Application of deep learning-based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time.

OBJECTIVES: To compare the image quality and diagnostic performance of conventional motion-corrected periodically rotated overlapping parallel line with enhanced reconstruction (PROPELLER) MRI sequences with post-processed PROPELLER MRI sequences using deep learning-based (DL) reconstructions. METHODS: In this prospective study of 30 patients, conventional (19 min 18 s) and accelerated MRI sequences (7 min 16 s) using the PROPELLER technique were acquired. Accelerated sequences were post-processed using DL. The image quality and diagnostic confidence were qualitatively assessed by 2 readers using a 5-point Likert scale. Analysis of the pathological findings of cartilage, rotator cuff tendons and muscles, glenoid labrum and subacromial bursa was performed. Inter-reader agreement was calculated using Cohen's kappa statistic. Quantitative evaluation of image quality was measured using the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). RESULTS: Mean image quality and diagnostic confidence in evaluation of all shoulder structures were higher in DL sequences (p value = 0.01). Inter-reader agreement ranged between kappa values of 0.155 (assessment of the bursa) and 0.947 (assessment of the rotator cuff muscles). In 17 cases, thickening of the subacromial bursa of more than 2 mm was only visible in DL sequences. The pathologies of the other structures could be properly evaluated by conventional and DL sequences. Mean SNR (p value = 0.01) and CNR (p value = 0.02) were significantly higher for DL sequences. CONCLUSIONS: The accelerated PROPELLER sequences with DL post-processing showed superior image quality and higher diagnostic confidence compared to the conventional PROPELLER sequences. Subacromial bursa can be thoroughly assessed in DL sequences, while the other structures of the shoulder joint can be assessed in conventional and DL sequences with a good agreement between sequences. KEY POINTS: • MRI of the shoulder requires long scan times and can be hampered by motion artifacts. • Deep learning-based convolutional neural networks are used to reduce image noise and scan time while maintaining optimal image quality. The radial k-space acquisition technique (PROPELLER) can reduce the scan time and has potential to reduce motion artifacts. • DL sequences show a higher diagnostic confidence than conventional sequences and therefore are preferred for assessment of the subacromial bursa, while conventional and DL sequences show comparable performance in the evaluation of the shoulder joint.

A Feasibility Study on Deep Learning Reconstruction to Improve Image Quality With PROPELLER Acquisition in the Setting of T2-Weighted Gynecologic Pelvic Magnetic Resonance Imaging.

OBJECTIVES: Evaluate deep learning (DL) to improve the image quality of the PROPELLER (Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction technique) for 3 T magnetic resonance imaging of the female pelvis. METHODS: Three radiologists prospectively and independently compared non-DL and DL PROPELLER sequences from 20 patients with a history of gynecologic malignancy. Sequences with different noise reduction factors (DL 25%, DL 50%, and DL 75%) were blindly reviewed and scored based on artifacts, noise, relative sharpness, and overall image quality. The generalized estimating equation method was used to assess the effect of methods on the Likert scales. Quantitatively, the contrast-to-noise ratio and signal-to-noise ratio (SNR) of the iliac muscle were calculated, and pairwise comparisons were performed based on a linear mixed model. P values were adjusted using the Dunnett method. Interobserver agreement was assessed using the κ statistic. P value was considered statistically significant at less than 0.05. RESULTS: Qualitatively, DL 50 and DL 75 were ranked as the best sequences in 86% of cases. Images generated by the DL method were significantly better than non-DL images ( P < 0.0001). Iliacus muscle SNR on DL 50 and DL 75 was significantly better than non-DL images ( P < 0.0001). There was no difference in contrast-to-noise ratio between the DL and non-DL techniques in the iliac muscle. There was a high percent agreement (97.1%) in terms of DL sequences' superior image quality (97.1%) and sharpness (100%) relative to non-DL images. CONCLUSION: The utilization of DL reconstruction improves the image quality of PROPELLER sequences with improved SNR quantitatively.

Comparison of deep learning-based reconstruction of PROPELLER Shoulder MRI with conventional reconstruction.

