T2-weighted Turbo Spin-echo Images Research Articles

Diagnostic performance of magnetic resonance imaging in preoperative local staging of rectal cancer after neoadjuvant chemoradiotherapy

This paper aims to investigate the diagnostic performance of magnetic resonance imaging (MRI) in predicting the pathologic stage of locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (CRT) and the role of MRI in selecting patients with a pathologic complete response (ypCR). Restaging MRI (yMRI) examinations of 136 patients with LARC treated with neoadjuvant CRT followed by surgery were retrospectively analyzed by two radiologists. All examinations were performed on a 1.5 Tesla MRI machine with a pelvic phased-array coil. T2-weighted turbo spin-echo images and diffusion-weighted imaging were obtained. Histopathologic reports of the surgical specimens were the reference standard. The accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV) of yMRI in predicting the pathologic T-stage (ypT), N-stage, and ypCR were calculated. The inter-observer agreement was evaluated using kappa statistics. The yMRI results showed 67% accuracy, 59% sensitivity, 80% specificity, 81% PPV, and 56% NPV in identifying ypT (ypT0-2 versus ypT3-4). In predicting the nodal status, the yMRI results revealed 63% accuracy, 60% sensitivity, 65% specificity, 47% PPV, and 75% NPV. In predicting ypCR, the yMRI results showed 84% accuracy, 20% sensitivity, 92% specificity, 23% PPV, and 90% NPV. The kappa statistics revealed substantial agreement between the two radiologists. Utilization of yMRI showed high specificity and PPV in predicting the tumor stage and high NPV in predicting the nodal stage; in addition, yMRI revealed moderate accuracy in the T and N classifications, mainly due to underestimating the tumor stage and overestimating the nodal status. Finally, yMRI revealed high specificity and NPV but low sensitivity in predicting the complete response.

Effectiveness and Accuracy of MRI-Ultrasound Fusion Targeted Biopsy Based on PI-RADS v2.1 Category in Transition/Peripheral Zone of the Prostate.

MRI-ultrasound fusion targeted biopsy (MRI-TBx) improves the clinically significant prostate cancer (csPCa) detection with fewer cores. However, whether systematic biopsy-guided by transrectal ultrasound (TRUS-SBx) can be omitted when undergoing MRI-TBx in transition zone (TZ) and peripheral zone (PZ) remains unclear. To assess the performance and effectiveness of MRI-TBx based on PI-RADS v2.1 for csPCa diagnosis in TZ and PZ, respectively. Retrospective. A total of 309 selected cases (median age 70 years) with 356 lesions who underwent both MRI-TBx and TRUS-SBx were enrolled. A 3.0 T, multiparametric MRI (mp-MRI) including T2-weighted turbo-spin echo imaging (T2WI), diffusion-weighted spin-echo echo planar imaging (DWI), dynamic contrast-enhanced MRI with time-resolved T1-weighted imaging (DCE). Mp-MRI was assessed by two radiologists using PI-RADS v2.1. The csPCa detection rates provided by MRI-TBx, TRUS-SBx and combined biopsy in TZ and PZ were calculated, respectively. McNemar test was used to compare the csPCa detection rates in TZ and PZ, respectively. The frequencies and distribution of all detected prostate cancers by different biopsy methods were also compared. P < 0.05 was considered statistically significant. Among 356 lesions in 309 patients, 208 (68 in TZ, 140 in PZ) were pathologically confirmed as csPCa. In TZ, there were significant differences for csPCa detection with PI-RADS 3 between combined biopsy and TRUS-SBx (23.5% vs. 15.3%), MRI-TBx (23.5% vs. 16.3%), respectively. MRI-TBx detected 23% (19/83) cases missed by TRUS-SBx in which 68% (13/19) were csPCa. In PZ, there were no statistical differences between MRI-TBx and combined biopsy with PI-RADS 3-5 (P=0.21, 0.25, 0.07, respectively). In 9% (14/152) cases only detected by MRI-TBx, 86% (12/14) were clinically significant. Five percent (7/152) of cases only detected by TRUS-SBx were completely nonclinically significant. MRI-TBx played a positive role on csPCa diagnosis in TZ, but combined biopsy might be the best choice especially in the subgroup PI-RADS 3. In PZ, MRI-TBx had an advantage over TRUS-SBx for csPCa detection. 2. Stage 2.

Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging.

Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for clinical trials. MRI can noninvasively depict morphological and metabolic characteristics of H3 K27M mutant DMG. This study aimed to develop a deep learning (DL) approach to noninvasively predict H3 K27M mutation in DMG using T2-weighted images. Retrospective and prospective. For diffuse midline brain gliomas, 341 patients from Center-1 (27 ± 19 years, 184 males), 42 patients from Center-2 (33 ± 19 years, 27 males) and 35 patients (37 ± 18 years, 24 males). For diffuse spinal cord gliomas, 133 patients from Center-1 (30 ± 15 years, 80 males). 5T and 3T, T2-weighted turbo spin echo imaging. Conventional radiological features were independently reviewed by two neuroradiologists. H3 K27M status was determined by histopathological examination. The Dice coefficient was used to evaluate segmentation performance. Classification performance was evaluated using accuracy, sensitivity, specificity, and area under the curve. Pearson's Chi-squared test, Fisher's exact test, two-sample Student's t-test and Mann-Whitney U test. A two-sided P value <0.05 was considered statistically significant. In the testing cohort, Dice coefficients of tumor segmentation using DL were 0.87 for diffuse midline brain and 0.81 for spinal cord gliomas. In the internal prospective testing dataset, the predictive accuracies, sensitivities, and specificities of H3 K27M mutation status were 92.1%, 98.2%, 82.9% in diffuse midline brain gliomas and 85.4%, 88.9%, 82.6% in spinal cord gliomas. Furthermore, this study showed that the performance generalizes to external institutions, with predictive accuracies of 85.7%-90.5%, sensitivities of 90.9%-96.0%, and specificities of 82.4%-83.3%. In this study, an automatic DL framework was developed and validated for accurately predicting H3 K27M mutation using T2-weighted images, which could contribute to the noninvasive determination of H3 K27M status for clinical decision-making. 2 Technical Efficacy: Stage 2.

Paraspinal muscles atrophy on both sides and at multiple levels after unilateral lumbar partial discectomy.

To identify the changes in cross-sectional areas (CSAs) and fatty infiltration of both sides of the paravertebral muscles and their associations with prognostic factors in patients who underwent unilateral lumbar discectomy. We retrospectively reviewed 27 patients who underwent magnetic resonance imaging before and after 1- or 2-level lumbar discectomy. The CSAs and functional cross-sectional areas of the paraspinal muscles were bilaterally measured from L1 to L2 to L5 to S1 based on T2-weighted axial images. These parameters were compared pre-and postoperatively. CSAs and functional cross-sectional areas decreased also in non-operative, non-surgical levels, not only in operated levels after discectomy. In the correlation analysis, the CSA of psoas major muscle at L1 to L2 was significantly decreased in patients with lower preoperative lordosis (r = 0.598, P = .040). The postoperative CSA of psoas major muscle at L4 to L5 was lower in those with the higher Pfirrmann grade (r = -0.590, P = .002); however, the CSA of quadratus lumborum muscle at L1 to L2 showed the opposite result (r = 0.526, P = .036). Similar results were also observed in the partial correlation adjusted for age and postoperative duration. Patients who underwent discectomy experienced overall paraspinal muscle atrophy in the lumbar region, including surgical and non-surgical sites. Such atrophic changes emphasized the need for core strengthening and lumbar rehabilitation from the early period after partial discectomy.

High-Resolution 7T MR Imaging of the Trochlear Nerve.

The trochlear nerve has traditionally been difficult to identify on MR imaging. The advent of 7T MR imaging promises to greatly benefit visualization of small structures due to gains in the signal-to-noise ratio allowing improved spatial resolution. We investigated the utility of a clinically feasible ultra-high-resolution 7T MR imaging protocol for identification of the trochlear nerve, as well as assessment of normal trochlear nerve anatomy. Coronal high-resolution 2D T2-weighted TSE images used in a 7T epilepsy protocol of 50 subjects at our institution were reviewed by 2 independent radiologists for visualization of the trochlear nerve at the nerve origin and cisternal, tentorial, and cavernous segments. The frequency of nerve visibility within these segments and their anatomy were documented, and disagreements were resolved by joint review. Of the 100 nerves reviewed in 50 subjects, at least 2 segments of the trochlear nerve from the brainstem to the cavernous sinus were identified in 100% of cases. The origins from the brainstem and cisternal segment were visible in 65% and 93% of nerves, respectively. The trochlear nerve was identified at the trochlear groove in 100% of cases and in the posterior wall of the cavernous sinus in 74% of cases. Coronal high-resolution 2D TSE at 7T reliably identified the trochlear nerve throughout its course and is a promising tool for imaging patients with suspected trochlear nerve pathology.

