Image quality of whole-body diffusion MR images comparing deep-learning accelerated and conventional sequences.

OBJECTIVES: To compare the image quality of deep learning accelerated whole-body (WB) with conventional diffusion sequences. METHODS: Fifty consecutive patients with bone marrow cancer underwent WB-MRI. Two experts compared axial b900 s/mm2 and the corresponding maximum intensity projections (MIP) of deep resolve boost (DRB) accelerated diffusion-weighted imaging (DWI) sequences (time of acquisition: 6:42 min) against conventional sequences (time of acquisition: 14 min). Readers assessed paired images for noise, artefacts, signal fat suppression, and lesion conspicuity using Likert scales, also expressing their overall subjective preference. Signal-to-noise and contrast-to-noise ratios (SNR and CNR) and the apparent diffusion coefficient (ADC) values of normal tissues and cancer lesions were statistically compared. RESULTS: Overall, radiologists preferred either axial DRB b900 and/or corresponding MIP images in almost 80% of the patients, particularly in patients with a high body-mass index (BMI > 25 kg/m2). In qualitative assessments, axial DRB images were preferred (preferred/strongly preferred) in 56-100% of cases, whereas DRB MIP images were favoured in 52-96% of cases. DRB-SNR/CNR was higher in all normal tissues (p < 0.05). For cancer lesions, the DRB-SNR was higher (p < 0.001), but the CNR was not different. DRB-ADC values were significantly higher for the brain and psoas muscles, but not for cancer lesions (mean difference: + 53 µm2/s). Inter-class correlation coefficient analysis showed good to excellent agreement (95% CI 0.75-0.93). CONCLUSION: DRB sequences produce higher-quality axial DWI, resulting in improved MIPs and significantly reduced acquisition times. However, differences in the ADC values of normal tissues need to be considered. CLINICAL RELEVANCE STATEMENT: Deep learning accelerated diffusion sequences produce high-quality axial images and MIP at reduced acquisition times. This advancement could enable the increased adoption of Whole Body-MRI for the evaluation of patients with bone marrow cancer. KEY POINTS: Deep learning reconstruction enables a more than 50% reduction in acquisition time for WB diffusion sequences. DRB images were preferred by radiologists in almost 80% of cases due to fewer artefacts, improved background signal suppression, higher signal-to-noise ratio, and increased lesion conspicuity in patients with higher body mass index. Cancer lesion diffusivity from DRB images was not different from conventional sequences.

Presentation of microstructural diffusion components by color schemes in abdominal organs.

PURPOSE: Development of a color scheme representation to facilitate the interpretation of tri-exponential DWI data from abdominal organs, where multi-exponential behavior is more pronounced. METHODS: Multi-exponential analysis of DWI data provides information about the microstructure of the tissue under study. The tri-exponential signal analysis generates numerous parameter images that are difficult to analyze individually. Summarized color images can simplify at-a-glance analysis. A color scheme was developed in which the slow, intermediate, and fast diffusion components were each assigned to a different red, green, and blue color channel. To improve the appearance of the image, histogram equalization, gamma correction, and white balance were used, and the processing parameters were adjusted. Examples of the resulting color maps of the diffusion fractions of healthy and pathological kidney and prostate are shown. RESULTS: The color maps obtained by the presented method show the merged information of the slow, intermediate, and fast diffusion components in a single view. A differentiation of the different fractions becomes clearly visible. Fast diffusion regimes, such as in the renal hilus, can be clearly distinguished from slow fractions, such as in dense tumor tissue. CONCLUSION: Combining the diffusion information from tri-exponential DWI analysis into a single color image allows for simplified interpretation of the diffusion fractions. In the future, such color images may provide additional information about the microstructural nature of the tissue under study.

Deep learning image reconstruction of diffusion-weighted imaging in evaluation of prostate cancer focusing on its clinical implications.