OBJECTIVE: To compare the image quality and agreement among conventional and accelerated periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI with both conventional reconstruction (CR) and deep learning-based reconstruction (DLR) methods for evaluation of shoulder. MATERIALS AND METHODS: We included patients who underwent conventional (acquisition time, 8 min) and accelerated (acquisition time, 4 min and 24 s; 45% reduction) PROPELLER shoulder MRI using both CR and DLR methods between February 2021 and February 2022 on a 3 T MRI system. Quantitative evaluation was performed by calculating the signal-to-noise ratio (SNR). Two musculoskeletal radiologists compared the image quality using conventional sequence with CR as the reference standard. Interobserver agreement between image sets for evaluating shoulder was analyzed using weighted/unweighted kappa statistics. RESULTS: Ninety-two patients with 100 shoulder MRI scans were included. Conventional sequence with DLR had the highest SNR (P < .001), followed by accelerated sequence with DLR, conventional sequence with CR, and accelerated sequence with CR. Comparison of image quality by both readers revealed that conventional sequence with DLR (P = .003 and P < .001) and accelerated sequence with DLR (P = .016 and P < .001) had better image quality than the conventional sequence with CR. Interobserver agreement was substantial to almost perfect for detecting shoulder abnormalities (κ = 0.600-0.884). Agreement between the image sets was substantial to almost perfect (κ = 0.691-1). CONCLUSION: Accelerated PROPELLER with DLR showed even better image quality than conventional PROPELLER with CR and interobserver agreement for shoulder pathologies comparable to that of conventional PROPELLER with CR, despite the shorter scan time.

Single-shot RARE with Dixon: Application to robust abdominal imaging with uniform fat and water separation at 3T.

PURPOSE: To develop a true single shot turbo spin echo (SShTSE) acquisition with Dixon for robust T2 -weighted abdominal imaging with uniform fat and water separation at 3T. METHODS: The in-phase (IP) and out-of-phase (OP) echoes for Dixon processing were acquired in the same repetition time of a SShTSE using partial echoes. A phase-preserved bi-directional homodyne reconstruction was developed to compensate the partial echo and the partial phase encoding of SShTSE. With IRB approval, the SShTSE-Dixon was compared against standard SShTSE, without and with fat suppression using spectral adiabatic inversion recovery (SPAIR) in 5 healthy volunteers and 5 patients. The SNR and contrast ratio (CR) of spleen to liver were compared among different acquisitions. RESULTS: The bi-directional homodyne reconstruction successfully minimized ringing artifacts because of partial acquisitions. SShTSE-Dixon achieved uniform fat suppression compared to SShTSE-SPAIR with fat suppression failures of 1/10 versus 10/10 in the axial plane and 0/5 versus 5/5 in the coronal plane, respectively. The SNRs of the liver (12.2 ± 4.9 vs. 11.7 ± 5.2; P = .76) and spleen (25.9 ± 11.6 vs. 23.7 ± 9.7; P = .14) were equivalent between fat-suppressed images (SShTSE-Dixon water-only and SShTSE-SPAIR). The SNRs of liver (14.4 ± 5.7 vs. 13.4 ± 5.0; P = .60) and spleen (26.5 ± 10.1 vs. 25.7 ± 8.5; P = .56) were equivalent between non-fat-suppressed images (SShTSE-Dixon IP and SShTSE). The CRs of spleen to liver were also similar between fat-suppressed images (2.6 ± 0.4 vs. 2.5 ± 0.5; P =.92) and non-fat-suppressed images (2.3 ± 0.6 vs. 2.2 ± 0.4; P =.84). CONCLUSION: SShTSE-Dixon generates robust abdominal T2 -weighted images at 3T with and without uniform fat suppression, along with perfectly co-registered fat-only images in a single acquisition.

Combining inhomogeneous magnetization transfer and multipoint Dixon acquisition: Potential utility and evaluation.

