Quality Of Diffusion-weighted Images Research Articles

Pictorial review of the diagnosis of muscle-invasive bladder cancer using vesical imaging-reporting and data system (VI-RADS).

The Vesical Imaging-Reporting and Data System (VI-RADS) is a standard magnetic resonance imaging (MRI) and diagnostic method for muscle-invasive bladder cancer that was published in 2018. Several studies have demonstrated that VI-RADS has high diagnostic power and reproducibility. However, reading VI-RADS requires a certain amount of expertise, and radiologists need to be aware of the various pitfalls. MRI of the bladder includes T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCEI). T2WI is excellent for understanding anatomy. DWI and DCEI show high contrast between the tumor and normal anatomical structures and are suitable for staging local tumors. Bladder tumors are classified into five categories according to their size and morphology and their positional relationship to the bladder wall based on the VI-RADS diagnostic criteria. If the T2WI, DWI, and DCEI categories are the same, the category is the VI-RADS category. If the categories do not match, the DWI category is the VI-RADS category. If image quality of DWI is not evaluable, the DCEI category is the final category. In many cases, DWI is dominant, but this does not mean that T2WI and DCEI can be omitted from the reading of the bladder. In this educational review, typical and atypical teaching cases are demonstrated, and how to resolve misdiagnosis and the limitations of VI-RADS are discussed. The most important aspect of VI-RADS reading is to practice multiparametric reading with a solid understanding of the characteristics and role of each sequence and an awareness of the various pitfalls.

Super-resolution of diffusion-weighted images using space-customized learning model.

Diffusion-weighted imaging (DWI) is a noninvasive method used for investigating the microstructural properties of the brain. However, a tradeoff exists between resolution and scanning time in clinical practice. Super-resolution has been employed to enhance spatial resolution in natural images, but its application on high-dimensional and non-Euclidean DWI remains challenging. This study aimed to develop an end-to-end deep learning network for enhancing the spatial resolution of DWI through post-processing. We proposed a space-customized deep learning approach that leveraged convolutional neural networks (CNNs) for the grid structural domain (x-space) and graph CNNs (GCNNs) for the diffusion gradient domain (q-space). Moreover, we represented the output of CNN as a graph using correlations defined by a Gaussian kernel in q-space to bridge the gap between CNN and GCNN feature formats. Our model was evaluated on the Human Connectome Project, demonstrating the effective improvement of DWI quality using our proposed method. Extended experiments also highlighted its advantages in downstream tasks. The hybrid convolutional neural network exhibited distinct advantages in enhancing the spatial resolution of DWI scans for the feature learning of heterogeneous spatial data.

Accelerating cardiac diffusion tensor imaging using Swin transformer with octave convolution

AbstractCardiac diffusion tensor imaging (cDTI) is a promising and noninvasive magnetic resonance imaging (MRI) technique for assessing myocardial fiber microstructure. However, its clinical application is hampered by lengthy scanning and susceptibility to motion artifacts. This study proposes a cDTI reconstruction model (OSDTI), which leverages octave convolution (OctConv) and Swin Transformer to reconstruct diffusion‐weighted (DW) images from undersampled k‐space data. Our model capitalizes on the strengths of both OctConv and Swin Transformers. Specifically, the OctConv module excels at capturing intricate details and texture information within DW images, while the residual‐connected Swin Transformer module facilitates information retention across various feature extraction stages, thereby enhancing overall image comprehension and reconstruction accuracy. The evaluation of OSDTI using a human ex vivo heart dataset robustly validated the generalization ability of the model. Comparative analyze against existing deep‐learning methods demonstrated OSDTI's superior performance in both DW image quality and diffusion tensor metrics.

Evaluation of deep learning reconstruction on diffusion-weighted imaging quality and apparent diffusion coefficient using an ice-water phantom.

