Soft Tissue Contrast Research Articles

Key Insights on the Classification and Theranostic Applications of Magnetic Resonance Imaging Contrast Agents.

Magnetic resonance imaging (MRI) is a non-invasive molecular imaging tool being extensively employed in clinical and biomedical research for the detection of a broad spectrum of diseases. This technique offers remarkable spatial resolution, good tissue penetration and a high soft tissue contrast. Contrast agents (CAs) have been regularly used in MRI tests to enhance the resolution of MR images and to visualize the diseased sites in the body. In the past years, considerable efforts have been devoted towards developing new theranostic MRI agents that can be tailored to integrate the targeting and therapeutic functions in a single agent. In this review, we have underlined the role of the MRI CAs in the developing field of 'theranostics' and their recent applications in the combined imaging and therapy of different types of tumors. In addition, this review also outlines the different categories of MRI CAs and their comprehensive classification based on different criteria such as chemical composition, relaxation mechanism and biodistribution with clinically relevant examples.

Citizen science approach to assessing patient perception of MRI with flexible radiofrequency coils

Magnetic Resonance Imaging (MRI) is a major medical imaging modality, which is non-invasive and provides unique soft tissue contrast without ionizing radiation. The successful completion of MRI exams critically depends on patient compliance, and, thus patient comfort. The design, appearance and usability of local MRI radiofrequency (RF) coils potentially influences the patients’ perception of the exam. However, systematic investigations and empirical evidence for these aspects are missing. A questionnaire specifically evaluating the impact of RF coils on patient comfort in MRI would be a valuable addition to clinical studies comparing the performance of novel flexible RF coils with standard rigid coils. This paper describes the development of such a questionnaire in the scope of a citizen science (CS) initiative conducted with a group of students at the upper secondary school level. In this work, the CS initiative is presented in the format of a case report and its impact on scientific projects and the students’ education is outlined. The resulting questionnaire is made available in German and English so as to be directly applicable by researchers working on the clinical evaluation of novel RF coils or the comfort evaluation of specific hardware setups in general.

Beginning of clinical treatment using the 1.5 Tesla MR-Linac system in Japan: a narrative review.

In the field of radiation therapy, image-guided radiotherapy (IGRT) technology has been gradually improving and highly accurate radiation treatment has been possible. Research on IGRT using 1.5 Tesla magnetic resonance imaging (MRI) began in 1999, and a radiation therapy device called 1.5 Tesla magnetic resonance linear accelerator (MR-Linac), which combines a linear accelerator with 1.5 Tesla MRI, was developed in Europe. The aim of this review is to present an overview of 1.5 Tesla MR-Linac with a review of the literature and our experience. Reports related to 1.5 Tesla MR-Linac were searched for in PubMed and are discussed in relation to our experience. The 1.5 Tesla MR-Linac enables IGRT using 1.5 Tesla MRI, further enhancing the precision of radiation therapy. Position verification by cone-beam computed tomography (CBCT) is performed in many institutions, but soft tissue contrast is often unclear in CBCT images of the abdomen and mediastinal organs. Since the 1.5 Tesla MR-Linac allows position verification using MRI, position verification can be performed using clear MRI even in regions where CBCT is unclear. With the 1.5 Tesla MR-Linac, it is possible to perform online adaptive radiotherapy (ART) using 1.5 Tesla MRI. Online ART is a method in which images are acquired while the patient is on the treatment table. The method is based on the current condition of the organs in the body on that day and an optimal treatment field is recreated. Additionally, it allows monitoring of tumor motion using cine images obtained by 1.5 Tesla MRI during the delivery of X-ray radiation. A previous report showed that patients with prostate cancer who received radiotherapy by MR-Linac had fewer side effects than those in patients who received conventional CBCT radiation therapy. The 1.5 Tesla MR-Linac obtained CE-mark certification in Europe in August 2018 and it has been used for clinical treatment. In Japan, clinical treatment using this device started in 2021. By using 1.5 Tesla MR-Linac, patients can be provided with higher precision radiotherapy. In this review, we provide an overview of 1.5 Tesla MR-Linac.

