Intravoxel incoherent motion imaging to assess the acute effects of moderate-intensity continuous training and high-intensity interval training on thigh muscles.

This study investigated the use of intravoxel incoherent motion imaging (IVIM) to compare skeletal muscle perfusion during and after high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) to determine the impact on fat oxidation outcomes. Twenty overweight volunteers were recruited for the study. Each participant received one HIIT intervention and one MICT intervention using a cycling ergometer. Participants underwent a magnetic resonance imaging scan before, immediately after, and 1 and 2 h after each intervention. The IVIM parameters (D, fD*) of the rectus femoris, vastus lateralis, and biceps femoris long head were obtained. Changes in IVIM parameters of these muscles after both exercise interventions were compared using a two-factor repeated measures analysis of variance. In the rectus femoris, the fD* increased immediately after exercise intervention (d = 0.69 × 10-3  mm2 /s, p < 0.0083) and 2 h after exercise intervention (d = 0.64 × 10-3  mm2 /s, p < 0.0083) compared with before exercise. The increase in the fD* in the HIIT group was greater than that in the MICT group (d = 0.32, p = 0.023). In the vastus lateralis, the fD* increased immediately after the exercise intervention (d = 0.53 × 10-3  mm2 /s, p < 0.001) and returned to the pre-exercise level 1 h after exercising. The increase in the fD* in the HIIT group was lower than that in the MICT group (d = -0.21, p = 0.015). For the biceps femoris long head, the fD* was not significantly different between the two exercise interventions before and after exercise. Furthermore, the fD* 60 min after the HIIT intervention correlated with maximal oxygen consumption (VO2max), whereas fD* immediately after the MICT intervention correlated with VO2max. In summary, IVIM parameters can be used to evaluate differences in muscle perfusion between HIIT and MICT, and show a correlation with VO2max.

Deep Learning-Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast-Enhanced MRI.

BACKGROUND: The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs. PURPOSE: To assess the performance of the deep learning-based artificial intelligence (AI) software in identifying and measuring lesions on contrast-enhanced magnetic resonance imaging (MRI) images in patients with FLLs. STUDY TYPE: Retrospective. SUBJECTS: 395 patients with 1149 FLLs. FIELD STRENGTH/SEQUENCE: The 1.5 T and 3 T scanners, including T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging. ASSESSMENT: The diagnostic performance of AI, radiologist, and their combination was compared. Using 20 mm as the cut-off value, the lesions were divided into two groups, and then divided into four subgroups: <10, 10-20, 20-40, and ≥40 mm, to evaluate the sensitivity of radiologists and AI in the detection of lesions of different sizes. We compared the pathologic sizes of 122 surgically resected lesions with measurements obtained using AI and those made by radiologists. STATISTICAL TESTS: McNemar test, Bland-Altman analyses, Friedman test, Pearson's chi-squared test, Fisher's exact test, Dice coefficient, and intraclass correlation coefficients. A P-value <0.05 was considered statistically significant. RESULTS: The average Dice coefficient of AI in segmentation of liver lesions was 0.62. The combination of AI and radiologist outperformed the radiologist alone, with a significantly higher detection rate (0.894 vs. 0.825) and sensitivity (0.883 vs. 0.806). The AI showed significantly sensitivity than radiologists in detecting all lesions <20 mm (0.848 vs. 0.788). Both AI and radiologists achieved excellent detection performance for lesions ≥20 mm (0.867 vs. 0.881, P = 0.671). A remarkable agreement existed in the average tumor sizes among the three measurements (P = 0.174). DATA CONCLUSION: AI software based on deep learning exhibited practical value in automatically identifying and measuring liver lesions. TECHNICAL EFFICACY: Stage 2.

Comparison of Free-Breathing 3D Phase-Resolved Functional Lung (PREFUL) MRI With Dynamic 19 F Ventilation MRI in Patients With Obstructive Lung Disease and Healthy Volunteers.