This study assessed the influence of deep learning reconstruction (DLR) on the quality of diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) using an ice-water phantom. An ice-water phantom with known diffusion properties (true ADC = 1.1 × 10-3 mm2/s at 0°C) was imaged at various b-values (0, 1000, 2000, and 4000s/mm2) using a 3T magnetic resonance imaging scanner with slice thicknesses of 1.5 and 3.0mm. All DWIs were reconstructed with or without DLR. ADC maps were generated using combinations of b-values 0 and 1000, 0 and 2000, and 0 and 4000s/mm2. Based on the quantitative imaging biomarker alliance profile, the signal-to-noise ratio (SNRs) in DWIs was calculated, and the accuracy, precision, and within-subject parameter variance (wCV) of the ADCs were evaluated. DLR improved the SNR in DWIs with b-values ranging from 0 to 2000s/mm2; however, its effectiveness was diminished at 4000s/mm2. There was no noticeable difference in the ADCs of images generated with or without implementing DLR. For a slice thickness of 1.5mm and combined b-values of 0 and 4000s/mm2, the ADC values were 0.97 × 10-3and 0.98 × 10-3mm2/s with and without DLR, respectively, both being lower than the true ADC value. Furthermore, DLR enhanced the precision and wCV of the ADC measurements. DLR can enhance the SNR, repeatability, and precision of ADC measurements; however, it does not improve their accuracies.

Feasibility study of super-resolution deep learning-based reconstruction using k-space data in brain diffusion-weighted images.

The purpose of this study is to evaluate the influence of super-resolution deep learning-based reconstruction (SR-DLR), which utilizes k-space data, on the quality of images and the quantitation of the apparent diffusion coefficient (ADC) for diffusion-weighted images (DWI) in brain magnetic resonance imaging (MRI). A retrospective analysis was performed on 34 patients who had undergone DWI using a 3T MRI system with SR-DLR reconstruction based on k-space data in August 2022. DWI was reconstructed with SR-DLR (Matrix = 684 × 684) and without SR-DLR (Matrix = 228 × 228). Measurements were made of the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) in white matter (WM) and grey matter (GM), and the full width at half maximum (FWHM) of the septum pellucidum. Two radiologists assessed image noise, contrast, artifacts, blur, and the overall quality of three image types using a four-point scale. Quantitative and qualitative scores between images with and without SR-DLR were compared using the Wilcoxon signed-rank test. Images with SR-DLR showed significantly higher SNRs and CNRs than those without SR-DLR (p < 0.001). No statistically significant variances were found in the apparent diffusion coefficients (ADCs) in WM and GM between images with and without SR-DLR (ADC in WM, p = 0.945; ADC in GM, p = 0.235). Moreover, the FWHM without SR-DLR was notably lower compared to that with SR-DLR (p < 0.001). SR-DLR has the potential to augment the quality of DWI in DL MRI scans without significantly impacting ADC quantitation.

Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in in vitro and in vivo studies

Deep learning reconstruction (DLR) to improve imaging quality has already been introduced, but no studies have evaluated the effect of DLR on diffusion-weighted imaging (DWI) or intravoxel incoherent motion (IVIM) in in vitro or in vivo studies. The purpose of this study was to determine the effect of DLR for magnetic resonance imaging (MRI) in terms of image quality improvement, apparent diffusion coefficient (ADC) assessment, and IVIM index evaluation on DWI through in vitro and in vivo studies. For the in vitro study, a phantom recommended by the Quantitative Imaging Biomarkers Alliance was scanned and reconstructed with and without DLR, and 15 patients with brain tumors with normal-appearing gray and white matter examined using IVIM and reconstructed with and without DLR were included in the in vivo study. The ADCs of all phantoms for DWI with and without DLR, as well as the coefficient of variation percentage (CV%), and ADCs and IVIM indexes for each participant, were evaluated based on DWI with and without DLR by means of region-of-interest measurements. For the in vitro study, using the mean ADCs for all phantoms, a t-test was adopted to compare DWI with and without DLR. For the in vivo study, a Wilcoxon signed-rank test was used to compare the CV% between the two types of DWI. In addition, the Wilcoxon signed-rank test was used to compare the ADC, true diffusion coefficient (D), pseudodiffusion coefficient (D*), and percentage of water molecules in micro perfusion within 1 voxel (f) with and without DLR; the limits of agreement of each parameter were determined through a Bland-Altman analysis. The in vitro study identified no significant differences between the ADC values for DWI with and without DLR (P > 0.05), and the CV% was significantly different for DWI with and without DLR (P < 0.05) when b values ≥250 s/mm2 were used. The in vivo study revealed that D* and f with and without DLR were significantly different (P < 0.001). The limits of agreement of the ADC, D, and D* values for DWI with and without DLR were determined as 0.00 ± 0.51 × 10-3, 0.00 ± 0.06 × 10-3, and 1.13 ± 4.04 × 10-3 mm2/s, respectively. The limits of agreement of the f values for DWI with and without DLR were determined as -0.01 ± 0.07. Deep learning reconstruction for MRI has the potential to significantly improve DWI quality at higher b values. It has some effect on D* and f values in the IVIM index evaluation, but ADC and D values are less affected by DLR.