Background: Image-based assessment of prostate cancer (PCa) is increasingly emphasized in the diagnostic workflow for selecting biopsy targets and possibly predicting clinically significant prostate cancer (csPCa). Assessment is based on Prostate Imaging-Reporting and Data System (PI-RADS) which is largely dependent on T2-weighted image (T2WI) and diffusion weighted image (DWI). This study aims to determine whether deep learning reconstruction (DLR) can improve the image quality of DWI and affect the assessment of PI-RADS ≥4 in patients with PCa. Methods: In this retrospective study, 3.0T post-biopsy prostate magnetic resonance imaging (MRI) of 70 patients with PCa in Korea University Ansan Hospital from November 2021 to July 2022 was reconstructed with and without using DLR. Four DWI image sets were made: (I) conventional DWI (CDWI): DWI with acceleration factor 2 and conventional parallel imaging reconstruction, (II) DL1: DWI with acceleration factor 2 using DLR, (III) DL2: DWI with acceleration factor 3 using DLR, and (IV) DL3: DWI with acceleration factor 3 and half average b-value using DLR. Apparent diffusion coefficient (ADC) value, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured by one reviewer, while two reviewers independently assessed overall image quality, noise, and lesion conspicuity using a four-point visual scoring system from each DWI image set. Two reviewers also performed PI-RADSv2.1 scoring on lesions suspected of malignancy. Results: A total of 70 patients (mean age, 70.8±9.7 years) were analyzed. The image acquisition time was 4:46 min for CDWI and DL1, 3:40 min for DL2, and 2:00 min for DL3. DL1 and DL2 images resulted in better lesion conspicuity compared to CDWI images assessed by both readers (P<0.05). DLR resulted in a significant increase in SNR, from 38.4±14.7 in CDWI to 56.9±21.0 in DL1. CNR increased from 25.1±11.5 in CDWI to 43.1±17.8 in DL1 (P<0.001). PI-RADS v2.1 scoring for PCa lesions was more agreeable with the DL1 reconstruction method than with CDWI (κ value CDWI, DL1; 0.40, 0.61, respectively). A statistically significant number of lesions were upgraded from PI-RADS <4 in CDWI image to PI-RADS ≥4 in DL1 images for both readers (P<0.05). Most of the PI-RADS upgraded lesions were from higher than unfavorable intermediate-risk groups according to the 2023 National Comprehensive Cancer Network guidelines with statistically significant difference of marginal probability in DL1 and DL2 for both readers (P<0.05). Conclusions: DLR in DWI for PCa can provide options for improving image quality with a significant impact on PI-RADS evaluation or about a 23% reduction in acquisition time without compromising image quality.

Semiautomated Segmentation and Analysis of Airway Lumen in Pediatric Patients Using Ultra Short Echo Time MRI.

RATIONALE AND OBJECTIVES: Ultra short echo time (UTE) magnetic resonance imaging (MRI) pulse sequences have shown promise for airway assessment, but the feasibility and repeatability in the pediatric lung are unknown. The purpose of this work was to develop a semiautomated UTE MRI airway segmentation pipeline from the trachea-to-tertiary airways in pediatric participants and assess repeatability and lumen diameter correlations to lung function. MATERIALS AND METHODS: A total of 29 participants (n = 7 healthy, n = 11 cystic fibrosis, n = 6 asthma, and n = 5 ex-preterm), aged 7-18 years, were imaged using a 3D stack-of-spirals UTE examination at 3 T. Two independent observers performed airway segmentations using a pipeline developed in-house; observer 1 repeated segmentations 1 month later. Segmentations were extracted using region-growing with leak detection, then manually edited if required. The airway trees were skeletonized, pruned, and labeled. Airway lumen diameter measurements were extracted using ray casting. Intra- and interobserver variability was assessed using the Sørensen-Dice coefficient (DSC) and intra-class correlation coefficient (ICC). Correlations between lumen diameter and pulmonary function were assessed using Spearman's correlation coefficient. RESULTS: For airway segmentations and lumen diameter, intra- and interobserver DSCs were 0.88 and 0.80, while ICCs were 0.95 and 0.89, respectively. The variability increased from the trachea-to-tertiary airways for intra- (DSC: 0.91-0.64; ICC: 0.91-0.49) and interobserver (DSC: 0.84-0.51; ICC: 0.89-0.21) measurements. Lumen diameter was significantly correlated with forced expiratory volume in 1 second and forced vital capacity (P < .05). CONCLUSION: UTE MRI airway segmentation from the trachea-to-tertiary airways in pediatric participants across a range of diseases is feasible. The UTE MRI-derived lumen measurements were repeatable and correlated with lung function.

Deep learning-based k-space-to-image reconstruction and super resolution for diffusion-weighted imaging in whole-spine MRI.

PURPOSE: To assess the feasibility of deep learning (DL)-based k-space-to-image reconstruction and super resolution for whole-spine diffusion-weighted imaging (DWI). METHOD: This retrospective study included 97 consecutive patients with hematologic and/or oncologic diseases who underwent DL-processed whole-spine MRI from July 2022 to March 2023. For each patient, conventional (CONV) axial single-shot echo-planar DWI (b = 50, 800 s/mm2) was performed, followed by DL reconstruction and super resolution processing. The presence of malignant lesions and qualitative (overall image quality and diagnostic confidence) and quantitative (nonuniformity [NU], lesion contrast, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], and ADC values) parameters were assessed for DL and CONV DWI. RESULTS: Ultimately, 67 patients (mean age, 63.0 years; 35 females) were analyzed. The proportions of vertebrae with malignant lesions for both protocols were not significantly different (P: [0.55-0.99]). The overall image quality and diagnostic confidence scores were higher for DL DWI (all P ≤ 0.002) than CONV DWI. The NU, lesion contrast, SNR, and CNR of each vertebral segment (P ≤ 0.04) but not the NU of the sacral segment (P = 0.51) showed significant differences between protocols. For DL DWI, the NU was lower, and lesion contrast, SNR, and CNR were higher than those of CONV DWI (median values of all segments; 19.8 vs. 22.2, 5.4 vs. 4.3, 7.3 vs. 5.5, and 0.8 vs. 0.7). Mean ADC values of the lesions did not significantly differ between the protocols (P: [0.16-0.89]). CONCLUSIONS: DL reconstruction can improve the image quality of whole-spine diffusion imaging.