BACKGROUND: Non-contrast-enhanced 1 H magnetic resonance imaging (MRI) with full lung coverage shows promise for assessment of regional lung ventilation but a comparison with direct ventilation measurement using 19 F MRI is lacking. PURPOSE: To compare ventilation parameters calculated using 3D phase-resolved functional lung (PREFUL) MRI with 19 F MRI. STUDY TYPE: Prospective. POPULATION: Fifteen patients with asthma, 14 patients with chronic obstructive lung disease, and 13 healthy volunteers. FIELD STRENGTH/SEQUENCE: A 3D gradient-echo pulse sequence with golden-angle increment and stack-of-stars encoding at 1.5 T. ASSESSMENT: All participants underwent 3D PREFUL MRI and 19 F MRI. For 3D PREFUL, static regional ventilation (RVent) and dynamic flow-volume cross-correlation metric (FVL-CM) were calculated. For both parameters, ventilation defect percentage (VDP) values and ventilation defect (VD) maps (including a combination of both parameters [VDPCombined ]) were determined. For 19 F MRI, images from eight consecutive breaths under volume-controlled inhalation of perfluoropropane were acquired. Time-to-fill (TTF) and wash-in (WI) parameters were extracted. For all 19 F parameters, a VD map was generated and the corresponding VDP values were calculated. STATISTICAL TESTS: For all parameters, the relationship between the two techniques was assessed using a Spearman correlation (r). Differences between VDP values were compared using Bland-Altman analysis. For regional comparison of VD maps, spatial overlap and Sørensen-Dice coefficients were computed. RESULTS: 3D PREFUL VDP values were significantly correlated to VDP measures by 19 F (r range: 0.59-0.70). For VDPRVent , no significant bias was observed with VDP of the third and fourth breath (bias range = -6.8:7.7%, P range = 0.25:0.30). For VDPFVL-CM , no significant bias was found with VDP values of fourth-eighth breaths (bias range = -2.0:12.5%, P range = 0.12:0.75). The overall spatial overlap of all VD maps increased with each breath, ranging from 61% to 81%, stabilizing at the fourth breath. DATA CONCLUSION: 3D PREFUL MRI parameters showed moderate to strong correlation with 19 F MRI. Depending on the 3D PREFUL VD map, the best regional agreement was found to 19 F VD maps of third-fifth breath. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Effect of CFTR modulator therapy with elexacaftor/tezacaftor/ivacaftor on pulmonary ventilation derived by 3D phase-resolved functional lung MRI in cystic fibrosis patients.