Sine Spin flat detector CT can improve cerebral soft tissue imaging: a retrospective in vivo study

BackgroundFlat detector computed tomography (FDCT) is frequently applied for periinterventional brain imaging within the angiography suite. Novel technical developments such as the Sine Spin FDCT (S-FDCT) may provide an improved cerebral soft tissue contrast. This study investigates the effect of S-FDCT on the differentiation between gray and white matter compared to conventional FDCT (C-FDCT) and multidetector computed tomography (MDCT).MethodsA retrospective analysis of a prospectively maintained patient database was performed, including patients who underwent mechanical thrombectomy in our institution and received S-FDCT or C-FDCT as well as MDCT. Differentiation between gray and white matter on the contralateral hemisphere to the ischemic stroke was analyzed quantitatively by contrast-to-noise ratio (CNR) and qualitatively (5-point ordinal scale).ResultsIn a cohort of 109 patients, MDCT demonstrated the best differentiation between gray and white matter compared to both FDCT techniques (p ≤ 0.001). Comparing both generations of FDCT, S-FDCT provided better visibility of the basal ganglia (p = 0.045) and the supratentorial cortex (p = 0.044) compared to C-FDCT both in quantitative and qualitative analyses. Median CNR were as follows: S-FDCT 2.41 (interquartile range [IQR] 1.66–3.21), C-FDCT 0.96 (0.46–1.70), MDCT 3.43 (2.83–4.17). For basal ganglia, median score and IQR were as follows: S-FDCT 2.00 (2.00–3.00), C-FDCT 1.50 (1.00–2.00), MDCT 5.00 (4.00–5.00).ConclusionsThe novel S-FDCT improves the periinterventional imaging quality of cerebral soft tissue compared to C-FDCT. Thus, it may improve the diagnosis of complications within the angiography suite. MDCT provides the best option for x-ray-based imaging of the brain tissue.Relevance statementFlat detector computed tomography is a promising technique for cerebral soft tissue imaging, while the novel Sine Spin flat detector computed tomography technique improves imaging quality compared to conventional flat detector computed tomography and thus may facilitate periinterventional diagnosis of gray and white matter.Key points• Flat detector computed tomography (FDCT) is frequently applied for periinterventional brain imaging.• The potential of novel Sine Spin FDCT (S-FDCT) is unknown so far.• S-FDCT improves the visibility of cerebral soft tissue compared to conventional FDCT.• Multidetector computed tomography is superior to both FDCT techniques.• S-FDCT may facilitate the evaluation of brain parenchyma within the angiography suite.Graphical

MR Imaging‐Guided High Intensity Focused Ultrasound in the Treatment of Neurologic Disease: Current Applications and Future Directions

MR imaging‐guided focused ultrasound is a revolutionary noninvasive technique with a wide breadth of clinical potential. MR imaging‐guided focused ultrasound uses focused ultrasound energy delivered transcranially to treat a variety of neurologic disease processes. High-intensity focused ultrasound creates sufficient acoustic energy for targeted noninvasive thermal ablation of intracranial soft tissues, with sharply marginated treatment zones overall superior to the established percutaneous ablation techniques. The use of concurrent MR imaging allows highly accurate spatial and thermal guidance, with fine anatomic detail, high soft-tissue contrast, and real-time monitoring of the treatment zone. Here we review the current established applications of MR imaging‐guided focused ultrasound in the treatment of neurologic disease and the relevant anatomic targets for each discussed disease entity, with a focus on essential tremor, Parkinson disease, and neuropathic pain, along with investigative applications and future directions related to this innovative technology.Learning Objective: To provide a comprehensive review of the existing literature on current neurologic applications of MR imaging‐guided high-intensity focused ultrasound and the relevant anatomic targets for each disease entity, discuss investigative applications and future directions for MR imaging‐guided high-intensity focused ultrasound, and increase awareness among providers to promote safe informed use of this relatively newer technology