PURPOSE: The recently introduced inhomogeneous magnetization transfer (ihMT) method has predominantly been applied for imaging the central nervous system. Future applications of ihMT, such as in peripheral nerves and muscles, will involve imaging in the vicinity of adipose tissues. This work aims to systematically investigate the partial volume effect of fat on the ihMT signal and to propose an efficient fat-separation method that does not interfere with ihMT measurements. METHODS: First, the influence of fat on ihMT signal was studied using simulations. Next, the ihMT sequence was combined with a multi-echo Dixon acquisition for fat separation. The sequence was tested in 9 healthy volunteers using a 3T human scanner. The ihMT ratio (ihMTR) values were calculated in regions of interest in the brain and the spinal cord using standard acquisition (no fat saturation), water-only, in-phase, and out-of-phase reconstructions. The values obtained were compared with a standard fat suppression method, spectral presaturation with inversion recovery. RESULTS: Simulations showed variations in the ihMTR values in the presence of fat, depending on the TEs used. The IhMTR values in the brain and spinal cord derived from the water-only ihMT multi-echo Dixon images were in good agreement with values from the unsuppressed sequence. The ihMT-spectral presaturation with inversion recovery combination resulted in 24%-35% lower ihMTR values compared with the standard non-fat-suppressed acquisition. CONCLUSION: The presence of fat within a voxel affects the ihMTR calculations. The IhMT multi-echo Dixon method does not compromise the observable ihMT effect and can potentially be used to remove fat influence in ihMT.

EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network.

BACKGROUND: A large number of biological studies have shown that miRNAs are inextricably linked to many complex diseases. Studying the miRNA-disease associations could provide us a root cause understanding of the underlying pathogenesis in which promotes the progress of drug development. However, traditional biological experiments are very time-consuming and costly. Therefore, we come up with an efficient models to solve this challenge. RESULTS: In this work, we propose a deep learning model called EOESGC to predict potential miRNA-disease associations based on embedding of embedding and simplified convolutional network. Firstly, integrated disease similarity, integrated miRNA similarity, and miRNA-disease association network are used to construct a coupled heterogeneous graph, and the edges with low similarity are removed to simplify the graph structure and ensure the effectiveness of edges. Secondly, the Embedding of embedding model (EOE) is used to learn edge information in the coupled heterogeneous graph. The training rule of the model is that the associated nodes are close to each other and the unassociated nodes are far away from each other. Based on this rule, edge information learned is added into node embedding as supplementary information to enrich node information. Then, node embedding of EOE model training as a new feature of miRNA and disease, and information aggregation is performed by simplified graph convolution model, in which each level of convolution can aggregate multi-hop neighbor information. In this step, we only use the miRNA-disease association network to further simplify the graph structure, thus reducing the computational complexity. Finally, feature embeddings of both miRNA and disease are spliced into the MLP for prediction. On the EOESGC evaluation part, the AUC, AUPR, and F1-score of our model are 0.9658, 0.8543 and 0.8644 by 5-fold cross-validation respectively. Compared with the latest published models, our model shows better results. In addition, we predict the top 20 potential miRNAs for breast cancer and lung cancer, most of which are validated in the dbDEMC and HMDD3.2 databases. CONCLUSION: The comprehensive experimental results show that EOESGC can effectively identify the potential miRNA-disease associations.

PEIS: a novel approach of tumor purity estimation by identifying information sites through integrating signal based on DNA methylation data.

BACKGROUND: Tumor purity plays an important role in understanding the pathogenic mechanism of tumors. The purity of tumor samples is highly sensitive to tumor heterogeneity. Due to Intratumoral heterogeneity of genetic and epigenetic data, it is suitable to study the purity of tumors. Among them, there are many purity estimation methods based on copy number variation, gene expression and other data, while few use DNA methylation data and often based on selected information sites. Consequently, how to choose methylation sites as information sites has an important influence on the purity estimation results. At present, the selection of information sites was often based on the differentially methylated sites that only consider the mean signal, without considering other possible signals and the strong correlation among adjacent sites. RESULTS: Considering integrating multi-signals and strong correlation among adjacent sites, we propose an approach, PEIS, to estimate the purity of tumor samples by selecting informative differential methylation sites. Application to 12 publicly available tumor datasets, it is shown that PEIS provides accurate results in the estimation of tumor purity which has a high consistency with other existing methods. Also, through comparing the results of different information sites selection methods in the evaluation of tumor purity, it shows the PEIS is superior to other methods. CONCLUSIONS: A new method to estimate the purity of tumor samples is proposed. This approach integrates multi-signals of the CpG sites and the correlation between the sites. Experimental analysis shows that this method is in good agreement with other existing methods for estimating tumor purity.

Robust pCASL perfusion imaging using a 3D Cartesian acquisition with spiral profile reordering (CASPR).