Impact on the image quality of modified reduced field-of-view diffusion-weighted magnetic resonance imaging of pancreatic adenocarcinoma using spatially tailored two-dimensional radiofrequency pulses with a tilted excitation plane: A comparison with conventional field-of-view imaging.

PURPOSE: Modified reduced FOV diffusion-weighted imaging (DWI) using spatially-tailored 2D RF pulses with tilted excitation plane (tilted r-DWI) has been developed. The purpose of this study was to evaluate the impact on image quality and quantitative apparent diffusion coefficient (ADC) values of tilted r-DWI for pancreatic ductal adenocarcinomas (PDAC) in comparison to conventional full-FOV DWI (f-DWI). METHODS: This retrospective study included 21 patients (mean 70.7, range 50-85 years old) with pathologically confirmed PDAC. All MR images were obtained using 3 T systems. Two radiologists evaluated presence of blurring or ghost artifacts, susceptibility artifacts, and aliasing artifacts; anatomic visualization of the pancreas; interslice signal homogeneity; overall image quality; and conspicuity of the PDAC. For quantitative analysis, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), signal-intensity ratio (SIR) and ADC values were measured using regions of interest. RESULTS: All image quality scores except aliasing artifacts in tilted r-DWI were significantly higher than those in f-DWI (p < 0.01). The CNR and SIR of PDAC were significantly higher in tilted r-DWI than in f-DWI (6.7 ± 4.4 vs. 4.7 ± 3.9, 2.02 ± 0.72 vs. 1.72 ± 0.60, p < 0.01). Conversely, the SNR of PDAC in tilted r-DWI was significantly lower than that in f-DWI (56.0 ± 33.1 vs. 113.6 ± 67.3, p < 0.01). No significant difference was observed between mean ADC values of the PDAC calculated from tilted r-DWI (tilted r-ADC) and those from f-DWI (f-ADC) (1225 ± 250 vs. 1294 ± 302, p = 0.11). CONCLUSION: The r-DWI using 2D RF techniques with a tilted excitation plane was shown to significantly improve the image quality and CNR and reduce image artifacts compared to f-DWI techniques in MRI evaluations of PDAC without significantly affecting ADC values.

Longitudinal Evaluation of Bronchial Changes in Cystic Fibrosis Patients Undergoing Elexacaftor/Tezacaftor/Ivacaftor Therapy Using Lung MRI With Ultrashort Echo-Times.

BACKGROUND: Lung magnetic resonance imaging (MRI) with ultrashort echo-times (UTE-MRI) allows high-resolution and radiation-free imaging of the lung structure in cystic fibrosis (CF). In addition, the combination of elexacaftor/tezacaftor/ivacaftor (ETI) has improved CF clinical outcomes such as need for hospitalization. However, the effect on structural disease still needs longitudinal evaluation at high resolution. PURPOSE: To analyze the effects of ETI on lung structural alterations using UTE-MRI, with a focus on bronchiectasis reversibility. STUDY TYPE: Retrospective. POPULATION: Fifty CF patients (mean age 24.3 ± 9.2; 23 males). FIELD STRENGTH/SEQUENCE: 1.5 T, UTE-MRI. ASSESSMENT: All subjects completed both UTE-MRI and pulmonary function tests (PFTs) during two annual visits (M0 and M12), and 30 of them completed a CT scan. They initiated ETI treatment after M0 within a maximum of 3 months from the annual examinations. Three observers scored a clinical MRI Bhalla score on UTE-MRI. Bronchiectasis reversibility was defined as a reduction in both outer and inner bronchial dimensions. Correlations were searched between the Bhalla score and PFT such as the forced expiratory volume in 1 second percentage predicted (FEV1%p). STATISTICAL TESTS: Comparison was assessed using the paired t-test, correlation using the Spearman correlation test with a significance level of 0.05. Concordance and reproducibility were assessed using intraclass correlation coefficient (ICC). RESULTS: There was a significant improvement in MRI Bhalla score after ETI treatment. UTE-MRI demonstrated bronchiectasis reversibility in a subgroup of 18 out of 50 CF patients (36%). These patients with bronchiectasis reversibility were significantly younger, with lower severity of wall thickening but no difference in mucus plugging extent (P = 0.39) was found. The reproducibility of UTE-MRI evaluations was excellent (ICC ≥ 0.95), was concordant with CT scan (N = 30; ICC ≥ 0.90) and significantly correlated to FEV1% at PFT at M0 (N = 50; r = 0.71) and M12 (N = 50; r = 0.72). DATA CONCLUSION: UTE-MRI is a reproducible tool for the longitudinal follow-up of CF patients, allowing to quantify the response to ETI and demonstrating the reversibility of some structural alterations such as bronchiectasis in a substantial fraction of this study population. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Evaluation of diffusion-weighted magnetic resonance imaging of the rectal cancers: comparison between modified reduced field-of-view single-shot echo-planar imaging with tilted two-dimensional radiofrequency excitation pulses and conventional full field-of-view readout-segmented echo-planar imaging.