Visualization of rat tendon in three dimensions using micro-Computed Tomography

Micro-computed tomography (CT) is an X-ray-based imaging modality that produces three-dimensional (3D), high-resolution images of whole-mount tissues, but is typically limited to dense tissues, such as bone. The X-rays readily pass-through tendons, rendering them transparent. Contrast-enhancing chemical stains have been explored, but their use to improve contrast in different tendon types and across developmental stages for micro-CT imaging has not been systematically evaluated. Therefore, we investigated how phosphotungstic acid (PTA) staining and tissue hydration impacts tendon contrast for micro-CT imaging. We showed that PTA staining increased X-ray absorption of tendon to enhance tissue contrast and obtain 3D micro-CT images of immature (postnatal day 21) and sexually mature (postnatal day 50) rat tendons within the tail and hindlimb. Further, we demonstrated that tissue hydration state following PTA staining significantly impacts soft tissue contrast. Using this method, we also found that tail tendon fascicles appear to cross between fascicle bundles. Ultimately, contrast-enhanced 3D micro-CT imaging will lead to better understanding of tendon structure, and relationships between the bone and soft tissues.•Simple tissue fixation and staining technique enhances soft tissue contrast for tendon visualization using micro-CT.•3D tendon visualization in situ advances understanding of musculoskeletal tissue structure and organization.

A pictorial review of scrotal and penile pathology on computed tomography.

Computed tomography (CT) may show a variety of scrotal and penile pathologic finding, but is usually not used as a first-line imaging due to its limited soft tissue contrast. Nonetheless, there are three main scenarios for imaging of the scrotum and penis with CT. Pathologies may be found incidentally in patients undergoing abdominal and pelvic CT scanning for different reasons. In emergency settings, CT is frequently performed, and the recognition of scrotal and penile pathologies by the reporting radiologist is crucial to ensure optimal patient treatment and outcome. If MRI scanning cannot be performed due to contraindications or is unavailable in resource, limited CT may be used for the further characterization of scrotal and penile pathology found on ultrasound. This pictorial review wants to familiarize general and emergency radiologists with the anatomy and possible pathological findings of the scrotum and penis on CT.

Deep learning application for abdominal organs segmentation on 0.35 T MR-Linac images.

Linear accelerator (linac) incorporating a magnetic resonance (MR) imaging device providing enhanced soft tissue contrast is particularly suited for abdominal radiation therapy. In particular, accurate segmentation for abdominal tumors and organs at risk (OARs) required for the treatment planning is becoming possible. Currently, this segmentation is performed manually by radiation oncologists. This process is very time consuming and subject to inter and intra operator variabilities. In this work, deep learning based automatic segmentation solutions were investigated for abdominal OARs on 0.35 T MR-images. One hundred and twenty one sets of abdominal MR images and their corresponding ground truth segmentations were collected and used for this work. The OARs of interest included the liver, the kidneys, the spinal cord, the stomach and the duodenum. Several UNet based models have been trained in 2D (the Classical UNet, the ResAttention UNet, the EfficientNet UNet, and the nnUNet). The best model was then trained with a 3D strategy in order to investigate possible improvements. Geometrical metrics such as Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Hausdorff Distance (HD) and analysis of the calculated volumes (thanks to Bland-Altman plot) were performed to evaluate the results. The nnUNet trained in 3D mode achieved the best performance, with DSC scores for the liver, the kidneys, the spinal cord, the stomach, and the duodenum of 0.96 ± 0.01, 0.91 ± 0.02, 0.91 ± 0.01, 0.83 ± 0.10, and 0.69 ± 0.15, respectively. The matching IoU scores were 0.92 ± 0.01, 0.84 ± 0.04, 0.84 ± 0.02, 0.54 ± 0.16 and 0.72 ± 0.13. The corresponding HD scores were 13.0 ± 6.0mm, 16.0 ± 6.6mm, 3.3 ± 0.7mm, 35.0 ± 33.0mm, and 42.0 ± 24.0mm. The analysis of the calculated volumes followed the same behavior. Although the segmentation results for the duodenum were not optimal, these findings imply a potential clinical application of the 3D nnUNet model for the segmentation of abdominal OARs for images from 0.35 T MR-Linac.