OBJECTIVES: To investigate whether 3D phase-resolved functional lung (PREFUL)-MRI parameters are suitable to measure response to elexacaftor/tezacaftor/ivacaftor (ETI) therapy and their association with clinical outcomes in cystic fibrosis (CF) patients. METHODS: Twenty-three patients with CF (mean age: 21; age range: 14-46) underwent MRI examination at baseline and 8-16 weeks after initiation of ETI. Morphological and 3D PREFUL scans assessed pulmonary ventilation. Morphological images were evaluated using a semi-quantitative scoring system, and 3D PREFUL scans were evaluated by ventilation defect percentage (VDP) values derived from regional ventilation (RVent) and cross-correlation maps. Improved ventilation volume (IVV) normalized to body surface area (BSA) between baseline and post-treatment visit was computed. Forced expiratory volume in 1 second (FEV1) and mid-expiratory flow at 25% of forced vital capacity (MEF25), as well as lung clearance index (LCI), were assessed. Treatment effects were analyzed using paired Wilcoxon signed-rank tests. Treatment changes and post-treatment agreement between 3D PREFUL and clinical parameters were evaluated by Spearman's correlation. RESULTS: After ETI therapy, all 3D PREFUL ventilation markers (all p < 0.0056) improved significantly, except for the mean RVent parameter. The BSA normalized IVVRVent was significantly correlated to relative treatment changes of MEF25 and mucus plugging score (all |r| > 0.48, all p < 0.0219). In post-treatment analyses, 3D PREFUL VDP values significantly correlated with spirometry, LCI, MRI global, morphology, and perfusion scores (all |r| > 0.44, all p < 0.0348). CONCLUSIONS: 3D PREFUL MRI is a very promising tool to monitor CFTR modulator-induced regional dynamic ventilation changes in CF patients. CLINICAL RELEVANCE STATEMENT: 3D PREFUL MRI is sensitive to monitor CFTR modulator-induced regional ventilation changes in CF patients. Improved ventilation volume correlates with the relative change of mucus plugging, suggesting that reduced endobronchial mucus is predominantly responsible for regional ventilation improvement. KEY POINTS: • 3D PREFUL MRI-derived ventilation maps show significantly reduced ventilation defects in CF patients after ETI therapy. • Significant post-treatment correlations of 3D PREFUL ventilation measures especially with LCI, FEV1 %pred, and global MRI score suggest that 3D PREFUL MRI is sensitive to measure improved regional ventilation of the lung parenchyma due to reduced inflammation induced by ETI therapy in CF patients. • 3D PREFUL MRI-derived improved ventilation volume (IVV) correlated with MRI mucus plugging score changes suggesting that reduced endobronchial mucus is predominantly responsible for regional ventilation improvement 8-16 weeks after ETI therapy.

T1 mapping as a quantitative imaging biomarker for diagnosing cervical cancer: a comparison with diffusion kurtosis imaging.

BACKGROUND: T1 mapping can potentially quantitatively assess the intrinsic properties of tumors. This study was conducted to explore the ability of T1 mapping in distinguishing cervical cancer type, grade, and stage and compare the diagnostic performance of T1 mapping with diffusion kurtosis imaging (DKI). METHODS: One hundred fifty-seven patients with pathologically confirmed cervical cancer were enrolled in this prospectively study. T1 mapping and DKI were performed. The native T1, difference between native and postcontrast T1 (T1diff), mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) were calculated. Cervical squamous cell carcinoma (CSCC) and adenocarcinoma (CAC), low- and high-grade carcinomas, and early- and advanced-stage groups were compared using area under the receiver operating characteristic (AUROC) curves. RESULTS: The native T1 and MK were higher, and the MD and ADC were lower for CSCC than for CAC (all p < 0.05). Compared with low-grade CSCC, high-grade CSCC had decreased T1diff, MD, ADC, and increased MK (p < 0.05). Compared with low-grade CAC, high-grade CAC had decreased T1diff and increased MK (p < 0.05). Native T1 was significantly higher in the advanced-stage group than in the early-stage group (p < 0.05). The AUROC curves of native T1, MK, ADC and MD were 0,772, 0.731, 0.715, and 0.627, respectively, for distinguishing CSCC from CAC. The AUROC values were 0.762 between high- and low-grade CSCC and 0.835 between high- and low-grade CAC, with T1diff and MK showing the best discriminative values, respectively. For distinguishing between advanced-stage and early-stage cervical cancer, only the AUROC of native T1 was statistically significant (AUROC = 0.651, p = 0.002). CONCLUSIONS: Compared with DKI-derived parameters, native T1 exhibits better efficacy for identifying cervical cancer subtype and stage, and T1diff exhibits comparable discriminative value for cervical cancer grade.

Prediction of upgrade to clinically significant prostate cancer in patients under active surveillance: Performance of a fully automated AI-algorithm for lesion detection and classification.