PURPOSE: To evaluate the image quality qualitatively and quantitatively, as well as apparent diffusion coefficient (ADC) values of modified reduced field-of-view diffusion-weighted magnetic resonance imaging (MRI) using spatially tailored two-dimensional radiofrequency pulses with tilted excitation plane (tilted r-DWI) based on single-shot echo planar imaging (SS-EPI) compared with full-size field-of-view DWI (f-DWI) using readout segmented (RS)-EPI in patients with rectal cancer. MATERIALS AND METHODS: Twenty-two patients who underwent an MRI for further evaluation of rectal cancer were included in this retrospective study. All MR images were analyzed to compare image quality, lesion conspicuity, and artifacts between f-DWI with RS-EPI and tilted r-DWI with SS-EPI. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and ADC values were also compared. The Wilcoxon signed-rank test or paired t test was performed to compare the qualitative and quantitative assessments. RESULTS: All image quality scores, except aliasing artifacts, were significantly higher (p < 0.01 for all) in tilted r-DWI than f-DWI with RS-EPI. CNR in tilted r-DWI was significantly higher than in f-DWI with RS-EPI (p < 0.01), while SNR was not significantly different. Regarding the ADC values, no significant difference was observed between tilted r-DWI and f-DWI with RS-EPI (p = 0.27). CONCLUSION: Tilted r-DWI provides a better image quality with fewer artifacts and higher rectal lesion conspicuity than f-DWI with RS-EPI, indicating the feasibility of this MR sequence in evaluating rectal cancer in clinical practice.

Novel deep-learning-based diffusion weighted imaging sequence in 1.5 T breast MRI.

PURPOSE: This study aimed to assess the technical feasibility, the impact on image quality, and the acquisition time (TA) of a new deep-learning-based reconstruction algorithm in diffusion weighted imaging (DWI) of breast magnetic resonance imaging (MRI). METHODS: Retrospective analysis of 55 female patients who underwent breast DWI at 1.5 T. Raw data were reconstructed using a deep-learning (DL) reconstruction algorithm on a subset of the acquired averages, therefore a reduction of TA. Clinically used standard DWI sequence (DWIStd) and the DL-reconstructed images (DWIDL) were compared. Two radiologists rated the image quality of b800 and ADC images, using a Likert-scale from 1 to 5 with 5 being considered perfect image quality. Signal intensities were measured by placing a region of interest (ROI) at the same position in both sequences. RESULTS: TA was reduced by 40 % in DWIDL, compared to DWIStd, DWIDL improved noise and sharpness while maintaining contrast, the level of artifacts, and diagnostic confidence. There were no differences regarding the signal intensity values of the apparent diffusion coefficient (ADC), (p = 0.955), b50-values (p = 0.070) and b800-values (p = 0.415) comparing standard and DL-imaging. Lesion assessment showed no differences regarding the number of lesions in ADC and DWI (both p = 1.000) and regarding the lesion diameter in DWI (p = 0.961;0.972) and ADC (p = 0.961;0.972). CONCLUSIONS: The novel deep-learning-based reconstruction algorithm significantly reduces TA in breast DWI, while improving sharpness, reducing noise, and maintaining a comparable level of image quality, artifacts, contrast, and diagnostic confidence. DWIDL does not influence the quantifiable parameters.

Accelerated Diffusion-Weighted Imaging in 3 T Breast MRI Using a Deep Learning Reconstruction Algorithm With Superresolution Processing: A Prospective Comparative Study.