PURPOSE: To improve the robustness of arterial spin-labeled measured perfusion using a novel Cartesian acquisition with spiral profile reordering (CASPR) 3D turbo spin echo (TSE) in the brain and kidneys. METHODS: The CASPR view ordering followed a pseudo-spiral trajectory on a Cartesian grid, by sampling the center of k-space at the beginning of each echo train of a segmented 3D TSE acquisition. With institutional review board approval and written informed consent, 14 normal subjects (9 brain and 5 kidneys) were scanned with pCASL perfusion imaging using 3D CASPR and compared against 3D linear TSE (brain and kidneys), the established 2D EPI and 3D gradient and spin echo perfusion (brain), and 2D single-shot turbo spin-echo perfusion (kidneys). The SNR and the quantitative perfusion values were compared among different acquisitions. RESULTS: 3D CASPR TSE achieved robust perfusion across all slices compared to 3D linear TSE in the brain and kidneys. Compared to 2D EPI, 3D CASPR TSE showed higher SNR across the brain (P < 0.01), and exhibited good agreement (36.4 ± 4.7 and 36.9 ± 5.3 mL/100 g/min with 2D EPI and 3D CASPR, respectively), and with 3D gradient and spin echo (27.9 ± 7.2 mL/100 g/min). Compared to a single slice 2D single-shot turbo spin-echo acquisition, 3D CASPR TSE achieved robust perfusion across the entire kidneys in similar scan time with comparable quantified perfusion values (154.1 ± 74.6 and 151.7 ± 70.6 mL/100 g/min with 2D single-shot turbo spin-echo and 3D CASPR, respectively). CONCLUSION: The CASPR view ordering with 3D TSE achieves robust arterial spin-labeled perfusion in the brain and kidneys because of the sampling of the center of k-space at the beginning of each echo train.

Z-spectrum appearance and interpretation in the presence of fat: Influence of acquisition parameters.

PURPOSE: Chemical exchange saturation transfer (CEST) MRI is increasingly evolving from brain to body applications. One of the known problems in the body imaging is the presence of strong lipid signals. Although their influence on the CEST effect is acknowledged, there was no study that focuses on the interplay among echo time, fat fraction, and Z-spectrum. This study strives to address these points, with the emphasis on the application in the breast. METHODS: Z-spectra were simulated in phase and out of phase of the main fat peak at -3.4 ppm, with the fat fraction varying from 0 to 100%. The magnetization transfer ratio asymmetry in two ranges, centering at the exchanging pool and at 3.5 ppm approximately opposite the nonexchanging fat pool, were calculated and were plotted against fat fraction. The results were verified in phantoms and in vivo. RESULTS: The results demonstrate the combined influence of fat fraction and echo time on the Z-spectrum for gradient echo based CEST acquisitions. The influence is straightforward in the in-phase images, but it is more complicated in the out-of-phase images, potentially leading to erroneous CEST contrast. CONCLUSIONS: This study provides a basis for understanding the origin and appearance of lipid artifacts in CEST imaging, and lays the foundation for their efficient removal. Magn Reson Med 79:2731-2737, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Whole-body MRI for metastatic cancer detection using T2 -weighted imaging with fat and fluid suppression.

PURPOSE: To develop a whole-body MRI technique at 3T with improved lesion conspicuity for metastatic cancer detection using fast, high-resolution and high SNR T2 -weighted (T2 W) imaging with simultaneous fat and fluid suppression. THEORY AND METHODS: The proposed dual-echo T2 -weighted acquisition for enhanced conspicuity of tumors (DETECT) acquires 4 images, in-phase (IP) and out-of-phase (OP) at a short and a long TE using single-shot turbo spin echo. The IP/OP images at the short and long TEs are reconstructed using the standard Dixon and shared-field-map Dixon reconstruction respectively, for robust fat-water separation. An adaptive complex subtraction between the 2 TE water-only images achieves fluid attenuation. DETECT imaging was optimized and evaluated in whole-body imaging of 5 healthy volunteers, and compared against diffusion-weighted imaging with background suppression (DWIBS) in 5 patients with known metastatic renal cell carcinoma. RESULTS: Robust fat-water separation and fluid attenuation were achieved using the shared-field-map Dixon reconstruction and adaptive complex subtraction, respectively. DETECT imaging technique generated co-registered T2 W images with and without fat suppression, heavily T2 W, and fat and fluid suppressed T2 W whole-body images in <7 min. Compared to DWIBS acquired in 17 min, the DETECT imaging achieved better detection and localization of lesions in patients with metastatic cancer. CONCLUSION: DETECT imaging technique generates T2 W images with high resolution, high SNR, minimal geometric distortions, and provides good lesion conspicuity with robust fat and fluid suppression in <7 min for whole-body imaging, demonstrating efficient and reliable metastatic cancer detection at 3T.

