Learning-based Reconstruction Research Articles

Deep learning-based reconstruction improves the image quality of low-dose CT enterography in patients with inflammatory bowel disease

Deep learning-based reconstruction improves the image quality of low-dose CT enterography in patients with inflammatory bowel disease

A comprehensive review on Compton camera image reconstruction: from principles to AI innovations

AbstractCompton cameras have emerged as promising tools in biomedical imaging, offering sensitive gamma-ray imaging capabilities for diverse applications. This review paper comprehensively overviews the latest advancements in Compton camera image reconstruction technologies. Beginning with a discussion of the fundamental principles of Compton scattering and its relevance to gamma-ray imaging, the paper explores the key components and design considerations of Compton camera systems. We then review various image reconstruction algorithms employed in Compton camera systems, including analytical, iterative, and statistical approaches. Recent developments in machine learning-based reconstruction methods are also discussed, highlighting their potential to enhance image quality and reduce reconstruction time in biomedical applications. In particular, we focus on the challenges posed by conical back-projection in Compton camera image reconstruction, and how innovative signal processing techniques have addressed these challenges to improve image accuracy and spatial resolution. Furthermore, experimental validations of Compton camera imaging in preclinical and clinical settings, including multi-tracer and whole-gamma imaging studies are introduced. In summary, this review provides potentially useful information about the current state-of-the-art Compton camera image reconstruction technologies, offering a helpful guide for investigators new to this field.

A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies

AbstractAccelerated magnetic resonance imaging (MRI) has played an essential role in reducing data acquisition time for MRI. Acceleration can be achieved by acquiring fewer data points in k-space, which results in various artifacts in the image domain. Conventional reconstruction methods have resolved the artifacts by utilizing multi-coil information, but with limited robustness. Recently, numerous deep learning-based reconstruction methods have been developed, enabling outstanding reconstruction performances with higher acceleration. Advances in hardware and developments of specialized network architectures have produced such achievements. Besides, MRI signals contain various redundant information including multi-coil redundancy, multi-contrast redundancy, and spatiotemporal redundancy. Utilization of the redundant information combined with deep learning approaches allow not only higher acceleration, but also well-preserved details in the reconstructed images. Consequently, this review paper introduces the basic concepts of deep learning and conventional accelerated MRI reconstruction methods, followed by review of recent deep learning-based reconstruction methods that exploit various redundancies. Lastly, the paper concludes by discussing the challenges, limitations, and potential directions of future developments.

Clinical feasibility of deep learning–accelerated single-shot turbo spin echo sequence with enhanced denoising for pancreas MRI at 3 Tesla

PurposeTo assess the feasibility of the single-shot turbo spin echo sequence using deep learning-based reconstruction (DLR) (HASTEDL) with enhanced denoising for pancreas MRI. MethodsPatients who underwent pancreas MRI from March to April 2021 were included. Four T2-weighted images (non-FS conventional HASTE vs. HASTEDL with enhanced denoising and FS HASTEDL with enhanced denoising vs. HASTEDL) were acquired. Two readers independently assessed the image quality parameters of the two non-FS image sets using a 4-point Likert scale. The signal-to-noise ratio (SNR) of the cystic lesions and pancreatic parenchyma and the contrast-to-noise ratio between the cystic lesion and pancreatic parenchyma were calculated for all four image sets. The size of the largest cystic lesion and the diameter of pancreatic duct were measured. ResultsA total of 63 patients were included, 48 (76.2 %) of whom had 136 pancreatic cystic lesion(s). The acquisition times of conventional HASTE and HASTEDL were 69 and 18 sec, respectively. All image quality parameters except artifacts for reader 2 were significantly better for HASTEDL with enhanced denoising. Those images also received scores for overall image quality that were significantly higher than those for the conventional HASTE (3.26 ± 0.54 vs. 2.47 ± 0.56, p < 0.001). The SNR of the pancreatic cystic lesion and pancreatic parenchyma was significantly higher in the HASTEDL with enhanced denoising (p < 0.001 for both). Inter-reader variability for measuring the pancreatic cyst size (ICC, 0.999 vs. 0.995; 95 % LoA, −0.13481 to 0.14743 vs. −0.24097 to 0.27404) and duct diameter (ICC, 0.994 vs. 0.969; 95 % LoA, −0.11684 to 0.36026 vs. −0.45544 to 0.44664) was lower in HASTEDL with enhanced denoising than in the conventional HASTE. ConclusionHASTEDL with enhanced denoising could be useful for reducing the acquisition time of pancreas MRI while improving the image quality for the evaluation of pancreatic cystic lesions.