Thin-Slice Prostate MRI Enabled by Deep Learning Image Reconstruction.

Thin-slice prostate MRI might be beneficial for prostate cancer diagnostics. However, prolongation of acquisition time is a major drawback of thin-slice imaging. Therefore, the purpose of this study was to investigate the impact of a thin-slice deep learning accelerated T2-weighted (w) TSE imaging sequence (T2DLR) of the prostate as compared to conventional T2w TSE imaging (T2S). Thirty patients were included in this prospective study at one university center after obtaining written informed consent. T2S (3 mm slice thickness) was acquired first in three orthogonal planes followed by thin-slice T2DLR (2 mm slice thickness) in axial plane. Acquisition time of axial conventional T2S was 4:12 min compared to 4:37 min for T2DLR. Imaging datasets were evaluated by two radiologists using a Likert-scale ranging from 1-4, with 4 being the best regarding the following parameters: sharpness, lesion detectability, artifacts, overall image quality, and diagnostic confidence. Furthermore, preference of T2S versus T2DLR was evaluated. The mean patient age was 68 ± 8 years. Sharpness of images and lesion detectability were rated better in T2DLR with a median of 4 versus a median of 3 in T2S (p < 0.001 for both readers). Image noise was evaluated to be significantly worse in T2DLR as compared to T2S (p < 0.001 and p = 0.021, respectively). Overall image quality was also evaluated to be superior in T2DLR versus T2S with a median of 4 versus 3 (p < 0.001 for both readers). Both readers chose T2DLR in 29 cases as their preference. Thin-slice T2DLR of the prostate provides a significant improvement of image quality without significant prolongation of acquisition time.

Comment on Gassenmaier et al. Accelerated T2-Weighted TSE Imaging of the Prostate Using Deep Learning Image Reconstruction: A Prospective Comparison with Standard T2-Weighted TSE Imaging. Cancers 2021, 13, 3593.

Magnetic resonance imaging (MRI) plays a central role in oncology without using ionizing radiation or radioactive markers [...].

Prostatic urinary tract visualization with super-resolution deep learning models.

In urethra-sparing radiation therapy, prostatic urinary tract visualization is important in decreasing the urinary side effect. A methodology has been developed to visualize the prostatic urinary tract using post-urination magnetic resonance imaging (PU-MRI) without a urethral catheter. This study investigated whether the combination of PU-MRI and super-resolution (SR) deep learning models improves the visibility of the prostatic urinary tract. We enrolled 30 patients who had previously undergone real-time-image-gated spot scanning proton therapy by insertion of fiducial markers. PU-MRI was performed using a non-contrast high-resolution two-dimensional T2-weighted turbo spin-echo imaging sequence. Four different SR deep learning models were used: the enhanced deep SR network (EDSR), widely activated SR network (WDSR), SR generative adversarial network (SRGAN), and residual dense network (RDN). The complex wavelet structural similarity index measure (CW-SSIM) was used to quantitatively assess the performance of the proposed SR images compared to PU-MRI. Two radiation oncologists used a 1-to-5 scale to subjectively evaluate the visibility of the prostatic urinary tract. Cohen's weighted kappa (k) was used as a measure of agreement of inter-operator reliability. The mean CW-SSIM in EDSR, WDSR, SRGAN, and RDN was 99.86%, 99.89%, 99.30%, and 99.67%, respectively. The mean prostatic urinary tract visibility scores of the radiation oncologists were 3.70 and 3.53 for PU-MRI (k = 0.93), 3.67 and 2.70 for EDSR (k = 0.89), 3.70 and 2.73 for WDSR (k = 0.88), 3.67 and 2.73 for SRGAN (k = 0.88), and 4.37 and 3.73 for RDN (k = 0.93), respectively. The results suggest that SR images using RDN are similar to the original images, and the SR deep learning models subjectively improve the visibility of the prostatic urinary tract.