BACKGROUND: Multiparametric MRI (mpMRI) improves the detection of aggressive prostate cancer (PCa) subtypes. As cases of active surveillance (AS) increase and tumor progression triggers definitive treatment, we evaluated whether an AI-driven algorithm can detect clinically significant PCa (csPCa) in patients under AS. METHODS: Consecutive patients under AS who received mpMRI (PI-RADSv2.1 protocol) and subsequent MR-guided ultrasound fusion (targeted and extensive systematic) biopsy between 2017 and 2020 were retrospectively analyzed. Diagnostic performance of an automated clinically certified AI-driven algorithm was evaluated on both lesion and patient level regarding the detection of csPCa. RESULTS: Analysis of 56 patients resulted in 93 target lesions. Patient level sensitivity and specificity of the AI algorithm was 92.5%/31% for the detection of ISUP ≥ 1 and 96.4%/25% for the detection of ISUP ≥ 2, respectively. The only case of csPCa missed by the AI harbored only 1/47 Gleason 7a core (systematic biopsy; previous and subsequent biopsies rendered non-csPCa). CONCLUSIONS: AI-augmented lesion detection and PI-RADS scoring is a robust tool to detect progression to csPCa in patients under AS. Integration in the clinical workflow can serve as reassurance for the reader and streamline reporting, hence improve efficiency and diagnostic confidence.

Inter- and intravisit repeatability of free-breathing MRI in pediatric cystic fibrosis lung disease.

PURPOSE: The purpose of this study is to assess the intra- and interscan repeatability of free-breathing phase-resolved functional lung (PREFUL) MRI in stable pediatric cystic fibrosis (CF) lung disease in comparison to static breath-hold hyperpolarized 129-xenon MRI (Xe-MRI) and pulmonary function tests. METHODS: Free-breathing 1-hydrogen MRI and Xe-MRI were acquired from 15 stable pediatric CF patients and seven healthy age-matched participants on two visits, 1 month apart. Same-visit MRI scans were also performed on a subgroup of the CF patients. Following the PREFUL algorithm, regional ventilation (RVent) and regional flow volume loop cross-correlation maps were determined from the free-breathing data. Ventilation defect percentage (VDP) was determined from RVent maps (VDPRVent ), regional flow volume loop cross-correlation maps (VDPCC ), VDPRVent ∪ VDPCC , and multi-slice Xe-MRI. Repeatability was evaluated using Bland-Altman analysis, coefficient of repeatability (CR), and intraclass correlation. RESULTS: Minimal bias and no significant differences were reported for all PREFUL MRI and Xe-MRI VDP parameters between intra- and intervisits (all P > 0.05). Repeatability of VDPRVent , VDPCC , VDPRVent ∪ VDPCC , and multi-slice Xe-MRI were lower between the two-visit scans (CR = 14.81%, 15.36%, 16.19%, and 9.32%, respectively) in comparison to the same-day scans (CR = 3.38%, 2.90%, 1.90%, and 3.92%, respectively). pulmonary function tests showed high interscan repeatability relative to PREFUL MRI and Xe-MRI. CONCLUSION: PREFUL MRI, similar to Xe-MRI, showed high intravisit repeatability but moderate intervisit repeatability in CF, which may be due to inherent disease instability, even in stable patients. Thus, PREFUL MRI may be considered a suitable outcome measure for future treatment response studies.

Evaluation of image registration algorithms for 3D phase-resolved functional lung ventilation magnetic resonance imaging in healthy volunteers and chronic obstructive pulmonary disease patients.