MRI-Conditional Breast Tissue Expander: First In-Human Multi-Case Assessment of MRI-Related Complications and Image Quality.

This study aims to assess potential complications and effects on the magnetic resonance imaging (MRI) image quality of a new MRI-conditional breast tissue expander (Motiva Flora®) in its first in-human multi-case application. Twenty-four patients with 36 expanders underwent non-contrast breast MRI with T1-weighted, T2-weighted, and diffusion-weighted imaging (DWI) sequences on a 3 T unit before breast tissue expander exchange surgery, being monitored during and after MRI for potential complications. Three board-certified breast radiologists blindly and independently reviewed image quality using a four-level scale ("poor", "sufficient", "good", and "excellent"), with inter-reader reliability being assessed with Kendall's τb. The maximum diameters of RFID-related artifacts on T1-weighted and DWI sequences were compared with the Wilcoxon signed-rank test. All 24 examinations were completed without patient-related or device-related complications. The T1-weighted and T2-weighted sequences of all the examinations had "excellent" image quality and a median 11 mm (IQR 9-12 mm) RFID artifact maximum diameter, significantly lower (p < 0.001) than on the DWI images (median 32.5 mm, IQR 28.5-34.5 mm). DWI quality was rated at least "good" in 63% of the examinations, with strong inter-reader reliability (Kendall's τb 0.837, 95% CI 0.687-0.952). This first in-human study confirms the MRI-conditional profile of this new expander, which does not affect the image quality of T1-weighted and T2-weighted sequences and moderately affects DWI quality.

Image quality evaluation of diffusion-weighted imaging in bladder cancer: a comparison between integrated slice-specific dynamic shimming and single-shot echo-planar imaging.

Diffusion-weighted imaging (DWI) image quality will affect how well radiologists detect lesions and judge muscular invasion. This study qualitatively and quantitatively compared the image quality of DWI with integrated slice-specific dynamic shimming (iShim) and single-shot echo-planar imaging (SS-EPI) in the diagnosis of bladder cancer (BC) using 3.0 T magnetic resonance imaging (MRI). We also investigated the application value of iShim DWI in BC. This retrospective study enrolled 97 patients with BC who underwent a preoperative MRI examination, including iShim and SS-EPI DWI. Two radiologists, blinded to the type of DWI, independently rated DWIs on a 5-point Likert scale regarding image quality features (anatomical details, distortion, lesion conspicuity, artifacts, and overall image quality) and evaluated tumor muscular invasion. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), apparent diffusion coefficient (ADC) values, and tumor numbers were manually recorded by another 2 radiologists. Pathologists recorded tumor numbers and sizes in a standard manner. The inter- and intraobserver consistency of image quality features scoring was good to excellent (κ >0.75; P<0.001). The scores of iShim DWI were higher than those of SS-EPI DWI in terms of distortion, artifacts, and overall image quality (P<0.001). The SNR and CNR of iShim DWI were higher than those of SS-EPI DWI (P<0.001), but there was no significant difference in ADC values between the 2 sequences (P>0.05). Based on pathological findings, the sensitivity of iShim and SS-EPI DWI in diagnosing tumor that diameter less than 1 cm was 100% (79/79) and 93.7% (74/79), respectively. The specificity and accuracy (95.2% and 90.2%, respectively) of iShim DWI in diagnosing tumor muscular invasion were significantly higher than those of SS-EPI DWI (76.2% and 80.4%, respectively). The area under the receiver operating characteristic curve of iShim DWI was significantly higher than that of SS-EPI DWI in diagnosing tumor muscular invasion (P=0.017). Compared with SS-EPI DWI, iShim DWI provided higher image quality. iShim DWI effectively detected BC and better identified muscular invasion. This finding can guide the clinical selection of appropriate treatments for patients with BC.

