Effect of Ultra High-Resolution Computed Tomography and Model-Based Iterative Reconstruction on Detectability of Simulated Submillimeter Artery

High-resolution 0.25 mm Detector CT Has Limited Impact on Right Adrenal Vein Detectability in Preprocedural Contrast Enhanced CT for Adrenal Venous Sampling

OBJECTIVE

This study aims to identify factors associated with the detectability of the right adrenal vein (RAV) on preoperative contrast-enhanced CT scans of adrenal venous sampling (AVS) in the era of high-resolution CT (HRCT).

MATERIALS AND METHODS

In this retrospective study, 36 patients (15 men and 21 women; mean age, 56 y) who underwent preoperative contrast-enhanced CT [11 patients in HRCT with 0.25 mm detector matrix (Cannon Medical Systems) and 25 patients in conventional multidetector CT with 0.5 mm matrix] were included. A contrast agent dose of 600 mgI/kg was injected, and CT images were acquired at a fixed scan delay of 50 and 80 seconds. Adrenal venography and venous sampling were performed for the diagnosis of suspected primary hyperaldosteronism. The qualitative detectability of RAV on preoperative CT was assessed with adrenal venography as a reference. Clinical and imaging factors associated with a good detectability of RAV were analyzed via regression analysis. Optimal acquisition timing was assessed by analyzing the time-intensity curve and contrast enhancement pattern of the inferior vena cava using CT data from a separate cohort (n=5).

RESULTS

The qualitative detectability of RAV was deemed good in 15 patients and poor in 21 patients. Regression analysis revealed that only heterogeneous enhancement of inferior vena cava with bolus high attenuation, corresponding to an optimal acquisition timing from time-intensity curve analysis, was associated with a good detectability of RAV (odds ratio, 5.06). The use of HRCT was not statistically significant.

CONCLUSIONS

Optimal acquisition timing is a crucial factor for the detectability of RAV in preprocedural CT for AVS, while high-resolution 0.25 detector CT appears to have limited significance.

Superselective embolic particle guidance in vessel networks via shape-adaptive acoustic manipulation

Interventional embolization has been widely used as a clinical cancer therapy, which deactivates the tumors by occluding their blood supply vessels. However, conventional methods lack active control over the embolic particles, thus having a limited selectivity of millimeter-scale vessels and the issue of missing embolization. Here, we propose an ultrasound-based method for embolic particle control in submillimeter vessels. The biocompatible ultrasound generated from an extrasomatic source can transmit through biological tissues, and exert forces on the intravital embolic particles. We show that the particles, influenced by these forces, are steerable to the target branch at vascular bifurcations. By modulating the ultrasound to adapt the vascular bifurcation distribution, the particles flowing in the micro-vessel networks are steered to the target branch and embolize it. The acoustic steering within ex vivo and in vivo models both verify the potential of this non-invasive particle control for precise and safe interventional therapy.

Image Follow-Up After Flow Diverter Treatment Using Only Ultra-High Resolution CT Angiography with Model-Based Iterative Reconstruction: A Case Report

Follow-up examinations using magnetic resonance imaging or digital subtraction angiography are mandatory after flow diverter treatment of cerebral aneurysms. However, flow diverter features metal artifacts on magnetic resonance imaging and ischemic complications with digital subtraction angiography. Ultra-high-resolution computed tomography systems have recently become available in clinical practice. The combined use of ultra-high-resolution computed tomography and a reconstruction technique called model-based iterative reconstruction is expected to replace follow-up magnetic resonance imaging and digital subtraction angiography of flow diverter placement. Here, we report a case of ultra-high-resolution computed tomography with model-based iterative reconstruction after flow diverter treatment.

Utility of follow-up ultra-high-resolution CT angiography with model-based iterative reconstruction after flow diverter treatment for cerebral aneurysms

PURPOSE

Follow-up examinations after flow diverter (FD) treatment for cerebral aneurysms typically involve magnetic resonance imaging (MRI) or digital subtraction angiography (DSA). However, MRI is prone to vascular defects due to metal artifacts from FD, and DSA carries a risk of ischemic complications. In the context of computed tomography angiography (CTA), this study compares the efficacy of ultra-high-resolution CT (UHRCT) and novel reconstruction techniques, such as model-based iterative reconstruction (MBIR), against conventional methods such as filtered back projection (FBP) and hybrid iterative reconstruction (IR), to determine if they are a viable alternative to DSA in clinical settings.