High-resolution in vivo 4D-OCT fish-eye imaging using 3D-UNet with multi-level residue decoder.

Optical coherence tomography (OCT) allows high-resolution volumetric imaging of biological tissues in vivo. However, 3D-image acquisition often suffers from motion artifacts due to slow frame rates and involuntary and physiological movements of living tissue. To solve these issues, we implement a real-time 4D-OCT system capable of reconstructing near-distortion-free volumetric images based on a deep learning-based reconstruction algorithm. The system initially collects undersampled volumetric images at a high speed and then upsamples the images in real-time by a convolutional neural network (CNN) that generates high-frequency features using a deep learning algorithm. We compare and analyze both dual-2D- and 3D-UNet-based networks for the OCT 3D high-resolution image reconstruction. We refine the network architecture by incorporating multi-level information to accelerate convergence and improve accuracy. The network is optimized by utilizing the 16-bit floating-point precision for network parameters to conserve GPU memory and enhance efficiency. The result shows that the refined and optimized 3D-network is capable of retrieving the tissue structure more precisely and enable real-time 4D-OCT imaging at a rate greater than 10 Hz with a root mean square error (RMSE) of ∼0.03.

CineVN: Variational network reconstruction for rapid functional cardiac cine MRI.

To develop a reconstruction method for highly accelerated cardiac cine MRI with high spatiotemporal resolution and low temporal blurring, and to demonstrate accurate estimation of ventricular volumes and myocardial strain in healthy subjects and in patients. The proposed method, called CineVN, employs a spatiotemporal Variational Network combined with conjugate gradient descent for optimized data consistency and improved image quality. The method is first evaluated on retrospectively undersampled cine MRI data in terms of image quality. Then, prospectively accelerated data are acquired in 18 healthy subjects both segmented over two heartbeats per slice as well as in real time with 1.6 mm isotropic resolution. Ventricular volumes and strain parameters are computed and compared to a compressed sensing reconstruction and to a conventional reference cine MRI acquisition. Lastly, the method is demonstrated in 46 patients and ventricular volumes and strain parameters are evaluated. CineVN outperformed compressed sensing in image quality metrics on retrospectively undersampled data. Functional parameters and myocardial strain were the most accurate for CineVN compared to two state-of-the-art compressed sensing methods. Deep learning-based reconstruction using our proposed method enables accurate evaluation of cardiac function in real-time cine MRI with high spatiotemporal resolution. This has the potential to improve cardiac imaging particularly for patients with arrhythmia or impaired breath-hold capability.

Deep learning reconstruction for optimized bone assessment in zero echo time MR imaging of the knee

PurposeTo evaluate the impact of deep learning-based reconstruction (DLRecon) on bone assessment in zero echo-time (ZTE) MRI of the knee at 1.5 Tesla. MethodsThis retrospective study included 48 consecutive exams of 46 patients (23 females) who underwent clinically indicated knee MRI at 1.5 Tesla. Standard imaging protocol comprised a sagittal prescribed, isotropic ZTE sequence. ZTE image reconstruction was performed with a standard-of-care (non-DL) and prototype DLRecon method. Exams were divided into subsets with and without osseous pathology based on the radiology report. Using a 4-point scale, two blinded readers qualitatively graded features of bone depiction including artifacts and conspicuity of pathology including diagnostic certainty in the respective subsets. Quantitatively, one reader measured signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of bone. Comparative analyses were conducted to assess the differences between the reconstruction methods. In addition, interreader agreement was calculated for the qualitative gradings. ResultsDLRecon significantly improved gradings for bone depiction relative to non-DL reconstruction (all, p < 0.05), while there was no significant difference with regards to artifacts (both, median score of 0; p = 0.058). In the subset with pathologies, conspicuity of pathology and diagnostic confidence were also scored significantly higher in DLRecon compared to non-DL (median 3 vs 2; p ≤ 0.03). Interreader agreement ranged from moderate to almost-perfect (κ = 0.54–0.88). Quantitatively, DLRecon demonstrated significantly enhanced CNR and SNR of bone compared to non-DL (p < 0.001). ConclusionZTE MRI with DLRecon improved bone depiction in the knee, compared to non-DL. Additionally, DLRecon increased conspicuity of osseous findings together with diagnostic certainty.