The Effect of Oral Laxatives on Rectal Distension and Image Quality in Magnetic Resonance Imaging of the Prostate.

Introduction Increasing rectal size is associated with increased artefacts on magnetic resonance imaging (MRI) of the prostate and has the potential to degrade image quality. The objective of this study was to analyse the effect of oral laxative medication on rectal distension and image quality in prostate MRI. Methods Eighty patients prospectively received either 15 mg of oral senna (laxative group) or no medication (control group). Patients underwent prostate MRI according to standard local protocol and seven rectal dimensions on axial and sagittal images were measured. A subjective assessment of rectal distension was also made using a five-point Likert scale.Finally, artefacts on diffusion-weighted sequences were assessed using a four-point Likert scale. Results There was a small reduction in rectal diameter on sagittal images in the laxative group compared to the control group, with mean diameters of 27.1 mm and 30.0 mm respectively, p=0.02. There was no significant difference in rectal measurements of anteroposterior diameter, transverse diameter, or rectal circumference on axial imaging.Subjective scoring also demonstrated no significant difference in diffusion-weighted imaging quality between the laxative group and control group, p=0.82. Conclusion Bowel preparation with the oral laxative, senna, provided only a marginal decrease in rectal distension on one measure and no reduction in artefacts on diffusion-weighted sequences. The findings of this study do not support the routine use of this medication in patients undergoing prostate MRI.

Diffusion-weighted Breast MRI at 3 Tesla: Improved Lesion Visibility and Image Quality with a Combination of Water-excitation and Spectral Fat Saturation

In breast MRI with diffusion-weighted imaging (DWI), fat suppression is essential for eliminating the dominant lipid signal. This investigation evaluates a combined water-excitation-spectral-fatsat method (WEXfs) versus standard spectral attenuated inversion recovery (SPAIR) in high-resolution 3-Tesla breast MRI. Multiparametric breast MRI with 2 echo-planar DWI sequences was performed in 83 patients (50.1 ± 12.6 years) employing either WEXfs or SPAIR for fat signal suppression. Three radiologists assessed overall DWI quality and delineability of 88 focal lesions (28 malignant, 60 benign) on images with b values of 800 and 1600 s/mm2, as well as apparent diffusion coefficient (ADC) maps. For each fat suppression method and b value, the longest lesion diameter was determined in addition to measuring the signal intensity in DWI and ADC value in standardized regions of interest. Regardless of b values, image quality (all p < 0.001) and lesion delineability (all p ≤ 0.003) with WEXfs-DWI were deemed superior compared to SPAIR-DWI in benign and malignant lesions. Irrespective of lesion characterization, WEXfs-DWI provided superior signal-to-noise, contrast-to-noise and signal-intensity ratios with 1600 s/mm2 (all p ≤ 0.05). The lesion size difference between contrast-enhanced T1 subtraction images and DWI was smaller for WEXfs compared to SPAIR fat suppression (all p ≤ 0.007). The mean ADC value in malignant lesions was lower for WEXfs-DWI (p < 0.001), while no significant ADC difference was ascertained between both techniques in benign lesions (p=0.947). WEXfs-DWI provides better subjective and objective image quality than standard SPAIR-DWI, resulting in a more accurate estimation of benign and malignant lesion size.

Feasibility of diffusion weighting with a local inside-out nonlinear gradient coil for prostate MRI.

Prostate cancer remains the second leading cancer killer of men, yet it is also a disease with a high rate of overtreatment. Diffusion-weighted imaging (DWI) has shown promise as a reliable, grade-sensitive imaging method, but it is limited by low image quality. Currently, DWI quality image is directly related to low gradient amplitudes, since weak gradients must be compensated with long echo times. We propose a new type of MRI accessory, an "inside-out" and nonlinear gradient, whose sole purpose is to deliver diffusion encoding to a region of interest. Performance was simulated in OPERA and the resulting fields were used to simulate DWI with two-compartment and kurtosis models. Experiments with a nonlinear head gradient prove the accuracy of DWI and apparent diffusion coefficient (ADC) maps encoded with nonlinear gradients. Simulations validated thermal and mechanical safety while showing a 5- to 10-fold increase in gradient strength over prostate. With these strengths, lesion contrast to noise ratio in ADC maps approximately doubled for a range of anatomical positions. Proof-of-principle experiments show that spatially varying b-values can be corrected for accurate DWI and ADC. Dedicated nonlinear diffusion encoding hardware could improve prostate DWI.