OBJECTIVES: Diffusion-weighted imaging (DWI) enhances specificity in multiparametric breast MRI but is associated with longer acquisition time. Deep learning (DL) reconstruction may significantly shorten acquisition time and improve spatial resolution. In this prospective study, we evaluated acquisition time and image quality of a DL-accelerated DWI sequence with superresolution processing (DWI DL ) in comparison to standard imaging including analysis of lesion conspicuity and contrast of invasive breast cancers (IBCs), benign lesions (BEs), and cysts. MATERIALS AND METHODS: This institutional review board-approved prospective monocentric study enrolled participants who underwent 3 T breast MRI between August and December 2022. Standard DWI (DWI STD ; single-shot echo-planar DWI combined with reduced field-of-view excitation; b-values: 50 and 800 s/mm 2 ) was followed by DWI DL with similar acquisition parameters and reduced averages. Quantitative image quality was analyzed for region of interest-based signal-to-noise ratio (SNR) on breast tissue. Apparent diffusion coefficient (ADC), SNR, contrast-to-noise ratio, and contrast (C) values were calculated for biopsy-proven IBCs, BEs, and for cysts. Two radiologists independently assessed image quality, artifacts, and lesion conspicuity in a blinded independent manner. Univariate analysis was performed to test differences and interrater reliability. RESULTS: Among 65 participants (54 ± 13 years, 64 women) enrolled in the study, the prevalence of breast cancer was 23%. Average acquisition time was 5:02 minutes for DWI STD and 2:44 minutes for DWI DL ( P < 0.001). Signal-to-noise ratio measured in breast tissue was higher for DWI STD ( P < 0.001). The mean ADC values for IBC were 0.77 × 10 -3 ± 0.13 mm 2 /s in DWI STD and 0.75 × 10 -3 ± 0.12 mm 2 /s in DWI DL without significant difference when sequences were compared ( P = 0.32). Benign lesions presented with mean ADC values of 1.32 × 10 -3 ± 0.48 mm 2 /s in DWI STD and 1.39 × 10 -3 ± 0.54 mm 2 /s in DWI DL ( P = 0.12), and cysts presented with 2.18 × 10 -3 ± 0.49 mm 2 /s in DWI STD and 2.31 × 10 -3 ± 0.43 mm 2 /s in DWI DL . All lesions presented with significantly higher contrast in the DWI DL ( P < 0.001), whereas SNR and contrast-to-noise ratio did not differ significantly between DWI STD and DWI DL regardless of lesion type. Both sequences demonstrated a high subjective image quality (29/65 for DWI STD vs 20/65 for DWI DL ; P < 0.001). The highest lesion conspicuity score was observed more often for DWI DL ( P < 0.001) for all lesion types. Artifacts were scored higher for DWI DL ( P < 0.001). In general, no additional artifacts were noted in DWI DL . Interrater reliability was substantial to excellent (k = 0.68 to 1.0). CONCLUSIONS: DWI DL in breast MRI significantly reduced scan time by nearly one half while improving lesion conspicuity and maintaining overall image quality in a prospective clinical cohort.

Validation of an MR-based multimodal method for molecular composition and proton stopping power ratio determination using ex vivo animal tissues and tissue-mimicking phantoms.

Objective. Range uncertainty in proton therapy is an important factor limiting clinical effectiveness. Magnetic resonance imaging (MRI) can measure voxel-wise molecular composition and, when combined with kilovoltage CT (kVCT), accurately determine mean ionization potential (Im), electron density, and stopping power ratio (SPR). We aimed to develop a novel MR-based multimodal method to accurately determine SPR and molecular compositions. This method was evaluated in tissue-mimicking andex vivoporcine phantoms, and in a brain radiotherapy patient.Approach. Four tissue-mimicking phantoms with known compositions, two porcine tissue phantoms, and a brain cancer patient were imaged with kVCT and MRI. Three imaging-based values were determined: SPRCM(CT-based Multimodal), SPRMM(MR-based Multimodal), and SPRstoich(stoichiometric calibration). MRI was used to determine two tissue-specific quantities of the Bethe Bloch equation (Im, electron density) to compute SPRCMand SPRMM. Imaging-based SPRs were compared to measurements for phantoms in a proton beam using a multilayer ionization chamber (SPRMLIC).Main results. Root mean square errors relative to SPRMLICwere 0.0104(0.86%), 0.0046(0.45%), and 0.0142(1.31%) for SPRCM, SPRMM, and SPRstoich, respectively. The largest errors were in bony phantoms, while soft tissue and porcine tissue phantoms had <1% errors across all SPR values. Relative to known physical molecular compositions, imaging-determined compositions differed by approximately ≤10%. In the brain case, the largest differences between SPRstoichand SPRMMwere in bone and high lipids/fat tissue. The magnitudes and trends of these differences matched phantom results.Significance. Our MR-based multimodal method determined molecular compositions and SPR in various tissue-mimicking phantoms with high accuracy, as confirmed with proton beam measurements. This method also revealed significant SPR differences compared to stoichiometric kVCT-only calculation in a clinical case, with the largest differences in bone. These findings support that including MRI in proton therapy treatment planning can improve the accuracy of calculated SPR values and reduce range uncertainties.