X-ray phase contrast reveals soft tissue and shell growth lines in mollusks

High-resolution 3D imaging of species with exoskeletons such as shell-bearing mollusks typically involves destructive steps. Nondestructive alternatives are desirable since samples can be rare and valuable, and destructive steps are time-consuming and may distort the tissue. Here, we show for the first time that propagation-based phase-contrast X-ray imaging can significantly increase contrast in mollusks with intact shells. By using the recently upgraded monochromator at the SPring-8 BL20B2 synchrotron beamline, we imaged six species of mollusks, showing that X-ray phase contrast enhances soft-tissue contrast. Features that are almost invisible in conventional attenuation-based micro-computed tomography (micro-CT) are clearly reproduced with phase-contrast imaging under the same scan conditions. Furthermore, this method can reveal features such as growth rings in the shell and differentiate between calcite and aragonite crystal forms. Phase-contrast imaging can thus serve as a compelling alternative when destructive methods are not an option.

Editorial for “Whole-Body Magnetic Resonance Imaging at 0.05 Tesla”

Magnetic Resonance Imaging (MRI) stands as a cornerstone of modern medical diagnostics, offering noninvasive visualization of internal body structures with unparalleled detail. Its ability to produce detailed images with exceptional soft tissue contrast has made it an indispensable tool in various medical specialties, ranging from neurology to oncology. However, the widespread adoption of MRI has been hindered by its high cost and infrastructure requirements, which pose significant challenges, particularly in low-income and developing regions.

The CathEye: A Forward-Looking Ultrasound Catheter for Image-Guided Cardiovascular Procedures.

Catheter based procedures are typically guided by X-Ray, which suffers from low soft tissue contrast and only provides 2D projection images of a 3D volume. Intravascular ultrasound (IVUS) can serve as a complementary imaging technique. Forward viewing catheters are useful for visualizing obstructions along the path of the catheter. The CathEye system mechanically steers a single-element transducer to generate a forward-looking surface reconstruction from an irregularly spaced 2-D scan pattern. The steerable catheter leverages an expandable frame with cables to manipulate the distal end independently of vessel tortuosity. The tip position is estimated by measuring the cable displacements and used to create surface reconstructions of the imaging workspace with the single-element transducer. CathEye's imaging capabilities were tested with an agar phantom and an ex vivo chronic total occlusion (CTO) sample while the catheter was confined to various tortuous paths. The CathEye maintained similar scan patterns regardless of path tortuosity and was able to recreate major features of the imaging targets, such as holes and extrusions. The feasibility of forward-looking IVUS with the CathEye is demonstrated in this study. The CathEye mechanism can be applied to other imaging modalities with field-of-view (FOV) limitations and represents the basis for an interventional device fully integrated with image guidance.

Hybrid Simulated Annealing‐Evaporation Rate‐Based Water Cycle Algorithm Application for Medical Image Enhancement

Visualizing medical images is difficult due to artifacts, poor local contrast, low soft tissue contrast, excessive noise levels, and a wide dynamic range. This has created a serious problem for physicians, resulting in unapproachable and inaccurate disease diagnoses. To circumvent this problem, this research proposes a medical image enhancement (MIE) approach based on the hybrid simulated annealing‐evaporation rate‐based water cycle algorithm (SA‐ERWCA). The ERWCA enhances distorted medical images by finding the best optimal solution for the transformation parameters according to the fitness function. The fitness function consists of three objective measurements, namely, entropy, number of edges, and sum of edge intensities. Due to its high potential for finding a globally optimal solution, SA is applied solely to determine the optimized initial population of the ERWCA, thereby enhancing its convergence characteristics. To simply put, the main objective of SA is to avoid premature convergence of the ERWCA. Thus, blending SA and ERWCA produces better‐quality medical images. The performance of the proposed algorithm was compared to histogram equalization (HE), low contrast stretching (LCS), contrast‐limited adaptive histogram equalization (CLAHE), particle swarm optimization (PSO), accelerated PSO (APSO), and the water cycle algorithm (WCA). Along with the objective function fitness, seven full reference (FR) image quality assessment (IQA) metrics were implemented to evaluate image quality and compare performance. The findings of the present study showed that the proposed strategy outperforms all of the compared methods in terms of objective function fitness and perceptual visual IQA metrics such as the Harr wavelet‐based perceptual similarity index (HaarPSI), visual signal‐to‐noise ratio (VSNR), and information content weighted structural similarity index measure (IW‐SSIM). The suggested technique also exhibited better stability and faster convergence time to the optimum solution. Furthermore, the proposed approach outperformed others by avoiding premature convergence to the best solution and providing excellent optimization results with acceptable computational efficiency.