Calculation of Apparent Diffusion Coefficients in Prostate Cancer Using Deep Learning Algorithms: A Pilot Study.

BackgroundApparent diffusion coefficients (ADCs) obtained with diffusion-weighted imaging (DWI) are highly valuable for the detection and staging of prostate cancer and for assessing the response to treatment. However, DWI suffers from significant anatomic distortions and susceptibility artifacts, resulting in reduced accuracy and reproducibility of the ADC calculations. The current methods for improving the DWI quality are heavily dependent on software, hardware, and additional scan time. Therefore, their clinical application is limited. An accelerated ADC generation method that maintains calculation accuracy and repeatability without heavy dependence on magnetic resonance imaging scanners is of great clinical value.ObjectivesWe aimed to establish and evaluate a supervised learning framework for synthesizing ADC images using generative adversarial networks.MethodsThis prospective study included 200 patients with suspected prostate cancer (training set: 150 patients; test set #1: 50 patients) and 10 healthy volunteers (test set #2) who underwent both full field-of-view (FOV) diffusion-weighted imaging (f-DWI) and zoomed-FOV DWI (z-DWI) with b-values of 50, 1,000, and 1,500 s/mm2. ADC values based on f-DWI and z-DWI (f-ADC and z-ADC) were calculated. Herein we propose an ADC synthesis method based on generative adversarial networks that uses f-DWI with a single b-value to generate synthesized ADC (s-ADC) values using z-ADC as a reference. The image quality of the s-ADC sets was evaluated using the peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity (SSIM), and feature similarity (FSIM). The distortions of each ADC set were evaluated using the T2-weighted image reference. The calculation reproducibility of the different ADC sets was compared using the intraclass correlation coefficient. The tumor detection and classification abilities of each ADC set were evaluated using a receiver operating characteristic curve analysis and a Spearman correlation coefficient.ResultsThe s-ADCb1000 had a significantly lower RMSE score and higher PSNR, SSIM, and FSIM scores than the s-ADCb50 and s-ADCb1500 (all P < 0.001). Both z-ADC and s-ADCb1000 had less distortion and better quantitative ADC value reproducibility for all the evaluated tissues, and they demonstrated better tumor detection and classification performance than f-ADC.ConclusionThe deep learning algorithm might be a feasible method for generating ADC maps, as an alternative to z-ADC maps, without depending on hardware systems and additional scan time requirements.

Value of bowel preparation techniques for prostate MRI: a preliminary study

BackgroundBowel preparation before multiparametric MRI (mpMRI) of the prostate is performed widely, despite contradictory or no evidence for efficacy.PurposeTo investigate the value of hyoscine N-butylbromide (HBB), microenema (ME) and ‘dietary restrictions’ (DR) for artifact reduction and image quality (IQ) in mpMRI of the prostate.Study typeRetrospective.PopulationBetween 10/2018 and 02/2020 treatment-naïve men (median age, 64.9; range 39.8–87.3) who underwent mpMRI of the prostate were included. The total patient sample comprised of n = 180 patients, who received either HBB, ME, were instructed to adhere to DR, or received a combination of those measures prior to the MR scan.Field strength/sequenceT2-weighted imaging (T2w), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI) scanned on two 3T systems.AssessmentA radiologist specialized in urogenital imaging (R1) and a senior radiology resident (R2) visually assessed IQ parameters on transversal T2w, DWI and ADC maps on a 5-point Likert-like scale.Statistical testsGroup comparison between IQ parameters was performed on reader level using Kruskal–Wallis and Mann–Whitney U tests. Binary univariate logistic regression analysis was used to assess independent predictors of IQ. Interrater agreement was assessed using Intraclass Correlation Coefficient (ICC).Results‘DWI geometric distortion’ was significantly more pronounced in the HBB+/ME−/DR− (R1, 3.6 and R2, 4.0) as compared to the HBB−/ME+/DR− (R1, 4.2 and R2, 4.6) and HBB+/ME+/DR− (R1, 4.3 and R2, 4.7) cohort, respectively. Parameters ‘DWI IQ’ and ‘Whole MRI IQ’ were rated similarly by both readers. ME was a significant independent predictor of ‘good IQ’ for the whole MRI for R1 [b = 1.09, OR 2.98 (95% CI 1.29, 6.87)] and R2 [b = 1.01, OR 2.73 (95% CI 1.24, 6.04)], respectively.Data conclusionME seems to significantly improve image quality of DWI and the whole mpMRI image set of the prostate. HBB and DR did not have any benefit.