Learning-based Reconstruction of GRACE Data Based on Changes in Total Water Storage and Its Accuracy Assessment

Learning-based Reconstruction of GRACE Data Based on Changes in Total Water Storage and Its Accuracy Assessment

An unrolled neural network for accelerated dynamic MRI based on second-order half-quadratic splitting model

The reconstruction of dynamic magnetic resonance images from incomplete k-space data has sparked significant research interest due to its potential to reduce scan time. However, traditional iterative optimization algorithms fail to faithfully reconstruct images at higher acceleration factors and incur long reconstruction time. Furthermore, end-to-end deep learning-based reconstruction algorithms suffer from large model parameters and lack robustness in the reconstruction results. Recently, unrolled deep learning models, have shown immense potential in algorithm stability and applicability flexibility. In this paper, we propose an unrolled deep learning network based on a second-order Half-Quadratic Splitting(HQS) algorithm, where the forward propagation process of this framework strictly follows the computational flow of the HQS algorithm. In particular, we propose a degradation-sense module by associating random sampling patterns with intermediate variables to guide the iterative process. We introduce the Information Fusion Transformer(IFT) to extract both local and non-local prior information from image sequences, thereby removing aliasing artifacts resulting from random undersampling. Finally, we impose low-rank constraints within the HQS algorithm to further enhance the reconstruction results. The experiments demonstrate that each component module of our proposed model contributes to the improvement of the reconstruction task. Our proposed method achieves comparably satisfying performance to the state-of-the-art methods and it exhibits excellent generalization capabilities across different sampling masks. At the low acceleration factor, there is a 0.7% enhancement in the PSNR. Furthermore, when the acceleration factor reached 8 and 12, the PSNR achieves an improvement of 3.4% and 5.8% respectively.

Assessment of image quality and diagnostic accuracy for cervical spondylosis using T2w-STIR sequence with a deep learning-based reconstruction approach.

To investigate potential of enhancing image quality, maintaining interobserver consensus, and elevating disease diagnostic efficacy through the implementation of deep learning-based reconstruction (DLR) processing in 3.0T cervical spine fast magnetic resonance imaging (MRI) images, compared with conventional images. The 3.0T cervical spine MRI images of 71 volunteers were categorized into two groups: sagittal T2-weighted short T1 inversion recovery without DLR (Sag T2w-STIR) and with DLR (Sag T2w-STIR-DLR). The assessment covered artifacts, perceptual signal-to-noise ratio, clearness of tissue interfaces, fat suppression, overall image quality, and the delineation of spinal cord, vertebrae, discs, dopamine, and joints. Spanning canal stenosis, neural foraminal stenosis, herniated discs, annular fissures, hypertrophy of the ligamentum flavum or vertebral facet joints, and intervertebral disc degeneration were evaluated by three impartial readers. Sag T2w-STIR-DLR images exhibited markedly superior performance across quality indicators (median = 4 or 5) compared to Sag T2w-STIR sequences (median = 3 or 4) (p < 0.001). No statistically significant differences were observed between the two sequences in terms of diagnosis and grading (p > 0.05). The interobserver agreement for Sag T2w-STIR-DLR images (0.604-0.931) was higher than the other (0.545-0.853), Sag T2w-STIR-DLR (0.747-1.000) demonstrated increased concordance between reader 1 and reader 3 in comparison to Sag T2w-STIR (0.508-1.000). Acquisition time diminished from 364 to 197s through the DLR scheme. Our investigation establishes that 3.0T fast MRI images subjected to DLR processing present heightened image quality, bolstered diagnostic performance, and reduced scanning durations for cervical spine MRI compared with conventional sequences.