MATERIALS AND METHODS

A phantom study was conducted with the full-width half-maximum considered as the FD thickness. This study compared three reconstruction methods: MBIR, FBP, and hybrid IR. A clinical study was also conducted with 21 patients who underwent follow-up CTA after FD treatment. The FD's visibility was assessed using a 4-point scale in FBP, hybrid IR, and MBIR compared to cone-beam CT (CBCT) with angiographic systems.

RESULTS

In the phantom study, FBP, hybrid IR, and MBIR visualized thinner FD thicknesses and improved detail rendering in that order. MBIR proved to be significantly superior in both the phantom and clinical study.

CONCLUSION

UHRCT with MBIR is highly effective for follow-up evaluations after FD treatment and may become the first-choice modality in the future.

Radiology's Ionising Radiation Paradox: Weighing the Indispensable Against the Detrimental in Medical Imaging

Ionising radiation stands as an indispensable protagonist in the narrative of medical imaging, underpinning diagnostic evaluations and therapeutic interventions across an array of medical conditions. However, this protagonist poses a paradox - its inestimable service to medicine coexists with an undercurrent of potential health risks, primarily DNA damage and subsequent oncogenesis. The narrative of this comprehensive review unfurls around this intricate enigma, delicately balancing the indispensable diagnostic utility against the non-negotiable commitment to patient safety. In this critical discourse, the intricacies of ionising radiation are dissected, illuminating not only its sources but also the associated biological and health hazards. The exploration delves into the labyrinth of strategies currently deployed to minimise exposure and safeguard patients. By casting light on the scientific nuances of X-rays, computed tomography (CT), and nuclear medicine, it traverses the complex terrain of radiation use in radiology, to promote safer medical imaging practices, and to facilitate an ongoing dialogue about diagnostic necessity and risk. Through a rigorous examination, the pivotal relationship between radiation dose and dose response is elucidated, unravelling the mechanisms of radiation injury and distinguishing between deterministic and stochastic effects. Moreover, protection strategies are illuminated, demystifying concepts such as justification, optimisation, the As Low As Reasonably Achievable (ALARA) principle, dose and diagnostic reference levels, along with administrative and regulatory approaches. With an eye on the horizon, promising avenues of future research are discussed. These encompass low-radiation imaging techniques, long-term risk assessment in large patient cohorts, and the transformative potential of artificial intelligence in dose optimisation. This exploration of the nuanced complexities of radiation use in radiology aims to foster a collaborative impetus towards safer medical imaging practices. It underscores the need for an ongoing dialogue around diagnostic necessity and risk, thereby advocating for a continual reassessment in the narrative of medical imaging.

Novel Intraoperative Navigation Using Ultra-High-Resolution CT in Robot-Assisted Partial Nephrectomy

Simple Summary

Successful surgery in robot-assisted partial nephrectomy (RAPN), especially for highly complex tumors, relies on a detailed understanding of the anatomical relations of the tumor absolute and relative to the urinary tract and the vascular structures, including the renal pedicle. Intraoperative navigation with accurate information regarding tumor position relative to the surrounding urinary vascular structures undoubtedly assists the surgeon during RAPN. In this report, we performed RAPN with intraoperative navigation using a novel computed tomography scanner (UHR-CT) and compared its perioperative and short-term functional outcomes to those of area-detector CT (ADCT). We found that this novel navigation system using UHR-CT provided a shorter warm ischemia time and lower estimated blood loss than ADCT, and concluded this could be a useful tool for patients who undergo RAPN. This is the first report to evaluate the feasibility and usefulness of UHR-CT for intraoperative navigation during RAPN.

Abstract

To assess the perioperative and short-term functional outcomes of robot-assisted partial nephrectomy (RAPN) with intraoperative navigation using an ultra-high-resolution computed tomography (UHR-CT) scanner, we retrospectively analyzed 323 patients who underwent RAPN using an UHR-CT or area-detector CT (ADCT). Perioperative outcomes and the postoperative preservation ratio of estimated glomerular filtration rate (eGFR) were compared. After the propensity score matching, we evaluated 99 patients in each group. Although the median warm ischemia time (WIT) was less than 25 min in both groups, it was significantly shorter in the UHR-CT group than in the ADCT group (15 min vs. 17 min, p = 0.032). Moreover, the estimated blood loss (EBL) was significantly lower in the UHR-CT group than in the ADCT group (33 mL vs. 50 mL, p = 0.028). However, there were no significant intergroup differences in the postoperative preservation ratio of eGFR at 3 or 6 months of follow-up (ADCT 91.8% vs. UHR-CT 93.5%, p = 0.195; and ADCT 91.7% vs. UHR-CT 94.0%, p = 0.160, respectively). Although no differences in short-term renal function were observed in intraoperative navigation for RAPN in this propensity score–matched cohort, this study is the first to demonstrate that UHR-CT resulted in a shorter WIT and lower EBL than ADCT.