Generating High-Resolution Synthetic CT from Lung MRI with Ultrashort Echo Times: Initial Evaluation in Cystic Fibrosis.

Background Lung MRI with ultrashort echo times (UTEs) enables high-resolution and radiation-free morphologic imaging; however, its image quality is still lower than that of CT. Purpose To assess the image quality and clinical applicability of synthetic CT images generated from UTE MRI by a generative adversarial network (GAN). Materials and Methods This retrospective study included patients with cystic fibrosis (CF) who underwent both UTE MRI and CT on the same day at one of six institutions between January 2018 and December 2022. The two-dimensional GAN algorithm was trained using paired MRI and CT sections and tested, along with an external data set. Image quality was assessed quantitatively by measuring apparent contrast-to-noise ratio, apparent signal-to-noise ratio, and overall noise and qualitatively by using visual scores for features including artifacts. Two readers evaluated CF-related structural abnormalities and used them to determine clinical Bhalla scores. Results The training, test, and external data sets comprised 82 patients with CF (mean age, 21 years ± 11 [SD]; 42 male), 28 patients (mean age, 18 years ± 11; 16 male), and 46 patients (mean age, 20 years ± 11; 24 male), respectively. In the test data set, the contrast-to-noise ratio of synthetic CT images (median, 303 [IQR, 221-382]) was higher than that of UTE MRI scans (median, 9.3 [IQR, 6.6-35]; P < .001). The median signal-to-noise ratio was similar between synthetic and real CT (88 [IQR, 84-92] vs 88 [IQR, 86-91]; P = .96). Synthetic CT had a lower noise level than real CT (median score, 26 [IQR, 22-30] vs 42 [IQR, 32-50]; P < .001) and the lowest level of artifacts (median score, 0 [IQR, 0-0]; P < .001). The concordance between Bhalla scores for synthetic and real CT images was almost perfect (intraclass correlation coefficient, ≥0.92). Conclusion Synthetic CT images showed almost perfect concordance with real CT images for the depiction of CF-related pulmonary alterations and had better image quality than UTE MRI. Clinical trial registration no. NCT03357562 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Schiebler and Glide-Hurst in this issue.

Lung Function in Patients with Cystic Fibrosis before and during CFTR-Modulator Therapy Using 3D Ultrashort Echo Time MRI.

Background The triple combination of the cystic fibrosis transmembrane regulator (CFTR) modulators elexacaftor, tezacaftor, and ivacaftor (hereafter, elexacaftor/tezacaftor/ivacaftor) has a positive effect on lung function in patients with cystic fibrosis (CF). Purpose To compare three-dimensional (3D) ultrashort echo time (UTE) MRI functional lung data to common functional lung parameters in assessing lung function in patients with CF undergoing elexacaftor/tezacaftor/ivacaftor therapy. Materials and Methods In this prospective feasibility study, 16 participants with CF consented to undergo pulmonary MRI with a breath-hold 3D UTE sequence at baseline (April 2018-June 2019) and follow-up (April-July 2021). Eight participants received elexacaftor/tezacaftor/ivacaftor after baseline, and eight participants with unchanged treatment served as the control group. Lung function was assessed with body plethysmography and lung clearance index (LCI). Image-based functional lung parameters, such as ventilation inhomogeneity and ventilation defect percentage (VDP), were calculated from signal intensity change between MRI scans at inspiration and expiration. Metrics at baseline and follow-up were compared within groups (permutation test), correlation was tested (Spearman rank correlation), and 95% CIs were calculated (bootstrapping technique). Results MRI ventilation inhomogeneity correlated with LCI at baseline (r = 0.92, P < .001) and follow-up (r = 0.81, P = .002). Mean MRI ventilation inhomogeneity (baseline, 0.74 ± 0.15 [SD]; follow-up, 0.64 ± 0.11; P = .02) and mean VDP (baseline, 14.1% ± 7.4; follow-up, 8.5% ± 3.3; P = .02) decreased from baseline to follow-up in the treatment group. Lung function was stable over time (mean LCI: 9.3 turnovers ± 4.1 at baseline vs 11.5 turnovers ± 7.4 at follow-up; P = .34) in the control group. In all participants, correlation of forced expiratory volume in 1 second with MRI ventilation inhomogeneity was good at baseline (r = -0.61, P = .01) but poor during follow-up (r = -0.06, P = .82). Conclusion Noncontrast 3D UTE lung MRI functional parameters of ventilation inhomogeneity and VDP can be used to assess lung function over time in patients with CF and can add regional information to established global parameters, such as LCI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Iwasawa in this issue.