Deep learning CT reconstruction improves liver metastases detection

ObjectivesDetection of liver metastases is crucial for guiding oncological management. Computed tomography through iterative reconstructions is widely used in this indication but has certain limitations. Deep learning image reconstructions (DLIR) use deep neural networks to achieve a significant noise reduction compared to iterative reconstructions. While reports have demonstrated improvements in image quality, their impact on liver metastases detection remains unclear. Our main objective was to determine whether DLIR affects the number of detected liver metastasis. Our secondary objective was to compare metastases conspicuity between the two reconstruction methods.MethodsCT images of 121 patients with liver metastases were reconstructed using a 50% adaptive statistical iterative reconstruction (50%-ASiR-V), and three levels of DLIR (DLIR-low, DLIR-medium, and DLIR-high). For each reconstruction, two double-blinded radiologists counted up to a maximum of ten metastases. Visibility and contour definitions were also assessed. Comparisons between methods for continuous parameters were performed using mixed models.ResultsA higher number of metastases was detected by one reader with DLIR-high: 7 (2–10) (median (Q₁–Q₃); total 733) versus 5 (2–10), respectively for DLIR-medium, DLIR-low, and ASiR-V (p < 0.001). Ten patents were detected with more metastases with DLIR-high simultaneously by both readers and a third reader for confirmation. Metastases visibility and contour definition were better with DLIR than ASiR-V.ConclusionDLIR-high enhanced the detection and visibility of liver metastases compared to ASiR-V, and also increased the number of liver metastases detected.Critical relevance statementDeep learning-based reconstruction at high strength allowed an increase in liver metastases detection compared to hybrid iterative reconstruction and can be used in clinical oncology imaging to help overcome the limitations of CT.Key PointsDetection of liver metastases is crucial but limited with standard CT reconstructions.More liver metastases were detected with deep-learning CT reconstruction compared to iterative reconstruction.Deep learning reconstructions are suitable for hepatic metastases staging and follow-up.Graphical

3D reconstruction of an inertial-confinement fusion implosion with neural networks using multiple heterogeneous data sources.

3D asymmetries are major degradation mechanisms in inertial-confinement fusion implosions at the National Ignition Facility (NIF). These asymmetries can be diagnosed and reconstructed with the neutron imaging system (NIS) on three lines of sight around the NIF target chamber. Conventional tomographic reconstructions are used to reconstruct the 3D morphology of the implosion using NIS [Volegov et al., J. Appl. Phys. 127, 083301 (2020)], but the problem is ill-posed with only three imaging lines of sight. Asymmetries can also be diagnosed with the real-time neutron activation diagnostics (RTNAD) and the neutron time-of-flight (nToF) suite. Since the NIS, RTNAD, and nToF each sample a different part of the implosion using different physical principles, we propose that it is possible to overcome the limitations of too few imaging lines of sight by performing 3D reconstructions that combine information from all three heterogeneous data sources. This work presents a new machine learning-based reconstruction technique to do just this. By using a simple physics model and group of neural networks to map 3D morphologies to data, this technique can easily account for data of multiple different types. A simple proof-of-principle is presented, demonstrating that this technique can accurately reconstruct a hot-spot shape using synthetic primary neutron images and a hot-spot velocity vector. In particular, the hot-spot's asymmetry, quantified as spherical harmonic coefficients, is reconstructed to within ±4% of the radius in 90% of test cases. In the future, this technique will be applied to actual NIS, RTNAD, and nToF data to better understand 3D asymmetries at the NIF.

Deep Learning-Based Reconstruction of 3D T1 SPACE Vessel Wall Imaging Provides Improved Image Quality with Reduced Scan Times: A Preliminary Study.

Intracranial vessel wall imaging is technically challenging to implement, given the simultaneous requirements of high spatial resolution, excellent blood and CSF signal suppression, and clinically acceptable gradient times. Herein, we present our preliminary findings on the evaluation of a deep learning-optimized sequence using T1-weighted imaging. Clinical and optimized deep learning-based image reconstruction T1 3D Sampling Perfection with Application optimized Contrast using different flip angle Evolution (SPACE) were evaluated, comparing noncontrast sequences in 10 healthy controls and postcontrast sequences in 5 consecutive patients. Images were reviewed on a Likert-like scale by 4 fellowship-trained neuroradiologists. Scores (range, 1-4) were separately assigned for 11 vessel segments in terms of vessel wall and lumen delineation. Additionally, images were evaluated in terms of overall background noise, image sharpness, and homogeneous CSF signal. Segment-wise scores were compared using paired samples t tests. The scan time for the clinical and deep learning-based image reconstruction sequences were 7:26 minutes and 5:23 minutes respectively. Deep learning-based image reconstruction images showed consistently higher wall signal and lumen visualization scores, with the differences being statistically significant in most vessel segments on both pre- and postcontrast images. Deep learning-based image reconstruction had lower background noise, higher image sharpness, and uniform CSF signal. Depiction of intracranial pathologies was better or similar on the deep learning-based image reconstruction. Our preliminary findings suggest that deep learning-based image reconstruction-optimized intracranial vessel wall imaging sequences may be helpful in achieving shorter gradient times with improved vessel wall visualization and overall image quality. These improvements may help with wider adoption of intracranial vessel wall imaging in clinical practice and should be further validated on a larger cohort.