Ficoll as testing material for diffusion weighted imaging-quality assurance phantoms

PurposeThe aim of this work is to test the use of aqueous solutions of Ficoll®**, a highly branched polymer displaying crowding properties, to build a phantom suitable for Diffusion Weighted Imaging (DWI) in Magnetic Resonance Imaging (MRI). MethodsWe developed a test object made of a cylindrical plastic container with a precise geometrical arrangement suitable for measuring several samples at the same time. The container was designed to host single vials with variable geometry and number, and to fit inside common commercial head coils for MRI scanners.In our experiments, vials were filled with 8 aqueous solutions of Ficoll 70 and Ficoll 400 spanning a range of polymer concentration from 5 to 30% by weight. Vials containing ultra-pure water were also used as reference. Experiments were performed on both 1.5 and 3 T clinical scanners (GE, Philips and Siemens), under the conditions of a standard clinical examination. ResultsThe geometry of the phantom provided reduced imaging artifacts, especially image distortions at magnetic interfaces. We found that the Apparent Diffusion Coefficient (ADC) varied in the range of 0.00125–0.00223 mm2/s and decreased with Ficoll concentration. ADC vs Ficoll concentration exhibited a linear trend. Results were consistent over time and among different MRI clinical scanners, showing an average variability of 3% at 1.5 T and of 7.5% at 3 T. Moreover, no substantial difference was found between Ficoll 70 and 400. By varying Ficoll concentration, ADC can be modulated to approach tissue-mimicking values. Preliminary results for relaxation measurements proved that both T1 and T2 decreased with Ficoll concentration in the ranges 1.3–2.4 s and 150–800 ms respectively. ConclusionsIn this work, we propose a 3D phantom design based on the widespread crowding agent Ficoll, which is suitable for DWI quality assurance purposes in MRI acquisitions. Aqueous Ficoll solutions provide good performance in terms of stability, ease of preparation, and safety.

Radiotherapy for prostate cancer: Effect of gold fiducial markers on diffusion-weighted magnetic resonance imaging.

There has been an increase in the use of gold fiducial markers to ensure precise radiotherapy delivery in prostate cancer patients. However, metal artifacts may affect the quality of subsequent imaging used to assess disease status following treatment. In this study, we evaluated the effect of gold fiducial markers on magnetic resonance imaging (MRI), particularly on diffusion-weighted imaging (DWI). Among 57 patients with prostate cancer, 21 patients in whom two gold markers were placed in the prostate tumor with abnormal signal intensity on DWI were evaluated. The effect of the markers on DWI was evaluated on a scale of 1-5, with a high score indicating clinical usefulness. Change inapparent diffusion coefficient (ADC; 10-3 mm2 /s) from before to after marker placement was also evaluated. The mean effect of the markers on DWI was 4.3 (standard deviation [SD] 1.3, range 2-5) points. The mean change in ADC was 0.045 (SD 0.041, range 0.025-0.089) × 10-3 mm2 /s. The gold fiducial markers demonstrated negligible effect on DWI quality. Therefore, gold markers do not affect MRI quality, particularly DWI, and may be used during follow-up in prostate cancer patients.

Does microenema administration improve the quality of DWI sequences in rectal MRI?