Deep Learning-based Image Enhancement Techniques for Fast MRI in Neuroimaging.

Despite its superior soft tissue contrast and non-invasive nature, MRI requires long scan times due to its intrinsic signal acquisition principles, a main drawback which technological advancements in MRI have been focused on. In particular, scan time reduction is a natural requirement in neuroimaging due to detailed structures requiring high resolution imaging and often volumetric (3D) acquisitions, and numerous studies have recently attempted to harness deep learning (DL) technology in enabling scan time reduction and image quality improvement. Various DL-based image reconstruction products allow for additional scan time reduction on top of existing accelerated acquisition methods without compromising the image quality.

Feasibility and clinical implementation of MRI-guided surface brachytherapy.

Best practices for high-dose-rate surface applicator brachytherapy treatment (SABT) have long relied on computed tomography (CT)-based imaging to visualize diseased sites for treatment planning. Compared with magnetic resonance (MR)-based imaging, CT provides insufficient soft tissue contrast. This work described the feasibility of clinical implementation of MR-based imaging in SABT planning to provide individualized treatment optimization. A 3D-printed phantom was used to fit Freiberg flap-style (Elekta, The Netherlands) applicator. Images were taken using an optimized pointwise encoding time reduction with radial acquisition (PETRA) MR sequence for catheter visualization, and a helical CT scan to generate parallel treatment plans. This clinical study included three patients undergoing SABT for Dupuytren's contracture/palmar fascial fibromatosis imaged with the same modalities.SABT planning was performed in Oncentra Brachy (Elekta Brachytherapy, The Netherlands) treatment planning software. A geometric analysis was conducted by comparing CT-based digitization with MR-based digitization. CT and MR dwell positions underwent a rigid registration, and average Euclidean distances between dwell positions were calculated. A dosimetric comparison was performed, including point-based dose difference calculations and volumetric segmentations with Dice similarity coefficient (DSC) calculations. Euclidean distances between dwell positions from CT-based and MR-based plans were on average 0.68 ±0.05 mm and 1.35 ±0.17 mm for the phantom and patients, respectively. The point dose difference calculations were on average 0.92% for the phantom and 1.98% for the patients. The D95 and D90 DSC calculations were both 97.9% for the phantom, and on average 93.6% and 94.2%, respectively, for the patients. The sub-millimeter accuracy of dwell positions and high DSC's (> 0.95) of the phantom demonstrated that digitization was clinically acceptable, and accurate treatment plans were produced using MR-only imaging. This novel approach, MRI-guided SABT, will lead to individualized prescriptions for potentially improved patient outcomes.

Unsupervised Fusion of Misaligned PAT and MRI Images via Mutually Reinforcing Cross-Modality Image Generation and Registration.

Photoacoustic tomography (PAT) and magnetic resonance imaging (MRI) are two advanced imaging techniques widely used in pre-clinical research. PAT has high optical contrast and deep imaging range but poor soft tissue contrast, whereas MRI provides excellent soft tissue information but poor temporal resolution. Despite recent advances in medical image fusion with pre-aligned multimodal data, PAT-MRI image fusion remains challenging due to misaligned images and spatial distortion. To address these issues, we propose an unsupervised multi-stage deep learning framework called PAMRFuse for misaligned PAT and MRI image fusion. PAMRFuse comprises a multimodal to unimodal registration network to accurately align the input PAT-MRI image pairs and a self-attentive fusion network that selects information-rich features for fusion. We employ an end-to-end mutually reinforcing mode in our registration network, which enables joint optimization of cross-modality image generation and registration. To the best of our knowledge, this is the first attempt at information fusion for misaligned PAT and MRI. Qualitative and quantitative experimental results show the excellent performance of our method in fusing PAT-MRI images of small animals captured from commercial imaging systems.

Computed tomography and magnetic resonance imaging features of suspected transitional cell carcinoma lesions involving the bladder, prostate, and urethra in a dog: a case report

A 14-year-old, spayed female, poodle was presented with dysuria and hematuria. A mass that appeared hypoechoic on ultrasound and hypoattenuating on computed tomography (CT) extended from the bladder neck to the urethra. Magnetic resonance imaging (MRI) showed the mass invading the muscular layer of the bladder, urethra, and prostate with distinct margins. Transitional cell carcinoma (TCC) was confirmed with the CADET-BRAF test. This study describes the CT and MRI features of suspected TCC lesions involving the bladder, prostate, and urethra. MRI showed superior soft tissue contrast resolution, enabling evaluation of invasion of the muscular layer of the bladder and urethra.