Evaluation of deep-learning TSE images in clinical musculoskeletal imaging.

In this study, we compared the fat-saturated (FS) and non-FS turbo spin echo (TSE) magnetic resonance imaging knee sequences reconstructed conventionally (conventional-TSE) against a deep learning-based reconstruction of accelerated TSE (DL-TSE) scans. A total of 232 conventional-TSE and DL-TSE image pairs were acquired for comparison. For each consenting patient, one of the clinically acquired conventional-TSE proton density-weighted sequences in the sagittal or coronal planes (FS and non-FS), or in the axial plane (non-FS), was repeated using a research DL-TSE sequence. The DL-TSE reconstruction resulted in an image resolution that increased by at least 45% and scan times that were up to 52% faster compared to the conventional TSE. All images were acquired on a MAGNETOM Vida 3T scanner (Siemens Healthineers AG, Erlangen, Germany). The reporting radiologists, blinded to the acquisition time, were requested to qualitatively compare the DL-TSE against the conventional-TSE reconstructions. Despite having a faster acquisition time, the DL-TSE was rated to depict smaller structures better for 139/232 (60%) cases, equivalent for 72/232 (31%) cases and worse for 21/232 (9%) cases compared to the conventional-TSE. Overall, the radiologists preferred the DL-TSE reconstruction in 124/232 (53%) cases and stated no preference, implying equivalence, for 65/232 (28%) cases. DL-TSE reconstructions enabled faster acquisition times while enhancing spatial resolution and preserving the image contrast. From these results, the DL-TSE provided added or comparable clinical value and utility in less time. DL-TSE offers the opportunity to further reduce the overall examination time and improve patient comfort with no loss in diagnostic accuracy.

Evaluation of deep learning-based reconstruction late gadolinium enhancement images for identifying patients with clinically unrecognized myocardial infarction

BackgroundThe presence of infarction in patients with unrecognized myocardial infarction (UMI) is a critical feature in predicting adverse cardiac events. This study aimed to compare the detection rate of UMI using conventional and deep learning reconstruction (DLR)-based late gadolinium enhancement (LGEO and LGEDL, respectively) and evaluate optimal quantification parameters to enhance diagnosis and management of suspected patients with UMI.MethodsThis prospective study included 98 patients (68 men; mean age: 55.8 ± 8.1 years) with suspected UMI treated at our hospital from April 2022 to August 2023. LGEO and LGEDL images were obtained using conventional and commercially available inline DLR algorithms. The myocardial signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and percentage of enhanced area (Parea) employing the signal threshold versus reference mean (STRM) approach, which correlates the signal intensity (SI) within areas of interest with the average SI of normal regions, were analyzed. Analysis was performed using the standard deviation (SD) threshold approach (2SD–5SD) and full width at half maximum (FWHM) method. The diagnostic efficacies based on LGEDL and LGEO images were calculated.ResultsThe SNRDL and CNRDL were two times better than the SNRO and CNRO, respectively (P < 0.05). Parea−DL was elevated compared to Parea−O using the threshold methods (P < 0.05); however, no intergroup difference was found based on the FWHM method (P > 0.05). The Parea−DL and Parea−O also differed except between the 2SD and 3SD and the 4SD/5SD and FWHM methods (P < 0.05). The receiver operating characteristic curve analysis revealed that each SD method exhibited good diagnostic efficacy for detecting UMI, with the Parea−DL having the best diagnostic efficacy based on the 5SD method (P < 0.05). Overall, the LGEDL images had better image quality. Strong diagnostic efficacy for UMI identification was achieved when the STRM was ≥ 4SD and ≥ 3SD for the LGEDL and LGEO, respectively.ConclusionsSTRM selection for LGEDL magnetic resonance images helps improve clinical decision-making in patients with UMI. This study underscored the importance of STRM selection for analyzing LGEDL images to enhance diagnostic accuracy and clinical decision-making for patients with UMI, further providing better cardiovascular care.

Deep learning-based spectroscopic single-molecule localization microscopy.