Comparison of three dimensional isotropic turbo spin echo and conventional two dimensional turbo spin echo T2-weighted imaging in the anatomy detail at the cervical spine

Comparison of three dimensional isotropic turbo spin echo and conventional two dimensional turbo spin echo T2-weighted imaging in the anatomy detail at the cervical spine

A flexible MRI coil based on a cable conductor and applied to knee imaging

Flexible radiofrequency coils for magnetic resonance imaging (MRI) have garnered attention in research and industrial communities because they provide improved accessibility and performance and can accommodate a range of anatomic postures. Most recent flexible coil developments involve customized conductors or substrate materials and/or target applications at 3 T or above. In contrast, we set out to design a flexible coil based on an off-the-shelf conductor that is suitable for operation at 0.55 T (23.55 MHz). Signal-to-noise ratio (SNR) degradation can occur in such an environment because the resistance of the coil conductor can be significant with respect to the sample. We found that resonating a commercially available RG-223 coaxial cable shield with a lumped capacitor while the inner conductor remained electrically floating gave rise to a highly effective “cable coil.” A 10-cm diameter cable coil was flexible enough to wrap around the knee, an application that can benefit from flexible coils, and had similar conductor loss and SNR as a standard-of-reference rigid copper coil. A two-channel cable coil array also provided good SNR robustness against geometric variability, outperforming a two-channel coaxial coil array by 26 and 16% when the elements were overlapped by 20–40% or gapped by 30–50%, respectively. A 6-channel cable coil array was constructed for 0.55 T knee imaging. Incidental cartilage and bone pathologies were clearly delineated in T1- and T2-weighted turbo spin echo images acquired in 3–4 min with the proposed coil, suggesting that clinical quality knee imaging is feasible in an acceptable examination timeframe. Correcting for T1, the SNR measured with the cable coil was approximately threefold lower than that measured with a 1.5 T state-of-the-art 18-channel coil, which is expected given the threefold difference in main magnetic field strength. This result suggests that the 0.55 T cable coil conductor loss does not deleteriously impact SNR, which might be anticipated at low field.

A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Evaluation of KRAS Mutation in Rectal Cancer.

Recent studies showed the potential of MRI-based deep learning (DL) for assessing treatment response in rectal cancer, but the role of MRI-based DL in evaluating Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation remains unclear. To develop a DL method based on T2-weighted imaging (T2WI) and clinical factors for noninvasively evaluating KRAS mutation in rectal cancer. Retrospective. A total of 376 patients (108 women [28.7%]) with histopathology-confirmed rectal adenocarcinoma and KRAS mutation status. A 3 T, turbo spin echo T2WI and single-shot echo-planar diffusion-weighted imaging (b=0, 1000 sec/mm2 ). A clinical model was constructed with clinical factors (age, gender, carcinoembryonic antigen level, and carbohydrate antigen 199 level) and MRI features (tumor length, tumor location, tumor stage, lymph node stage, and extramural vascular invasion), and two DL models based on modified MobileNetV2 architecture were evaluated for diagnosing KRAS mutation based on T2WI alone (image model) or both T2WI and clinical factors (combined model). The clinical usefulness of these models was evaluated through calibration analysis and decision curve analysis (DCA). Mann-Whitney U test, Chi-squared test, Fisher's exact test, logistic regression analysis, receiver operating characteristic curve (ROC), Delong's test, Hosmer-Lemeshow test, interclass correlation coefficients, and Fleiss kappa coefficients (P < 0.05 was considered statistically significant). All the nine clinical-MRI characteristics were included for clinical model development. The clinical model, image model, and combined model in the testing cohort demonstrated good calibration and achieved areas under the curve (AUCs) of 0.668, 0.765, and 0.841, respectively. The combined model showed improved performance compared to the clinical model and image model in two cohorts. DCA confirmed the higher net benefit of the combined model than the other two models when the threshold probability is between 0.05 and 0.85. The proposed combined DL model incorporating T2WI and clinical factors may show good diagnostic performance. Thus, it could potentially serve as a supplementary approach for noninvasively evaluating KRAS mutation in rectal cancer. 3 TECHNICAL EFFICACY: Stage 2.

Evaluation of the Effects of Anti-PD-1 Therapy on Triple-Negative Breast Cancer in Mice by Diffusion Kurtosis Imaging and Dynamic Contrast-Enhanced Imaging.