Frequency Offset Corrected Inversion Pulse for B0 and B1 Insensitive Fat Suppression at 3T: Application to MR Neurography of Brachial Plexus.

BACKGROUND: The 3D short tau inversion recovery (STIR) sequence is routinely used in clinical MRI to achieve robust fat suppression. However, the performance of the commonly used adiabatic inversion pulse, hyperbolic secant (HS), is compromised in challenging areas with increased B0 and B1 inhomogeneities, such as brachial plexus at 3T. PURPOSE: To demonstrate the frequency offset corrected inversion (FOCI) pulse as an efficient fat suppression STIR pulse with increased robustness to B0 and B1 inhomogeneities at 3T, compared to the HS pulse. STUDY TYPE: Prospective. SUBJECTS/PHANTOM: Initial evaluation was performed in phantoms and one healthy volunteer by varying the B1 field, while subsequent comparison was performed in three healthy volunteers and five patients without varying the B1 . FIELD STRENGTH/SEQUENCE: 3T; 3D TSE-STIR with HS and FOCI pulses. ASSESSMENT: Brachial plexus images were qualitatively evaluated by two musculoskeletal radiologists independently using a four-point grading scale for fat suppression, shading artifacts, and nerve visualization. STATISTICAL TEST: The Wilcoxon signed-rank test with P < 0.05 was considered statistically significant. RESULTS: Simulations and phantom experiments demonstrated broader bandwidth (2.5 kHz vs. 0.83 kHz, increased B0 robustness) at the same adiabatic threshold and lower adiabatic threshold (5 µT vs. 7 µT at 3.5 ppm, increased B1 robustness) at the same bandwidth with the FOCI pulse compared to the HS pulse With increased bandwidth, the FOCI pulse achieved robust fat suppression even at 50% of maximum B1 strength, while the HS pulse required >75% of maximum B1 strength. Compared to the standard 3D TSE-STIR with HS pulse, the FOCI pulse achieved uniform fat suppression (P < 0.05), better nerve visualization (P < 0.05), and minimal shading artifacts (P < 0.01) in brachial plexus at 3T. DATA CONCLUSION: The FOCI pulse has increased robustness to B0 and B1 inhomogeneities, compared to the HS pulse, and enables uniform fat suppression in brachial plexus at 3T. LEVEL OF EVIDENCE: 1 Techinical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;48:1104-1111.

MR Neurography of Brachial Plexus at 3.0 T with Robust Fat and Blood Suppression.

Purpose To develop and evaluate magnetic resonance (MR) neurography of the brachial plexus with robust fat and blood suppression for increased conspicuity of nerves at 3.0 T in clinically feasible acquisition times. Materials and Methods This prospective study was HIPAA compliant, with institutional review board approval and written informed consent. A low-refocusing-flip-angle three-dimensional (3D) turbo spin-echo (TSE) sequence was modified to acquire both in-phase and out-of-phase echoes, required for chemical shift (Dixon) reconstruction, in the same repetition by using partial echoes combined with modified homodyne reconstruction with phase preservation. This multiecho TSE modified Dixon (mDixon) sequence was optimized by using simulations and phantom studies and in three healthy volunteers. The sequence was tested in five healthy volunteers and was evaluated in 10 patients who had been referred for brachial plexopathy at 3.0 T. The images were evaluated against the current standard of care, images acquired with a 3D TSE short inversion time inversion recovery (STIR) sequence, qualitatively by using the Wilcoxon signed-rank test and quantitatively by using the Friedman two-way analysis of variance, with P < .05 considered to indicate a statistically significant difference. Results Multiecho TSE-mDixon involving partial-echo and homodyne reconstruction with phase preservation achieved uniform fat suppression in half the imaging time compared with multiacquisition TSE-mDixon. Compared with 3D TSE STIR, fat suppression, venous suppression, and nerve visualization were significantly improved (P < .05), while arterial suppression was better but not significantly so (P = .06), with increased apparent signal-to-noise ratio in the dorsal nerve root ganglion and C6 nerve (P < .001) with the multiecho TSE-mDixon sequence. Conclusion The multiecho 3D TSE-mDixon sequence provides robust fat and blood suppression, resulting in increased conspicuity of the nerves, in clinically feasible imaging times and can be used for MR neurography of the brachial plexus at 3.0 T. © RSNA, 2016 Online supplemental material is available for this article.