Pseudo-Computed Tomography Generation from Noisy Magnetic Resonance Imaging with Deep Learning Algorithm

Purpose: Magnetic Resonance Imaging (MRI) applications offer superior soft tissue contrast compared with Computed Tomography (CT) for accurate radiotherapy planning although MRI images suffer from poor image quality and lack electron density for radiation dose calculation. The present study aims to use the Deep Learning (DL) approach to 1) enhance the quality of MRI images and 2) generate synthetic CT images using MRI images for more accurate radiotherapy planning.
 Materials and Methods: In this paper, the pix2pix Generative Adversarial Network was utilized to synthesize CT images from noisy MRI images of 20 arbitrarily patients with brain disease. The standard statistical measurements investigated the accuracy comparison of the modeled Hounsfield Unit (HU) value from MRI images and referenced CT of each patient. The famous quality metrics that were used to compare synthetic CTs and referenced CTs were the Mean Absolute eError (MAE), the structural similarity index (SSIM), and the Peak Signal-to-Noise Ratio (PSNR).
 Results: The higher quality measurements between the synthetic pseudo-CT and the referenced CT images as PSNR and SSIM should correlate with the lower MAE value. For the overall brain among blind test data, the measured peak signal-to-noise ratio, mean absolute error, and structural similarity index values were about 16.5, 28.13, and 93.46, respectively.
 Conclusion: The proposed method provides an acceptable level of statistical measurements computed on the Pseudo-CT and referenced CT, and it could be concluded that the p-CT can be implemented in radiotherapy treatment planning with acceptable accuracy.

The development of a 1.25 MHz 1024-channel sparse array for human transcranial imaging: in vitro characterization

Ultrasound imaging is overwhelmingly used as 2D modality even though 3D imaging capabilities have existed for decades. Recent generational shifts toward super-resolution ultrasound imaging and functional ultrasound imaging, especially in the brain, have generated renewed and sustained interest in acquiring truly volumetric, 4D data. However, volumetric imaging approaches are currently limited to small animals, due in part to the difficulty of imaging transcranially in humans and due to a lack of imaging arrays designed for this purpose. Clinical translation of these recent techniques as well as conventional diagnostic B-mode imaging may thus benefit from array designs that capitalize on large channel count imaging systems. We have designed and developed a 1024-channel sparse array with a 65 mm circular aperture and a 1–2 MHz bandwidth. This unique transducer achieves an aperture that is far larger than conventional matrix probes using a sparse arrangement of elements ordered in a density-tapered spiral design. This design has significantly decreased grating lobes compared to a matrix array probe. The large aperture of this probe also enables acquisition over a large field of view with a significant depth of more than 100 mm. Simulations, acoustic characterization, and in vitro tests demonstrate that this transducer achieves a high focal gain that enables ultrasonic visualization beneath the human skull and at large depths due to its low F-number capabilities. Furthermore, we show that this transducer is capable of high point target contrast and high soft tissue contrast, with contrast-to-noise ratios up to 1.9 when imaging transcranially through a 3 mm thick section of human skull. Because of the large surface area of this probe, it can capture over 3 coherence lengths in each dimension and is, therefore, able to able to ‘average out’ the aberration over a large surface area. This transducer is poised to have a significant clinical impact in transcranial human imaging.

Evaluating the Hounsfield unit assignment and dose differences between CT-based standard and deep learning-based synthetic CT images for MRI-only radiation therapy of the head and neck.