The monitoring of immunotherapies is still based on changes in the tumor size in imaging, with a long evaluation period and low sensitivity. To investigate the effectiveness of diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing the therapeutic efficacy of anti-programmed death-1 (PD-1) therapy in a mouse triple negative breast cancer (TNBC) model. Prospective. A total of 54 BALB/c mouse subcutaneous 4T1 transplantation models of TNBC. A 3.0-T; turbo spin echo (TSE) T2-weighted imaging, DKI with seven b values (0, 500, 1000, 1500, 2000, 2500, and 3000 sec/mm2 ) and T1-twist DCE acquisition series. DKI and DCE-MRI parameters were evaluated by two radiologists independently. Regions of interest (ROIs) were drawn manually on the maximum cross-sectional area of the lesion; care was taken to avoid necrotic areas. The tumor cell density, the CD45 and CD31 levels were analyzed by two pathologists. The two-tailed unpaired t-test, Mann-Whitney U test, Fisher's exact test and Pearson correlation coefficient were performed. A P< 0.05 was considered statistically significant. The apparent diffusion coefficient (ADC), mean diffusivity (MD), Ktrans and Kep values were significantly different between the two groups at each time point after treatment. There were significant differences in the mean kurtosis (MK) and Ve values between the two groups at 5 and 10 days after treatment but no significant differences at 15 days (P=0.317 and 0.183, respectively). The ADC and MD values were significantly correlated with tumor cell density (ADC, r=-0.833; MD, r=0.890) and the CD45 level (ADC, r=0.720; MD, r=0.718). The Ktrans and Kep values were significantly correlated with the CD31 level (Ktrans , r=0.820; Kep , r=0.683). DKI and DCE-MRI could reflect the changes in tumor microstructure and tumor tissue vasculature after anti-PD-1 therapy, respectively. 1 TECHNICAL EFFICACY: Stage 4.

Discrimination of lipoma from atypical lipomatous tumor/well-differentiated liposarcoma using magnetic resonance imaging radiomics combined with machine learning.

To evaluate the diagnostic capability of radiomics in distinguishing lipoma and Atypic Lipomatous Tumors/Well-Differentiated Liposarcomas (ALT/WDL) with Magnetic Resonance Imaging (MRI). Patients with a histopathologic diagnosis of lipoma (n = 45) and ALT/WDL (n = 20), who had undergone pre-surgery or pre-biopsy MRI, were enrolled. The MDM2 amplification was accepted as gold-standard test. The T1-weighted turbo spin echo images were used for radiomics analysis. Utility of a predefined standardized imaging protocol and a single type of 1.5T scanner were sought as inclusion criteria. Radiomics parameters that show a certain level of reproducibility were included in the study and supplied to Support Vector Machine (SVM) as a machine learning method. No significant difference was found in terms of gender, location and age between the lipoma and ALT/WDL groups. Sixty-five parameters were accepted as reproducible. Fifty-seven parameters were able to distinguish the two groups significantly (AUC range 0.564-0.902). Diagnostic performance of the SVM was one of the highest among literature findings: sensitivity = 96.8% (95% CI 94.03-98.39%), specificity = 93.72% (95% CI 86.36-97.73%) and AUC = 0.987 (95% CI 0.972-0.999). Although radiomics has been proven to be useful in previous literature regarding discrimination of lipomas and ALT/WDLs, we found that its accuracy could further be improved with utility of standardized hardware, imaging protocols and incorporation of machine learning methods.

Comparison of Multiparametric and Fast MRI Protocols in Detecting Clinically Significant Prostate Cancer and a Detailed Cost Analysis.

Due to the long acquisition time and high cost of multiparametric magnetic resonance imaging (mpMRI), biparametric and, more recently, fast prostate magnetic resonance imaging (fpMRI) protocols have been described. However, there is insufficient data about the diagnostic performance and cost of fpMRI. To compare the diagnostic performances and cost analysis of fpMRI and mpMRI in clinically significant prostate cancer (csPCA). Retrospective. A total of 103 patients (63 had csPCA) with a mean age of 66.83 (± 7.22) years were included. A 1.5-T; T1- and T2-weighted turbo spin-echo imaging (T1WI and T2WI), echo-planar diffusion-weighted images, and dynamic contrast-enhanced T1W imaging. Three readers independently evaluated the fpMRI and mpMRI images in different sessions blinded to all patient information. Diagnostic performances of fpMRI and mpMRI were evaluated. Kappa coefficient (κ) was used to determine the interreader and intrareader agreement. A detailed cost analysis was performed for each protocol. Receiver operating characteristics analysis, area under the curve (AUC), and κ test were used. Diagnostic performance parameters were also calculated. Of the 63 malignant index lesions (csPCA), 53/63 of those (84.1%) originated from the peripheral zone and 10/63 lesions (15.9%) originated from the transition zone. The AUC values for fpMRI were 0.878 for reader 1, 0.937 for reader 2, and 0.855 for reader 3. For mpMRI, the AUC values were 0.893 for reader 1, 0.94 for reader 2, and 0.862 for reader 3. Inter and intrareader agreements were moderate to substantial (κ range, 0.5-0.79). The total cost per examination was calculated as €12.39 and €30.10 for fpMRI and mpMRI, respectively. Fast MRI protocol has similar diagnostic performance with mpMRI in detecting csPCA, and fpMRI can be considered an alternative protocol that could create a lower financial burden on health-care systems. 4 TECHNICAL EFFICACY STAGE: 6.

