducibility, inter-reader agreement was analyzed. Furthermore, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and slope of the gray-value transition between different tissues were calculated. Radiation dose was evaluated by comparing CTDIvol, DLP, and mean effective dose values.

RESULTS

UHR-CT with AiCE reconstruction led to significant improvement in subjective (image noise and diagnostic acceptability: p < 0.000; ICC ≥ 0.91) and objective image quality (SNR: p < 0.000; CNR: p < 0.025) at significantly lower radiation doses (NR-CT 2.03 ± 0.14 mSv; UHR-CT 1.45 ± 0.11 mSv; p < 0.0001) compared to NR-CT.

CONCLUSIONS

Compared to NR-CT, UHR-CT combined with AiCE provides superior image quality at a markedly lower radiation dose. With improved soft tissue assessment and potentially improved tumor detection, UHR-CT may add further value to the role of CT in the assessment of head and neck pathologies.

Patient-specific miniplates versus patient-specific reconstruction plate: A biomechanical comparison with 3D-printed plates in mandibular reconstruction

BACKGROUND

Patient-specific 3D-printed miniplates for free flap fixation in mandibular reconstruction were recently associated with enhanced osseous union. Higher mechanical strains resulting from these plates are discussed as reasons, but biomechanical studies are missing. This study aims to examine, whether patient-specific 3D-printed miniplates provide an increased interosteotomy movement (IOM) and lower stiffness compared with reconstruction plates.

METHODS

Polyurethane (PU) mandible and fibula models (Synbone AG, Malans, Schweiz) were used to simulate mandibular reconstruction with a one segment fibula flap equivalent. Osteosynthesis was performed using either four patient-specific 3D-printed miniplates (3D-Mini) or one patient-specific 3D-printed reconstruction plate (3D-Recon). Mastication was simulated using cyclic dynamic loading with increasing loads until material failure or a maximum load of 1000 N. Continuous IOM recording was carried out using a 3D optical tracking system (ARAMIS, Carl Zeiss GOM Metrology, Braunschweig, Germany).

FINDINGS

The averaged stiffness at a load of 100-300 N load did not differ between the groups (p = 0.296). There was a faster 1.0 mm vertical displacement in the 3D-Mini group (26 376 ± 14 190 cycles versus 44 817 ± 30 430 cycles, p = 0.018). The IOM were higher with miniplate fixation in the distal gap (p = 0.040). In the mesial gap, there was no significant difference between the groups (p = 0.160).

INTERPRETATION

Fixation with patient-specific 3D-printed miniplates results in higher mechanical strains. Lower rates of pseudarthrosis, as seen in clinical studies, might be caused by this phenomenon. Surgeons should evaluate the primary use of 3D-printed miniplates in mandibular reconstruction due to advantages of intraoral plate removal alongside safe osteosynthesis.

Advanced imaging of head and neck infections

When head and neck infection is suspected, appropriate imaging contributes to treatment decisions and prognosis. While contrast-enhanced CT is the standard imaging modality for evaluating head and neck infections, MRI can better characterize the skull base, intracranial involvement, and osteomyelitis, implying that these are complementary techniques for a comprehensive assessment. Both CT and MRI are useful in the evaluation of abscesses and thrombophlebitis, while MRI is especially useful in the evaluation of intracranial inflammatory spread/abscess formation, differentiation of abscess from other conditions, evaluation of the presence and activity of inflammation and osteomyelitis, evaluation of mastoid extension in middle ear cholesteatoma, and evaluation of facial neuritis and labyrinthitis. Apparent diffusion coefficient derived from diffusion-weighted imaging is useful for differential diagnosis and treatment response of head and neck infections in various anatomical sites. Dynamic contrast-enhanced MRI perfusion may be useful in assessing the activity of skull base osteomyelitis. MR bone imaging may be of additional value in evaluating bony structures of the skull base and jaw. Dual-energy CT is helpful in reducing metal artifacts, evaluating deep neck abscess, and detecting salivary stones. Subtraction CT techniques are used to detect progressive bone-destructive changes and to reduce dental amalgam artifacts. This article provides a region-based approach to the imaging evaluation of head and neck infections, using both conventional and advanced imaging techniques.

Iterative Reconstruction: State-of-the-Art and Future Perspectives

ABSTRACT

Image reconstruction processing in computed tomography (CT) has evolved tremendously since its creation, succeeding at optimizing radiation dose while maintaining adequate image quality. Computed tomography vendors have developed and implemented various technical advances, such as automatic noise reduction filters, automatic exposure control, and refined imaging reconstruction algorithms.Focusing on imaging reconstruction, filtered back-projection has represented the standard reconstruction algorithm for over 3 decades, obtaining adequate image quality at standard radiation dose exposures. To overcome filtered back-projection reconstruction flaws in low-dose CT data sets, advanced iterative reconstruction algorithms consisting of either backward projection or both backward and forward projections have been developed, with the goal to enable low-dose CT acquisitions with high image quality. Iterative reconstruction techniques play a key role in routine workflow implementation (eg, screening protocols, vascular and pediatric applications), in quantitative CT imaging applications, and in dose exposure limitation in oncologic patients.Therefore, this review aims to provide an overview of the technical principles and the main clinical application of iterative reconstruction algorithms, focusing on the strengths and weaknesses, in addition to integrating future perspectives in the new era of artificial intelligence.

Clinical Evaluation of an Innovative Metal-Artifact-Reduction Algorithm in FD-CT Angiography in Cerebral Aneurysms Treated by Endovascular Coiling or Surgical Clipping

Treated cerebral aneurysms (IA) require follow-up imaging to ensure occlusion. Metal artifacts complicate radiologic assessment. Our aim was to evaluate an innovative metal-artifact-reduction (iMAR) algorithm for flat-detector computed tomography angiography (FD-CTA) regarding image quality (IQ) and detection of aneurysm residua/reperfusion in comparison to 2D digital subtraction angiography (DSA). Patients with IAs treated by endovascular coiling or clipping underwent both FD-CTA and DSA. FD-CTA datasets were postprocessed with/without iMAR algorithm (MAR+/MAR−). Evaluation of all FD-CTA and DSA datasets regarding qualitative (IQ, MAR) and quantitative (coil package diameter/CPD) parameters was performed. Aneurysm occlusion was assessed for each dataset and compared to DSA findings. In total, 40 IAs were analyzed (ncoiling = 24; nclipping = 16). All iMAR+ datasets demonstrated significantly better IQ (pIQ coiling < 0.0001; pIQ clipping < 0.0001). iMAR significantly reduced the metal-artifact burden but did not affect the CPD. iMAR significantly improved the detection of aneurysm residua/reperfusion with excellent agreement with DSA (naneurysm detection MAR+/MAR−/DSA = 22/1/26). The iMAR algorithm significantly improves IQ by effective reduction of metal artifacts in FD-CTA datasets. The proposed algorithm enables reliable detection of aneurysm residua/reperfusion with good agreement to DSA. Thus, iMAR can help to reduce the need for invasive follow-up in treated IAs.