Magnetic resonance image only (MRI-only) simulation for head and neck (H&N) radiotherapy (RT) could allow for single-image modality planning with excellent soft tissue contrast. In the MRI-only simulation workflow, synthetic computed tomography (sCT) is generated from MRI to provide electron density information for dose calculation. Bone/air regions produce little MRI signal which could lead to electron density misclassification in sCT. Establishing the dosimetric impact of this error could inform quality assurance (QA) procedures using MRI-only RT planning or compensatory methods for accurate dosimetric calculation. The aim of this study was to investigate if Hounsfield unit (HU) voxel misassignments from sCT images result in dosimetric errors in clinical treatment plans. Fourteen H&N cancer patients undergoing same-day CT and 3T MRI simulation were retrospectively identified. MRI was deformed to the CT using multimodal deformable image registration. sCTs were generated from T1w DIXON MRIs using a commercially available deep learning-based generator (MRIplanner, Spectronic Medical AB, Helsingborg, Sweden). Tissue voxel assignment was quantified by creating a CT-derived HU threshold contour. CT/sCT HU differences for anatomical/target contours and tissue classification regions including air (<250 HU), adipose tissue (-250 HU to -51 HU), soft tissue (-50 HU to 199 HU), spongy (200 HU to 499 HU) and cortical bone(>500 HU) were quantified. t-test was used to determine if sCT/CT HU differences were significant. The frequency of structures that had a HU difference>80 HU (the CT window-width setting for intra-cranial structures) was computed to establish structure classification accuracy. Clinical intensity modulated radiation therapy (IMRT) treatment plans created on CT were retrospectively recalculated on sCT images and compared using the gamma metric. The mean ratio of sCT HUs relative to CT for air, adipose tissue, soft tissue, spongy and cortical bone were 1.7±0.3, 1.1±0.1, 1.0±0.1, 0.9±0.1 and 0.8±0.1 (value of 1 indicates perfect agreement). T-tests (significance set at t=0.05) identified differences in HU values for air, spongy and cortical bone in sCT images compared to CT. The structures with sCT/CT HU differences>80 HU of note were the left and right (L/R) cochlea and mandible (>79% of the tested cohort), the oral cavity (for 57% of the tested cohort), the epiglottis (for 43% of the tested cohort) and the L/R TM joints (occurring>29% of the cohort). In the case of the cochlea and TM joints, these structures contain dense bone/air interfaces. In the case of the oral cavity and mandible, these structures suffer the additional challenge of being positionally altered in CT versus MRI simulation (due to a non-MR safe immobilizing bite block requiring absence of bite block in MR). Finally, the epiglottis HU assignment suffers from its small size and unstable positionality. Plans recalculated on sCT yielded global/local gamma pass rates of 95.5%±2% (3mm, 3%) and 92.7%±2.1% (2mm, 2%). The largest mean differences in D95, Dmean , D50 dose volume histogram (DVH) metrics for organ-at-risk (OAR) and planning tumor volumes (PTVs) were 2.3%±3.0% and 0.7%±1.9% respectively. In this cohort, HU differences of CT and sCT were observed but did not translate into a reduction in gamma pass rates or differences in average PTV/OAR dose metrics greater than 3%. For sites such as the H&N where there are many tissue interfaces we did not observe large scale dose deviations but further studies using larger retrospective cohorts are merited to establish the variation in sCT dosimetric accuracy which could help to inform QA limits on clinical sCT usage.

Robustness of Single- and Dual-Energy Deep-Learning-Based Scatter Correction Models on Simulated and Real Chest X-rays

Chest X-rays (CXRs) represent the first tool globally employed to detect cardiopulmonary pathologies. These acquisitions are highly affected by scattered photons due to the large field of view required. Scatter in CXRs introduces background in the images, which reduces their contrast. We developed three deep-learning-based models to estimate and correct scatter contribution to CXRs. We used a Monte Carlo (MC) ray-tracing model to simulate CXRs from human models obtained from CT scans using different configurations (depending on the availability of dual-energy acquisitions). The simulated CXRs contained the separated contribution of direct and scattered X-rays in the detector. These simulated datasets were then used as the reference for the supervised training of several NNs. Three NN models (single and dual energy) were trained with the MultiResUNet architecture. The performance of the NN models was evaluated on CXRs obtained, with an MC code, from chest CT scans of patients affected by COVID-19. The results show that the NN models were able to estimate and correct the scatter contribution to CXRs with an error of &lt;5%, being robust to variations in the simulation setup and improving contrast in soft tissue. The single-energy model was tested on real CXRs, providing robust estimations of the scatter-corrected CXRs.