Accelerated diffusion-weighted imaging of the prostate using deep learning image reconstruction: A retrospective comparison with standard diffusion-weighted imaging.

PURPOSE: Routine multiparametric MRI of the prostate reduces overtreatment and increases sensitivity in the diagnosis of the most common solid cancer in men. However, the capacity of MRI systems is limited. Here we investigate the ability of deep learning image reconstruction to accelerate time consuming diffusion-weighted imaging (DWI) acquisition while maintaining diagnostic image quality. METHOD: In this retrospective study, raw data of DWI sequences of consecutive patients undergoing MRI of the prostate at a tertiary care hospital in Germany were reconstructed using standard and deep learning reconstruction. To simulate a shortening of acquisition times by 39 %, one instead of two and six instead of ten averages were used in the reconstruction of b = 0 and 1000 s/mm2 images, respectively. Image quality was assessed by three radiologists and objective image quality metrics. RESULTS: After the application of exclusion criteria, 35 out of 147 patients examined between September 2022 and January 2023 were included in this study. The radiologists perceived less image noise on deep learning reconstructed images at b = 0 s/mm2 images and ADC maps with good inter-reader agreement. Signal-to-noise ratios were similar overall with discretely reduced values in the transitional zone after deep learning reconstruction. CONCLUSIONS: An acquisition time reduction of 39 % without loss in image quality is feasible in DWI of the prostate when using deep learning image reconstruction.

Whole-body diffusion magnetic resonance imaging with simultaneous multi-slice excitation in children and adolescents.

BACKGROUND: Whole-body magnetic resonance imaging (WB-MRI) is an increasingly used guideline-based imaging modality for oncological and non-oncological pathologies during childhood and adolescence. While diffusion-weighted imaging (DWI), a part of WB-MRI, enhances image interpretation and improves sensitivity, it also requires the longest acquisition time during a typical WB-MRI scan protocol. Interleaved short tau inversion recovery (STIR) DWI with simultaneous multi-slice (SMS) acquisition is an effective way to speed up examinations. OBJECTIVE: In this study of children and adolescents, we compared the acquisition time, image quality, signal-to-noise ratio (SNR) and apparent diffusion coefficient (ADC) values of an interleaved STIR SMS-DWI sequence with a standard non-accelerated DWI sequence for WB-MRI. MATERIALS AND METHODS: Twenty children and adolescents (mean age: 13.9 years) who received two WB-MRI scans at a maximum interval of 18 months, consisting of either standard DWI or SMS-DWI MRI, respectively, were included. For quantitative evaluation, the signal-to-noise ratio (SNR) was determined for b800 images and ADC maps of seven anatomical regions. Image quality evaluation was independently performed by two experienced paediatric radiologists using a 5-point Likert scale. The measurement time per slice stack, pause between measurements including shim and total measurement time of DWI for standard DWI and SMS-DWI were extracted directly from the scan data. RESULTS: When including the shim duration, the acquisition time for SMS-DWI was 43% faster than for standard DWI. Qualitatively, the scores of SMS-DWI were higher in six locations in the b800 images and four locations in the ADC maps. There was substantial agreement between both readers, with a Cohen's kappa of 0.75. Quantitatively, the SNR in the b800 images and the ADC maps did not differ significantly from one another. CONCLUSION: Whole body-MRI with SMS-DWI provided equivalent image quality and reduced the acquisition time almost by half compared to the standard WB-DWI protocol.

Advanced Diffusion-Weighted Imaging Sequences for Breast MRI: Comprehensive Comparison of Improved Sequences and Ultra-High B-Values to Identify the Optimal Combination.

This study investigated the image quality and choice of ultra-high b-value of two DWI breast-MRI research applications. The study cohort comprised 40 patients (20 malignant lesions). In addition to s-DWI with two m-b-values (b50 and b800) and three e-b-values (e-b1500, e-b2000, and e-b2500), z-DWI and IR m-b1500 DWI were applied. z-DWI was acquired with the same measured b-values and e-b-values as the standard sequence. For IR m-b1500 DWI, b50 and b1500 were measured, and e-b2000 and e-b2500 were mathematically extrapolated. Three readers used Likert scales to independently analyze all ultra-high b-values (b1500-b2500) for each DWI with regards to scan preference and image quality. ADC values were measured in all 20 lesions. z-DWI was the most preferred (54%), followed by IR m-b1500 DWI (46%). b1500 was significantly preferred over b2000 for z-DWI and IR m-b1500 DWI (p = 0.001 and p = 0.002, respectively). Lesion detection was not significantly different among sequences or b-values (p = 0.174). There were no significant differences in measured ADC values within lesions between s-DWI (ADC: 0.97 [±0.09] × 10-3 mm2/s) and z-DWI (ADC: 0.99 [±0.11] × 10-3 mm2/s; p = 1.000). However, there was a trend toward lower values in IR m-b1500 DWI (ADC: 0.80 [±0.06] × 10-3 mm2/s) than in s-DWI (p = 0.090) and z-DWI (p = 0.110). Overall, image quality was superior and there were fewer image artifacts when using the advanced sequences (z-DWI + IR m-b1500 DWI) compared with s-DWI. Considering scan preferences, we found that the optimal combination was z-DWI with a calculated b1500, especially regarding examination time.