To determine whether the administration of a microenema immediately prior to rectal magnetic resonance imaging (MRI) decreases the level of gas-related artifacts on diffusion-weighted imaging (DWI) sequences. This retrospective analysis included 492 (183 baseline and 309 post-total neoadjuvant treatment [TNT]) consecutive MRI scans for rectal cancer from January 2019 to January 2020. Scan-related factors were identified including microenema use (yes or no), field of view (FOV) in DWI (b = 800 or b = 1500), and magnet strength (1.5T or 3T). Two readers scored DWI studies for gas-related artifacts and T2-weighted sequences for the amount of intraluminal gas on a 5-point scale. Fisher's exact test and the Rao-Scott Chi-squared test were used to examine associations between microenema use and other factors. Generalized estimating equation and multivariable regression models were performed to examine the effect of microenema use in subgroups of scansfor each reader. Cohen's κ was used to assess inter-reader agreement. Gas-related artifact levels decreased in scans with microenema overall(P < 0.001) as well as when scans werestratified by FOV (P ≤ 0.003). For both readers, post-TNT scans with microenema showed lower artifact levels overall (P < 0.014 and P < 0.001) and inpost-TNTsubgroups of axial DWI scans(P ≤ 0.006 and P < 0.001) and scans acquiredwith a3T magnet (P ≤ 0.001 for both FOV). No evidence of decreased artifact level was found for baseline studies. Decreased gas was seen with microenema use (P < 0.001 for both readers). Inter-reader agreement on artifact-level and gas-level assessments ranged from slight to substantial (κ = 0.273-0.685). Microenema use prior to rectal MRI reduces gas-related artifacts on DWI, including both large and small FOV sequences and particularly on post-TNT scans performed at 3T, and offers a viable solution to improve DWI quality.

Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks

Ischemic stroke lesion segmentation from Computed Tomography Perfusion (CTP) images is important for accurate diagnosis of stroke in acute care units. However, it is challenged by low image contrast and resolution of the perfusion parameter maps, in addition to the complex appearance of the lesion. To deal with this problem, we propose a novel framework based on synthesized pseudo Diffusion-Weighted Imaging (DWI) from perfusion parameter maps to obtain better image quality for more accurate segmentation. Our framework consists of three components based on Convolutional Neural Networks (CNNs) and is trained end-to-end. First, a feature extractor is used to obtain both a low-level and high-level compact representation of the raw spatiotemporal Computed Tomography Angiography (CTA) images. Second, a pseudo DWI generator takes as input the concatenation of CTP perfusion parameter maps and our extracted features to obtain the synthesized pseudo DWI. To achieve better synthesis quality, we propose a hybrid loss function that pays more attention to lesion regions and encourages high-level contextual consistency. Finally, we segment the lesion region from the synthesized pseudo DWI, where the segmentation network is based on switchable normalization and channel calibration for better performance. Experimental results showed that our framework achieved the top performance on ISLES 2018 challenge and: (1) our method using synthesized pseudo DWI outperformed methods segmenting the lesion from perfusion parameter maps directly; (2) the feature extractor exploiting additional spatiotemporal CTA images led to better synthesized pseudo DWI quality and higher segmentation accuracy; and (3) the proposed loss functions and network structure improved the pseudo DWI synthesis and lesion segmentation performance. The proposed framework has a potential for improving diagnosis and treatment of the ischemic stroke where access to real DWI scanning is limited.

Gross tumour volume delineation in anal cancer on T2-weighted and diffusion-weighted MRI – Reproducibility between radiologists and radiation oncologists and impact of reader experience level and DWI image quality