Spectroscopic single-molecule localization microscopy (sSMLM) takes advantage of nanoscopy and spectroscopy, enabling sub-10nm resolution as well as simultaneous multicolor imaging of multi-labeled samples. Reconstruction of raw sSMLM data using deep learning is a promising approach for visualizing the subcellular structures at the nanoscale. Develop a novel computational approach leveraging deep learning to reconstruct both label-free and fluorescence-labeled sSMLM imaging data. We developed a two-network-model based deep learning algorithm, termed DsSMLM, to reconstruct sSMLM data. The effectiveness of DsSMLM was assessed by conducting imaging experiments on diverse samples, including label-free single-stranded DNA (ssDNA) fiber, fluorescence-labeled histone markers on COS-7 and U2OS cells, and simultaneous multicolor imaging of synthetic DNA origami nanoruler. For label-free imaging, a spatial resolution of 6.22nm was achieved on ssDNA fiber; for fluorescence-labeled imaging, DsSMLM revealed the distribution of chromatin-rich and chromatin-poor regions defined by histone markers on the cell nucleus and also offered simultaneous multicolor imaging of nanoruler samples, distinguishing two dyes labeled in three emitting points with a separation distance of 40nm. With DsSMLM, we observed enhanced spectral profiles with 8.8% higher localization detection for single-color imaging and up to 5.05% higher localization detection for simultaneous two-color imaging. We demonstrate the feasibility of deep learning-based reconstruction for sSMLM imaging applicable to label-free and fluorescence-labeled sSMLM imaging data. We anticipate our technique will be a valuable tool for high-quality super-resolution imaging for a deeper understanding of DNA molecules' photophysics and will facilitate the investigation of multiple nanoscopic cellular structures and their interactions.

Effectiveness of deep learning-based reconstruction for improvement of image quality and liver tumor detectability in the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging.

To evaluate the effectiveness of deep learning-based reconstruction (DLR) in improving image quality and tumor detectability of isovoxel high-resolution breath-hold fat-suppressed T1-weighted imaging (HR-BH-FS-T1WI) in the hepatobiliary phase (HBP) of Gadoxetic acid-enhanced magnetic resonance imaging (Gd-EOB-MRI). This retrospective evaluated 42 patients with 98 liver tumors who underwent Gd-EOB-MRI between March 2023 and May 2023 using three techniques based on HBP imaging: isovoxel HR-BH-FS-T1WI reconstructed (1) with DLR (BH-DLR +) and (2) without DLR (BH-DLR -) and (3) HR-FS-T1WI scanned with a free-breathing technique using a navigator-echo-triggered technique and DLR (Navi-DLR +). The three techniques were qualitatively and quantitatively compared by the Friedman test and the Bonferroni post-hoc test. Tumor detectability was compared using the McNemar test. BH-DLR + (3.85, average score of two radiologists) showed significantly better qualitative scores for image noise than BH-DLR - (2.84) and Navi-DLR + (3.37) (p < 0.0167), and Navi-DLR + showed significantly better scores than BH-DLR - (p < 0.0167). BH-DLR + (3.77) and BH-DLR - (3.77) showed significantly better qualitative scores for respiratory motion artifact than Navi-DLR + (2.75) (p < 0.0167), but there was no significant difference in scores between BH-DLR + and BH-DLR - (p > 0.0167). BH-DLR + (0.32) and Navi-DLR + (0.33) showed significantly higher lesion-to-nonlesion CR than BH-DLR - (0.29) (p < 0.0167), but there was no significant difference in lesion-to-nonlesion CR between BH-DLR + and Navi-DLR + (p > 0.0167). BH-DLR + (89.8%) showed significantly better tumor detectability than BH-DLR - (76.0%) and Navi-DLR + (77.6%) (p < 0.05). The use of DLR for isovoxel HR-BH-FS-T1WI was effective in improving image quality and tumor detectability.

Improved stent sharpness evaluation with super-resolution deep learning reconstruction in coronary CT angiography.

This study aimed to assess the impact of super-resolution deep learning reconstruction (SR-DLR) on coronary CT angiography (CCTA) image quality and blooming artifacts from coronary artery stents in comparison to conventional methods, including hybrid iterative reconstruction (HIR) and deep learning-based reconstruction (DLR). A retrospective analysis included 66 CCTA patients from July to November 2022. Major coronary arteries were evaluated for image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Stent sharpness was quantified using 10%-90% edge rise slope (ERS) and 10%-90% edge rise distance (ERD). Qualitative analysis employed a 5-point scoring system to assess overall image quality, image noise, vessel wall, and stent structure. SR-DLR demonstrated significantly lower image noise compared to HIR and DLR. SNR and CNR were notably higher in SR-DLR. Stent ERS was significantly improved in SR-DLR, with mean ERD values of 0.70 ± 0.20 mm for SR-DLR, 1.13 ± 0.28 mm for HIR, and 0.85 ± 0.26 mm for DLR. Qualitatively, SR-DLR scored higher in all categories. SR-DLR produces images with lower image noise, leading to improved overall image quality, compared with HIR and DLR. SR-DLR is a valuable image reconstruction algorithm for enhancing the spatial resolution and sharpness of coronary artery stents without being constrained by hardware limitations. The overall image quality was significantly higher in SR-DLR, resulting in sharper coronary artery stents compared to HIR and DLR.