An efficient weighted tag SNP-set analytical method in genome-wide association studies.

BACKGROUND: Single-nucleotide polymorphism (SNP)-set analysis in Genome-wide association studies (GWAS) has emerged as a research hotspot for identifying genetic variants associated with disease susceptibility. But most existing methods of SNP-set analysis are affected by the quality of SNP-set, and poor quality of SNP-set can lead to low power in GWAS. RESULTS: In this research, we propose an efficient weighted tag-SNP-set analytical method to detect the disease associations. In our method, we first design a fast algorithm to select a subset of SNPs (called tag SNP-set) from a given original SNP-set based on the linkage disequilibrium (LD) between SNPs, then assign a proper weight to each of the selected tag SNP respectively and test the joint effect of these weighted tag SNPs. The intensive simulation results show that the power of weighted tag SNP-set-based test is much higher than that of weighted original SNP-set-based test and that of un-weighted tag SNP-set-based test. We also compare the powers of the weighted tag SNP-set-based test based on four types of tag SNP-sets. The simulation results indicate the method of selecting tag SNP-set impacts the power greatly and the power of our proposed method is the highest. CONCLUSIONS: From the analysis of simulated replicated data sets, we came to a conclusion that weighted tag SNP-set-based test is a powerful SNP-set test in GWAS. We also designed a faster algorithm of selecting tag SNPs which include most of information of original SNP-set, and a better weighted function which can describe the status of each tag SNP in GWAS.

Clinical efficacy of motion-insensitive imaging technique with deep learning reconstruction to improve image quality in cervical spine MR imaging.

OBJECTIVE: To demonstrate that a T2 periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) technique using deep learning reconstruction (DLR) will provide better image quality and decrease image noise. METHODS: From December 2020 to March 2021, 35 patients examined cervical spine MRI were included in this study. Four sets of images including fast spin echo (FSE), original PROPELLER, PROPELLER DLR50%, and DLR75% were quantitatively and qualitatively reviewed. We calculated the signal-to-noise ratio (SNR) of the spinal cord and sternocleidomastoid (SCM) muscle and the contrast-to-noise ratio (CNR) of the spinal cord by applying region-of-interest at the spinal cord, SCM muscle, and background air. We evaluated image noise with regard to the spinal cord, SCM, and back muscles at each level from C2-3 to C6-7 in the 4 sets. RESULTS: At all disc levels, the mean SNR values for the spinal cord and SCM muscles were significantly higher in PROPELLER DLR50% and DLR75% compared to FSE and original PROPELLER images (P < .0083). The mean CNR values of the spinal cord were significantly higher in PROPELLER DLR50% and DLR75% compared to FSE at the C3-4 and 4-5 levels and PROPELLER DLR75% compared to FSE at the C6-7 level (P < .0083). Qualitative analysis of image noise on the spinal cord, SCM, and back muscles showed that PROPELLER DLR50% and PROPELLER DLR75% images showed a significant denoising effect compared to the FSE and original PROPELLER images. CONCLUSION: The combination of PROPELLER and DLR improved image quality with a high SNR and CNR and reduced noise. ADVANCES IN KNOWLEDGE: Motion-insensitive imaging technique (PROPELLER) increased the image quality compared to conventional FSE images. PROPELLER technique with a DLR reduced image noise and improved image quality.

Performance evaluation of a modular detector unit for X-ray computed tomography.

A research prototype CT scanner is currently under development in our lab. One of the key components in this project is the CT detector. This paper describes the design and performance evaluation of the modular CT detector unit for our proposed scanner. It consists of a Photodiode Array Assembly which captures irradiating X-ray photons and converts the energy into electrical current, and a mini Data Acquisition System which performs current integration and converts the analog signal into digital samples. The detector unit can be easily tiled together to form a CT detector. Experiments were conducted to characterize the detector performance both at the single unit level and system level. The noise level, linearity and uniformity of the proposed detector unit were reported and initial imaging studies were also presented which demonstrated the potential application of the proposed detector unit in actual CT scanners.