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

RATIONALE AND OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSION: 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.

Automated deep-learning system in the assessment of MRI-visible prostate cancer: comparison of advanced zoomed diffusion-weighted imaging and conventional technique.

BACKGROUND: Deep-learning-based computer-aided diagnosis (DL-CAD) systems using MRI for prostate cancer (PCa) detection have demonstrated good performance. Nevertheless, DL-CAD systems are vulnerable to high heterogeneities in DWI, which can interfere with DL-CAD assessments and impair performance. This study aims to compare PCa detection of DL-CAD between zoomed-field-of-view echo-planar DWI (z-DWI) and full-field-of-view DWI (f-DWI) and find the risk factors affecting DL-CAD diagnostic efficiency. METHODS: This retrospective study enrolled 354 consecutive participants who underwent MRI including T2WI, f-DWI, and z-DWI because of clinically suspected PCa. A DL-CAD was used to compare the performance of f-DWI and z-DWI both on a patient level and lesion level. We used the area under the curve (AUC) of receiver operating characteristics analysis and alternative free-response receiver operating characteristics analysis to compare the performances of DL-CAD using f- DWI and z-DWI. The risk factors affecting the DL-CAD were analyzed using logistic regression analyses. P values less than 0.05 were considered statistically significant. RESULTS: DL-CAD with z-DWI had a significantly better overall accuracy than that with f-DWI both on patient level and lesion level (AUCpatient: 0.89 vs. 0.86; AUClesion: 0.86 vs. 0.76; P < .001). The contrast-to-noise ratio (CNR) of lesions in DWI was an independent risk factor of false positives (odds ratio [OR] = 1.12; P < .001). Rectal susceptibility artifacts, lesion diameter, and apparent diffusion coefficients (ADC) were independent risk factors of both false positives (ORrectal susceptibility artifact = 5.46; ORdiameter, = 1.12; ORADC = 0.998; all P < .001) and false negatives (ORrectal susceptibility artifact = 3.31; ORdiameter = 0.82; ORADC = 1.007; all P ≤ .03) of DL-CAD. CONCLUSIONS: Z-DWI has potential to improve the detection performance of a prostate MRI based DL-CAD. TRIAL REGISTRATION: ChiCTR, NO. ChiCTR2100041834 . Registered 7 January 2021.

Comparison of diagnostic quality of 3D ultrashort-echo-time techniques for pulmonary magnetic resonance imaging in free-breathing.

BACKGROUND: Ultrashort-echo-time (UTE) sequences have been developed to overcome technical limitations of pulmonary magnetic resonance imaging (MRI). Recently, it has been shown that UTE sequences with breath-hold allow rapid image acquisition with sufficient image quality. However, patients with impaired respiration require alternative acquisition strategies while breathing freely. PURPOSE: To compare the diagnostic performance of free-breathing three-dimensional (3D)-UTE sequences with different trajectories based on pulmonary imaging of immunocompromised patients. MATERIAL AND METHODS: In a prospective study setting, two 3D-UTE sequences performed in free-breathing and exploiting non-Cartesian trajectories-one using a stack-of-spirals and the other exploiting a radial trajectory-were acquired at 3 T in patients undergoing hematopoietic stem cell transplantation. Two radiologists assessed the images regarding presence of pleural effusions and pulmonary infiltrations. Computed tomography (CT) was used as reference. RESULTS: A total of 28 datasets, each consisting of free-breathing 3D-UTE MRI with the two sequence techniques and a reference CT scan, were acquired in 20 patients. Interrater agreement was substantial for pulmonary infiltrations using both sequence techniques (κ = 0.77 - 0.78). Regarding pleural effusions, agreement was almost perfect in the stack-of-spirals (κ = 0.81) and moderate in the radial sequence (κ = 0.59). No significant differences in detectability of the assessed pulmonary pathologies were observed between both 3D-UTE sequence techniques (P > 0.05), and their level of agreement was substantial throughout (κ = 0.62-0.81). Both techniques provided high sensitivities and specificities (79%-100%) for the detection of pulmonary infiltrations and pleural effusions compared to reference CT. CONCLUSION: The diagnostic performance of the assessed 3D-UTE MRI sequences was similar. Both sequences enable the detection of typical inflammatory lung pathologies.