Magnetic Resonance Imaging-Based Radiomics Features Associated with Depth of Invasion Predicted Lymph Node Metastasis and Prognosis in Tongue Cancer.

BackgroundAdequate safe margin in tongue cancer radical surgery is one of the most important prognostic factors. However, the role of peritumoral tissues in predicting lymph node metastasis (LNM) and prognosis using radiomics analysis remains unclear.PurposeTo investigate whether magnetic resonance imaging (MRI)‐based radiomics analysis with peritumoral extensions contributes toward the prediction of LNM and prognosis in tongue cancer.Study typeRetrospective.PopulationTwo hundred and thirty‐six patients (38.56% female) with tongue cancer (training set, N = 157; testing set, N = 79; 37.58% and 40.51% female for each).Field Strength/Sequence1.5 T; T2‐weighted turbo spin‐echo images.AssessmentRadiomics models (Rprim, Rprim+3, Rprim+5, Rprim+10, Rprim+15) were developed with features extracted from the primary tumor without or with peritumoral extensions (3, 5, 10, and 15 mm, respectively). Clinicopathological characteristics selected from univariate analysis, including MRI‐reported LN status, radiological extrinsic lingual muscle invasion, and pathological depth of invasion (DOI) were further incorporated into radiomics models to develop combined radiomics models (CRprim, CRprim+3, CRprim+5, CRprim+10, CRprim+15). Finally, the model performance was validated in the testing set. DOI was measured from the adjacent normal mucosa to the deepest point of tumor invasion.Statistical TestsChi‐square test, regression analysis, receiver operating characteristic curve (ROC) analysis, decision analysis, spearman correlation analysis. The Delong test was used to compare area under the ROC (AUC). P < 0.05 was considered statistically significant.ResultsOf all the models, the CRprim+10 reached the highest AUC of 0.995 in the training set and 0.872 in the testing set. Radiomics features were significantly correlated with pathological DOI (correlation coefficients, −0.157 to −0.336). The CRprim+10 was an independent indicator for poor disease‐free survival (hazard ratio, 5.250) and overall survival (hazard ratio, 17.464) in the testing set.Data ConclusionRadiomics analysis with a 10‐mm peritumoral extension had excellent power to predict LNM and prognosis in tongue cancer.

Retrospective Assessment of the Impact of Primary Language Video Instructions on Image Quality of Abdominal MRI.

To assess the impact of instructional videos in patients' primary language on abdominal MR image quality for whom English is a second language (ESL). Twenty-nine ESL patients viewed Spanish or Mandarin Chinese instructional videos (approximately 2.5 min in duration) in the preparation room before abdominal MRI (ESL-video group). Comparison groups included 50 ESL patients who underwent MRI before video implementation (ESL-no video group) and 81 English-speaking patients who were matched for age, sex, magnet strength, and history of prior MRI with patients in the first two groups. Three radiologists independently assessed respiratory motion and image quality on turbo spin-echo T2-weighted images (T2WI) and postcontrast T1-weighted images (T1WI) using 1 to 5 Likert scales. Groups were compared using Kruskal-Wallis tests as well as generalized estimating equations (GEEs) to adjust for possible confounders. For T2WI respiratory motion and T2WI overall image quality, Likert scores of the ESL-no video group (mean score across readers of 2.6 ± 0.1 and 2.6 ± 0.1) were lower (all P < .001) compared with English-speaking (3.3 ± 0.2 and 3.3 ± 0.1) and ESL-video (3.2 ± 0.1 and 3.0 ± 0.2) groups. In the GEE model, mean T2WI respiratory motion (both adjusted P < .001) and T2WI overall quality (adjusted P= .03 and .11) were higher in English and ESL-video groups compared with ESL-no video group. For T1WI respiratory motion and T1WI overall image quality, Likert scores were not different between groups (P > .05), including in the GEE model (adjusted P > .05). Providing ESL patients with an instructional video in their primary language before abdominal MRI is an effective intervention to improve imaging quality.