Self-supervised CT super-resolution with hybrid model

Software-based methods can improve CT spatial resolution without changing the hardware of the scanner or increasing the radiation dose to the object. In this work, we aim to develop a deep learning (DL) based CT super-resolution (SR) method that can reconstruct low-resolution (LR) sinograms into high-resolution (HR) CT images. We mathematically analyzed imaging processes in the CT SR imaging problem and synergistically integrated the SR model in the sinogram domain and the deblur model in the image domain into a hybrid model (SADIR). SADIR incorporates the CT domain knowledge and is unrolled into a DL network (SADIR-Net). The SADIR-Net is a self-supervised network, which can be trained and tested with a single sinogram. SADIR-Net was evaluated through SR CT imaging of a Catphan700 physical phantom and a real porcine phantom, and its performance was compared to the other state-of-the-art (SotA) DL-based CT SR methods. On both phantoms, SADIR-Net obtains the highest information fidelity criterion (IFC), structure similarity index (SSIM), and lowest root-mean-square-error (RMSE). As to the modulation transfer function (MTF), SADIR-Net also obtains the best result and improves the MTF50% by 69.2% and MTF10% by 69.5% compared with FBP. Alternatively, the spatial resolutions at MTF50% and MTF10% from SADIR-Net can reach 91.3% and 89.3% of the counterparts reconstructed from the HR sinogram with FBP. The results show that SADIR-Net can provide performance comparable to the other SotA methods for CT SR reconstruction, especially in the case of extremely limited training data or even no data at all. Thus, the SADIR method could find use in improving CT resolution without changing the hardware of the scanner or increasing the radiation dose to the object.

Carotid computed tomography angiography after cobalt-based alloy carotid artery stenting using ultra-high-resolution computed tomography with model-based iterative reconstruction

In conventional carotid computed tomographic angiography, the artifacts of the stent vary depending on the structure and characteristics of the alloy type. Cobalt-based alloy stents have been reported to exhibit high artifacts, and accurate evaluation of the internal lumen can be difficult. Recently, ultra-high-resolution computed tomography scanner systems have become available for clinical practice. The primary features of this computed tomography scanner are a 0.25-mm detector row width and a 1024 × 1024 matrix. We report a case-series of carotid artery stenting using a cobalt-based alloy stent scanned by an ultra-high-resolution computed tomography scanner system and model-based iterative reconstruction. We also report that the combination of the ultra-high-resolution computed tomography scanner system with model-based iterative reconstruction would be useful to evaluate vessel patency after placement of a cobalt-based alloy stent.

Deep learning-based and hybrid-type iterative reconstructions for CT: comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions

PURPOSE

To determine the image quality improvement including vascular structures using deep learning reconstruction (DLR) for ultra-high-resolution CT (UHR-CT) and area-detector CT (ADCT) compared to a commercially available hybrid-iterative reconstruction (IR) method.

MATERIALS AND METHOD

Thirty-two patients suspected of renal cell carcinoma underwent dynamic contrast-enhanced (CE) CT using UHR-CT or ADCT systems. CT value and contrast-to-noise ratio (CNR) on each CT dataset were assessed with region of interest (ROI) measurements. For qualitative assessment of improvement for vascular structure visualization, each artery was assessed using a 5-point scale. To determine the utility of DLR, CT values and CNRs were compared among all UHR-CT data by means of ANOVA followed by Bonferroni post hoc test, and same values on ADCT data were also compared between hybrid IR and DLR methods by paired t test.

RESULTS

For all arteries except the aorta, the CT value and CNR of the DLR method were significantly higher compared to those of the hybrid-type IR method in both CT systems reconstructed as 512 or 1024 matrixes (p < 0.05).

CONCLUSION

DLR has a higher potential to improve the image quality resulting in a more accurate evaluation for vascular structures than hybrid IR for both UHR-CT and ADCT.

Updates in Vascular Computed Tomography

Computed tomography angiography (CTA) has become a mainstay for the imaging of vascular diseases, because of high accuracy, availability, and rapid turnaround time. High-quality CTA images can now be routinely obtained with high isotropic spatial resolution and temporal resolution. Advances in CTA have focused on improving the image quality, increasing the acquisition speed, eliminating artifacts, and reducing the doses of radiation and iodinated contrast media. Dual-energy computed tomography provides material composition capabilities that can be used for characterizing lesions, optimizing contrast, decreasing artifact, and reducing radiation dose. Deep learning techniques can be used for classification, segmentation, quantification, and image enhancement.