Ultra-High-Resolution T2-Weighted PROPELLER MRI of the Rectum With Deep Learning Reconstruction: Assessment of Image Quality and Diagnostic Performance.

OBJECTIVE: The aim of this study was to evaluate the impact of ultra-high-resolution acquisition and deep learning reconstruction (DLR) on the image quality and diagnostic performance of T2-weighted periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) imaging of the rectum. MATERIALS AND METHODS: This prospective study included 34 patients who underwent magnetic resonance imaging (MRI) for initial staging or restaging of rectal tumors. The following 4 types of oblique axial PROPELLER images perpendicular to the tumor were obtained: a standard 3-mm slice thickness with conventional reconstruction (3-CR) and DLR (3-DLR), and 1.2-mm slice thickness with CR (1.2-CR) and DLR (1.2-DLR). Three radiologists independently evaluated the image quality and tumor extent by using a 5-point scoring system. Diagnostic accuracy was evaluated in 22 patients with rectal cancer who underwent surgery after MRI without additional neoadjuvant therapy (median interval between MRI and surgery, 22 days). The signal-to-noise ratio and tissue contrast were measured on the 4 types of PROPELLER imaging. RESULTS: 1.2-DLR imaging showed the best sharpness, overall image quality, and rectal and lesion conspicuity for all readers (P < 0.01). Of the assigned scores for tumor extent, extramural venous invasion (EMVI) scores showed moderate agreement across the 4 types of PROPELLER sequences in all readers (intraclass correlation coefficient, 0.60-0.71). Compared with 3-CR imaging, the number of cases with MRI-detected extramural tumor spread was significantly higher with 1.2-DLR imaging (19.0 ± 2.9 vs 23.3 ± 0.9, P = 0.03), and the number of cases with MRI-detected EMVI was significantly increased with 1.2-CR, 3-DLR, and 1.2-DLR imaging (8.0 ± 0.0 vs 9.7 ± 0.5, 11.0 ± 2.2, and 12.3 ± 1.7, respectively; P = 0.02). For the diagnosis of histopathologic extramural tumor spread, 3-CR and 1.2-CR had significantly higher specificity than 3-DLR and 1.2-DLR imaging (0.75 and 0.78 vs 0.64 and 0.58, respectively; P = 0.02), and only 1.2-CR had significantly higher accuracy than 3-CR imaging (0.83 vs 0.79, P = 0.01). The accuracy of MRI-detected EMVI with reference to pathological EMVI was significantly lower for 3-CR and 3-DLR compared with 1.2-CR (0.77 and 0.74 vs 0.85, respectively; P < 0.01), and was not significantly different between 1.2-CR and 1.2-DLR (0.85 vs 0.80). Using any pathological venous invasion as the reference standard, the accuracy of MRI-detected EMVI was significantly the highest with 1.2-DLR, followed by 1.2-CR, 3-CR, and 3-DLR (0.71 vs 0.67 vs 0.59 vs 0.56, respectively; P < 0.01). The signal-to-noise ratio was significantly highest with 3-DLR imaging (P < 0.05). There were no significant differences in tumor-to-muscle contrast between the 4 types of PROPELLER imaging. CONCLUSIONS: Ultra-high-resolution PROPELLER T2-weighted imaging of the rectum combined with DLR improved image quality, increased the number of cases with MRI-detected extramural tumor spread and EMVI, but did not improve diagnostic accuracy with respect to pathology in rectal cancer, possibly because of false-positive MRI findings or false-negative pathologic findings.

IL-2K35C-moFA, a Long-Acting Engineered Cytokine with Decreased Interleukin 2 Receptor α Binding, Improved the Cellular Selectivity Profile and Antitumor Efficacy in a Mouse Tumor Model.

Human interleukin 2 (IL-2) has shown impressive results as a therapeutic agent for cancer. However, IL-2-based cancer therapy is limited by strong Treg amplification owing to its high binding affinity to IL-2 receptor α (IL-2Rα) and its short half-life owing to its small molecular size. In this study, we solved these problems using a covalent modification strategy of the IL-2 variant, i.e., substituting cysteine (C) for lysine (K) at position 35, using octadecanedicarboxylic acid through maleimide chemistry, creating IL-2K35C-moFA. IL-2K35C-moFA was equipotent to human IL-2 wild type (IL-2WT) in activating tumor-killing CD8+ memory effector T cells (CD8+ T) and NK cells bearing the intermediate affinity IL-2 receptors, and less potent than IL-2WT on CTLL-2 cells bearing the high-affinity IL-2 receptors. Moreover, it was shown to support the preferential activation of IL-2 receptor ß (IL-2Rß) over IL-2Rα because of the mutation and fatty acid conjugation. In a B16F10 murine tumor model, IL-2K35C-moFA showed efficacy as a single dose and provided durable immunity for 1 week. Our results support the further evaluation of IL-2K35C-moFA as a novel cancer immunotherapy.

Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging.

INTRODUCTION: Magnetic resonance imaging (MRI) has played an increasingly major role in the evaluation of patients with prostate cancer, although prostate MRI presents several technical challenges. Newer techniques, such as deep learning (DL), have been applied to medical imaging, leading to improvements in image quality. Our goal is to evaluate the performance of a new deep learning-based reconstruction method, "DLR" in improving image quality and mitigating artifacts, which is now commercially available as AIRTM Recon DL (GE Healthcare, Waukesha, WI). We hypothesize that applying DLR to the T2WI images of the prostate provides improved image quality and reduced artifacts. METHODS: This study included 31 patients with a history of prostate cancer that had a multiparametric MRI of the prostate with an endorectal coil (ERC) at 1.5 T or 3.0 T. Four series of T2-weighted images were generated in total: one set with the ERC signal turned on (ERC) and another set with the ERC signal turned off (Non-ERC). Each of these sets then reconstructed using two different reconstruction methods: conventional reconstruction (Conv) and DL Recon (DLR): ERCDLR, ERCConv, Non-ERCDLR, and Non-ERCConv. Three radiologists independently reviewed and scored the four sets of images for (i) image quality, (ii) artifacts, and (iii) visualization of anatomical landmarks and tumor. RESULTS: The Non-ERCDLR scored as the best series for (i) overall image quality (p < 0.001), (ii) reduced artifacts (p < 0.001), and (iii) visualization of anatomical landmarks and tumor. CONCLUSION: Prostate imaging without the use of an endorectal coil could benefit from deep learning reconstruction as demonstrated with T2-weighted imaging MRI evaluations of the prostate.

Assessment of Early Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer Using Amide Proton Transfer-weighted Chemical Exchange Saturation Transfer MRI: A Pilot Study.

Purpose To determine if amide proton transfer-weighted chemical exchange saturation transfer (APTW CEST) MRI is useful in the early assessment of treatment response in persons with triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, a total of 51 participants (mean age, 51 years [range, 26-79 years]) with TNBC were included who underwent APTW CEST MRI with 0.9- and 2.0-µT saturation power performed at baseline, after two cycles (C2), and after four cycles (C4) of neoadjuvant systemic therapy (NAST). Imaging was performed between January 31, 2019, and November 11, 2019, and was a part of a clinical trial (registry number NCT02744053). CEST MR images were analyzed using two methods-magnetic transfer ratio asymmetry (MTRasym) and Lorentzian line shape fitting. The APTW CEST signals at baseline, C2, and C4 were compared for 51 participants to evaluate the saturation power levels and analysis methods. The APTW CEST signals and their changes during NAST were then compared for the 26 participants with pathology reports for treatment response assessment. Results A significant APTW CEST signal decrease was observed during NAST when acquisition at 0.9-µT saturation power was paired with Lorentzian line shape fitting analysis and when the acquisition at 2.0 µT was paired with MTRasym analysis. Using 0.9-µT saturation power and Lorentzian line shape fitting, the APTW CEST signal at C2 was significantly different from baseline in participants with pathologic complete response (pCR) (3.19% vs 2.43%; P = .03) but not with non-pCR (2.76% vs 2.50%; P > .05). The APTW CEST signal change was not significant between pCR and non-pCR at all time points. Conclusion Quantitative APTW CEST MRI depended on optimizing acquisition saturation powers and analysis methods. APTW CEST MRI monitored treatment effects but did not differentiate participants with TNBC who had pCR from those with non-pCR. © RSNA, 2021 Clinical trial registration no. NCT02744053 Supplemental material is available for this article.Keywords Molecular Imaging-Cancer, Molecular Imaging-Clinical Translation, MR-Imaging, Breast, Technical Aspects, Tumor Response, Technology Assessment.