Fast T2-Weighted Imaging With Deep Learning-Based Reconstruction: Evaluation of Image Quality and Diagnostic Performance in Patients Undergoing Radical Prostatectomy.

Deep learning-based reconstruction (DLR) can potentially improve image quality by reduction of noise, thereby enabling fast acquisition of magnetic resonance imaging (MRI). However, a systematic evaluation of image quality and diagnostic performance of MRI using short acquisition time with DLR has rarely been investigated in men with prostate cancer. To assess the image quality and diagnostic performance of MRI using short acquisition time with DLR for the evaluation of extraprostatic extension (EPE). Retrospective. One hundred and nine men. 3 T; turbo spin echo T2-weighted images (T2WI), echo-planar diffusion-weighted, and spoiled gradient echo dynamic contrast-enhanced images. To compare image quality, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and subjective analysis using Likert scales on three T2WIs (MRI using conventional acquisition time, MRI using short acquisition time [fast MRI], and fast MRI with DLR) were performed. The diagnostic performance for EPE was evaluated by three independent readers. SNR, CNR, and image quality scores across the three imaging protocols were compared using Friedman tests. The diagnostic performance for EPE was assessed using the area under receiver operating characteristic curves (AUCs). P < 0.05 was considered statistically significant. Fast MRI with DLR demonstrated significantly higher SNR (mean ± SD, 14.7 ± 6.8 vs. 8.8 ± 4.9) and CNR (mean ± SD, 6.5 ± 6.3 vs. 3.4 ± 3.6) values and higher image quality scores (median, 4.0 vs. 3.0 for three readers) than fast MRI. The AUCs for EPE were significantly higher with the use of DLR (0.86 vs. 0.75 for reader 2 and 0.82 vs. 0.73 for reader 3) compared with fast MRI, whereas differences were not significant for reader 1 (0.81 vs. 0.74; P=0.09). DLR may be useful in reducing the acquisition time of prostate MRI without compromising image quality or diagnostic performance. 4 TECHNICAL EFFICACY: Stage 3.

Evaluation of Visualizing the Prostatic Urinary Tract in MRI With a Super Resolution Deep Learning Model for Urethra Sparing Radiotherapy

<h3>Purpose/Objective(s)</h3> In the treatment planning for urethra-sparing radiation therapy in localized prostate cancer, it is important to visualize the prostatic urinary tract to reduce the risk of urinary symptoms linked to the urethral dose. We developed a methodology for visualizing the prostatic urinary tract by post-urination magnetic resonance imaging (PU-MRI) without using a urethral catheter for urethra-sparing radiotherapy. Several super-resolution (SR) deep learning models were proposed for improving image quality. This study investigated whether these SR deep learning models improve the visibility of the prostatic urinary tract in PU-MRI. <h3>Materials/Methods</h3> We enrolled 30 patients who had previously undergone real-time-image-gated spot scanning proton therapy (RGPT) by inserting fiducial markers at our institution from October 2019 to October 2020. PU-MRI was performed using a non-contrast high-resolution two-dimensional T2-weighted turbo spin-echo (TSE) imaging sequence. Four different SR deep learning models were constructed: the EDSR (enhanced deep SR network), WDSR (widely activated SR network), SRGAN (SR generative adversarial network), and RDN (residual dense network). All models were trained using a diverse 2K resolution high-quality image dataset. We imported PU-MRIs as low-resolution images and exported four SR images that were enlarged using one of the models each. To assess the performance of the proposed SR image compared to PU-MRI, we used the complex wavelet structural similarity index measure (CW-SSIM) as the quantitative metric. A 1-to-5 scale was used to subjectively evaluate the visibility of the prostatic urinary tract by two radiation oncologists. <h3>Results</h3> The mean (± standard deviation) of the CW-SSIM in the EDSR, WDSR, SRGAN, and RDN were 0.999 ± 0.001, 0.999 ± 0.002, 0.993 ± 0.002, and 0.997 ± 0.002, respectively. The mean prostatic urinary tract visibility scores for oncologist 1 and 2 were 3.70 ± 0.64 and 3.53 ± 0.99 for PU-MRI, 3.67 ± 0.60 and 2.70 ± 0.82 for EDSR, 3.70 ± 0.53 and 2.73 ± 0.81 for WDSR, 3.67 ± 0.65 and 2.73 ± 0.81 for SRGAN, and 4.37 ± 0.61 and 3.73 ± 0.93 for RDN, respectively. <h3>Conclusion</h3> This study investigated whether the combination of PU-MRI and SR deep learning models improves visibility of the prostatic urinary tract. The results suggest that SR images using RDN are highly similar to the original image and subjectively improve the visibility of the prostatic urinary tract.

Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Predicting and Monitoring the Response of Anti-Angiogenic Treatment in the Orthotopic Nude Mouse Model of Lung Adenocarcinoma.

The treatment efficacy of angiogenesis inhibitor could be underestimated at an early stage based on tumor volume changes. Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) can quantitatively assess tumors at the cellular level, but it is unclear whether it can provide useful information for assessing treatment response of anti-angiogenic treatment for lung adenocarcinoma. To determine the use of IVIM-DWI for non-invasive monitoring of the early response to anti-angiogenic treatment in the orthotopic transplantation of lung adenocarcinoma model. Prospective. Thirty-seven nude mice were randomized into two groups: treatment group (received bevacizumab + cisplatin, N= 20) and control group (received saline, N= 17). Single-shot turbo spin-echo (TSE) IVIM-DWI, TSE T2-weighted imaging at 3.0 T. Tumor volume, IVIM parameters (apparent diffusion coefficient [ADC], diffusivity [D], perfusion fraction [f], and pseudo-diffusion coefficient [D*]) were measured before and 2 hours, 3, 7, 10 and 14 days after treatment. Regions of interest were manually drawn along the inner edge of the tumor by two radiologists with 5 and 10-year experience in magnetic resonance imaging. Pathological examinations (hematoxylin and eosin stain, cluster of differentiation 34) were performed. Kolmogorov-Smirnov test, repeated-measure two-way analysis of variance test, Mann-Whitney U test, Pearson correlation analysis, receiver operating characteristic curve. P< 0.05 was considered statistically significant. The tumor volume of the two groups was significantly different only on day 14 (control group vs. treatment group, 43.15 ± 18.28 mm3 vs. 28.41 ± 1.71 mm3 ). ADC2h , ADC10d , D2h , D7d , D10d , and D14d were significantly higher, while f10d and f14d were significantly lower in the treatment group compared to those of the control group. Both the △ADC2h (r=-0.631) and △D2h (r=-0.700) showed moderate correlations with the relative tumor volume on day 14. IVIM has the potential to predict and monitor the early response to anti-angiogenic treatment, earlier than size changes, for lung adenocarcinoma. 2 TECHNICAL EFFICACY: Stage 4.

Delineation of Inner Annulus Fibrosus and Nucleus Pulposus on Routine T2-weighted MR Images.

Retrospective study of 150 IVDs. Assessment of costume algorithm ability to delineate the IAF and NP on routine T2 images. Central hyperintense region on T2-weighted MR images of normal lumbar IVDs represents a combination of IAF and NP. Ability to identify NP as distinct from IAF can help improve our understanding of IVD morphology in-vivo. Sagittal T2-weighted TSE MR images of 150 lumbar IVDs from 25 patients were analyzed. MR images were processed using a custom algorithm that markedly increased the signal intensity of structures with inherent signal intensity within 2 defined intensity thresholds. Signal intensity and contrast-to- noise ratio between outer annulus fibrosus, IAF, and NP were assessed at baseline and after processing. To assess consistency of underlying T2 differences, similar analysis was done on 108 discs from 18 patients in whom additional sagittal T2-weighted STIR images were available. Following image processing, apparent IAF and NP were rendered visible in 86% and 84.3% IVDs on T2-weighted TSE and STIR images respectively. While signal intensity of these 2 regions was inherently different (P< 0.001) before processing on TSE and STIR images, their visualization was facilitated by a significant increase (P<0.001) in contrast-to-noise ratio after processing. Nonvisualization of NP was associated with disc degeneration (P<0.001). Inherent differences exist in signal intensities of normal NP and IAF on T2-weighted MR images. Accentuating these differences using image postprocessing techniques can render these 2 structures visible.