Radiation dose reduction and image quality improvement with ultra-high resolution temporal bone CT using deep learning-based reconstruction: An anatomical study

PurposeThe purpose of this study was to evaluate the achievable radiation dose reduction of an ultra-high resolution computed tomography (UHR-CT) scanner using deep learning reconstruction (DLR) while maintaining temporal bone image quality equal to or better than high-resolution CT (HR-CT). Materials and methodsUHR-CT acquisitions were performed with variable tube voltages and currents at eight different dose levels (volumic CT dose index [CTDIvol] range: 4.6–79 mGy), 10242 matrix, and 0.25 mm slice thickness and reconstructed using DLR and hybrid iterative reconstruction (HIR) algorithms. HR-CT images were acquired using a standard protocol (120 kV/220 mAs; CTDI vol, 54.2 mGy, 5122 matrix, and 0.5 mm slice thickness). Two radiologists rated the image quality of seven structures using a five point confidence scale on six cadaveric temporal bone CTs. A global image quality score was obtained for each CT protocol by summing the image quality scores of all structures. ResultsWith DLR, UHR-CT at 120 kV/220 mAs (CTDIvol, 50.9 mGy) and 140 kV/220 mAs (CTDIvol, 79 mGy) received the highest global image quality scores (4.88 ± 0.32 [standard deviation (SD)] [range: 4–5] and 4.85 ± 0.35 [range: 4–5], respectively; P = 0.31), while HR-CT at 120 kV/220 mAs and UHR-CT at 120 kV/20 mAs received the lowest (i.e., 3.14 ± 0.75 [SD] [range: 2–5] and 2.97 ± 0.86 [SD] [range: 1–5], respectively; P = 0.14). All the DLR protocols had better image quality scores than HR-CT with HIR. ConclusionUHR-CT with DLR can be performed with up to a tenfold reduction in radiation dose compared to HR-CT with HIR while maintaining or improving image quality.

Advanced deep learning-based image reconstruction in lumbar spine MRI at 0.55 T – Effects on image quality and acquisition time in comparison to conventional deep learning-based reconstruction

ObjectivesTo evaluate an optimized deep leaning-based image post-processing technique in lumbar spine MRI at 0.55 T in terms of image quality and image acquisition time. Materials and methodsLumbar spine imaging was conducted on 18 patients using a 0.55 T MRI scanner, employing conventional (CDLR) and advanced (ADLR) deep learning-based post-processing techniques. Two musculoskeletal radiologists visually evaluated the images using a 5-point Likert scale to assess image quality and resolution. Quantitative assessment in terms of signal intensities (SI) and contrast ratios was performed by region of interest measurements in different body-tissues (vertebral bone, intervertebral disc, spinal cord, cerebrospinal fluid and autochthonous back muscles) to investigate differences between CDLR and ADLR sequences. ResultsThe images processed with the advanced technique (ADLR) were rated superior to the conventional technique (CDLR) in terms of signal/contrast, resolution, and assessability of the spinal canal and neural foramen. The interrater agreement was moderate for signal/contrast (ICC = 0.68) and good for resolution (ICC = 0.77), but moderate for spinal canal and neuroforaminal assessability (ICC = 0.55). Quantitative assessment showed a higher contrast ratio for fluid-sensitive sequences in the ADLR images. The use of ADLR reduced image acquisition time by 44.4%, from 14:22 min to 07:59 min. ConclusionsAdvanced deep learning-based image reconstruction algorithms improve the visually perceived image quality in lumbar spine imaging at 0.55 T while simultaneously allowing to substantially decrease image acquisition times. Clinical relevanceAdvanced deep learning-based image post-processing techniques (ADLR) in lumbar spine MRI at 0.55 T significantly improves image quality while reducing image acquisition time.