PurposeTo assess how gross tumour volume (GTV) delineation in anal cancer is affected by interobserver variations between radiologists and radiation oncologists, expertise level, and use of T2-weighted MRI (T2W-MRI) vs. diffusion-weighted imaging (DWI), and to explore effects of DWI quality. Methods and materialsWe retrospectively analyzed the MRIs (T2W-MRI and b800-DWI) of 25 anal cancer patients. Four readers (Senior and Junior Radiologist; Senior and Junior Radiation Oncologist) independently delineated GTVs, first on T2W-MRI only and then on DWI (with reference to T2W-MRI). Maximum Tumour Diameter (MTD) was calculated from each GTV. Mean GTVs/MTDs were compared between readers and between T2W-MRI vs. DWI. Interobserver agreement was calculated as Intraclass Correlation Coefficient (ICC), Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). DWI image quality was assessed using a 5-point artefact scale. ResultsInterobserver agreement between radiologists vs. radiation oncologists and between junior vs. senior readers was good–excellent, with similar agreement for T2W-MRI and DWI (e.g. ICCs 0.72–0.94 for T2W-MRI and 0.68–0.89 for DWI). There was a trend towards smaller GTVs on DWI, but only for the radiologists (P = 0.03–0.07). Moderate-severe DWI-artefacts were observed in 11/25 (44%) cases. Agreement tended to be lower in these cases. ConclusionOverall interobserver agreement for anal cancer GTV delineation on MRI is good for both radiologists and radiation oncologists, regardless of experience level. Use of DWI did not improve agreement. DWI artefacts affecting GTV delineation occurred in almost half of the patients, which may severely limit the use of DWI for radiotherapy planning if no steps are undertaken to avoid them.

Quality of Prostate MRI: Is the PI-RADS Standard Sufficient?

The Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) published a set of minimum technical standards (MTS) to improve image quality and reduce variability in multiparametric prostate MRI. The effect of PIRADSv2 MTS on image quality has not been validated. We aimed to determine whether adherence to PI-RADSv2 MTS improves study adequacy and perceived quality. Sixty-two prostate MRI examinations including T2 weighted (T2W) and diffusion weighted image (DWI) consecutively referred to our center from 62 different institutions within a 12-month period (September 2017 to September 2018) were included. Six readers assessed images as adequate or inadequate for use in PCa detection and a numerical image quality ranking was given using a 1-5 scale. The PI-RADSv2 MTS were synthesized into sets of seven and 10 rules for T2W and DWI, respectively. Image adherence was assessed using Digital Imaging and Communications in Medicine (DICOM) metadata. Statistical analysis of survey results and image adherence was performed based on reader quality scoring (Kendall Rank tau-b) and reader adequate scoring (Wilcoxon test for association) for T2 and DWI quality assessment. Out of 62 images, 52 (83%) T2W and 38 (61%) DWIs were rated to be adequate by a majority of readers. Reader adequacy scores showed no significant association with adherence to PI-RADSv2. There was a weak (tau-b = 0.22) but significant (p value = 0.01) correlation between adherence to PIRADSv2 MTS and image quality for T2W. Studies following all PI-RADSv2 T2W rules achieved a higher median average quality score (3.58 for 7/7 vs. 3.0 for <7/7, p = 0.012). No statistical relationship with PI-RADSv2 MTS adherence and DWI quality was found. Among 62 sites performing prostate MRI, few were considered of high quality, but the majority were considered adequate. DWI showed considerably lower rates of adequate studies in the sample. Adherence to PI-RADSv2 MTS did not increase the likelihood of having a qualitatively adequate T2W or DWI.

Investigation of in Vivo Human Cardiac Diffusion Tensor Imaging Using Unsupervised Dense Encoder-Fusion-Decoder Network

Diffusion tensor imaging (DTI) is currently the unique imaging technique that can detect the structure of in-vivo human myocardium without invasivity and radiation. However, it is particularly sensitive to motions, especially respiratory motion that results in serious signal loss in diffusion-weighted (DW) images. This makes it impossible to accurately measure cardiac microscopic structural properties. To cope with such problem, this paper proposes an unsupervised dense-encoder-fusion-decoder network (DEFD-net) to compensate for signal loss in cardiac DW images, which allows investigating in-vivo myocardium structure more accurately. The DEFD-net consists of three modules, namely dense-encoder, fusion module and decoder module. The dense-encoder and decoder are trained firstly with DW images acquired at different trigger delays in an unsupervised manner for extracting local and global features. A fusion strategy is then designed to fuse the extracted features. Finally, the well-trained decoder is used to reconstruct the fused DW image from the fused features. To validate the superiority of the proposed method, comparison with existing methods such as PCAMIP, WIF and U2Fusion is performed on both simulated and acquired datasets. The experimental results showed that the proposed method effectively compensates for motion-induced signal loss in DW images, thus leading to much better DW image quality with respect to existing methods. Moreover, the subsequently derived myocardium fiber structure is more regular.