The purpose of the current study was to assess the influence of the registration algorithms on the repeatability of three-dimensional (3D) phase-resolved functional lung (PREFUL) ventilation magnetic resonance imaging (MRI). Twenty-three healthy volunteers and 10 patients with chronic obstructive pulmonary disease (COPD) underwent 3D PREFUL MRI during tidal breathing. The registration of dynamically acquired data to a fixed image was executed using single-step, stepwise, and group-oriented registration (GOREG) approaches. Advanced Normalization Tools (ANTs) and the Forsberg image-registration package were used for the registration. Image registration algorithms were tested for differences and evaluated by the repeatability analysis of ventilation parameters using coefficient of variation (CoV), intraclass-correlation coefficient, Bland-Altman plots, and correlation to spirometry. Also, the registration time and image quality were computed for all registration approaches. Very strong to strong correlations (r range: 0.917-0.999) were observed between ventilation parameters derived using various registration approaches. Median CoV values of the cross-correlation (CC) parameter were significantly lower (all p ≤ 0.0054) for ANTs GOREG compared with single-step and stepwise ANTs registration. The majority of comparisons between COPD patients and age-matched healthy volunteers showed agreement among the registration approaches. The repeatability of regional ventilation (RVent)-based ventilation defect percentage (VDPRVent ) and VDPCC was significantly higher (both p ≤ 0.0054) for Forsberg GOREG compared with ANTs GOREG. All 3D PREFUL-derived ventilation parameters correlated with forced expiratory volume in 1 s (FEV1 ) and the FEV1 / forced vital capacity (FVC) ratio (all |r| > 0.40, all p < 0.03). The image sharpness of RVent maps was statistically elevated (all p < 0.001) using GOREG compared with single-step and stepwise registration approaches using ANTs. The best computational performance was achieved with Forsberg GOREG. The GOREG scheme improves the repeatability and image quality of dynamic 3D PREFUL ventilation parameters. Registration time can be ~10-fold reduced to 9 min using the Forsberg method with equal or even improved repeatability and comparable PREFUL ventilation results compared with the ANTs method.

Comparison of a Deep Learning-Accelerated vs. Conventional T2-Weighted Sequence in Biparametric MRI of the Prostate.

BACKGROUND: Demand for prostate MRI is increasing, but scan times remain long even in abbreviated biparametric MRIs (bpMRI). Deep learning can be leveraged to accelerate T2-weighted imaging (T2WI). PURPOSE: To compare conventional bpMRIs (CL-bpMRI) with bpMRIs including a deep learning-accelerated T2WI (DL-bpMRI) in diagnosing prostate cancer. STUDY TYPE: Retrospective. POPULATION: Eighty consecutive men, mean age 66 years (47-84) with suspected prostate cancer or prostate cancer on active surveillance who had a prostate MRI from December 28, 2020 to April 28, 2021 were included. Follow-up included prostate biopsy or stability of prostate-specific antigen (PSA) for 1 year. FIELD STRENGTH AND SEQUENCES: A 3 T MRI. Conventional axial and coronal T2 turbo spin echo (CL-T2), 3-fold deep learning-accelerated axial and coronal T2-weighted sequence (DL-T2), diffusion weighted imaging (DWI) with b = 50 sec/mm2 , 1000 sec/mm2 , calculated b = 1500 sec/mm2 . ASSESSMENT: CL-bpMRI and DL-bpMRI including the same conventional diffusion-weighted imaging (DWI) were presented to three radiologists (blinded to acquisition method) and to a deep learning computer-assisted detection algorithm (DL-CAD). The readers evaluated image quality using a 4-point Likert scale (1 = nondiagnostic, 4 = excellent) and graded lesions using Prostate Imaging Reporting and Data System (PI-RADS) v2.1. DL-CAD identified and assigned lesions of PI-RADS 3 or greater. STATISTICAL TESTS: Quality metrics were compared using Wilcoxon signed rank test, and area under the receiver operating characteristic curve (AUC) were compared using Delong's test. SIGNIFICANCE: P = 0.05. RESULTS: Eighty men were included (age: 66 ± 9 years; 17/80 clinically significant prostate cancer). Overall image quality results by the three readers (CL-T2, DL-T2) are reader 1: 3.72 ± 0.53, 3.89 ± 0.39 (P = 0.99); reader 2: 3.33 ± 0.82, 3.31 ± 0.74 (P = 0.49); reader 3: 3.67 ± 0.63, 3.51 ± 0.62. In the patient-based analysis, the reader results of AUC are (CL-bpMRI, DL-bpMRI): reader 1: 0.77, 0.78 (P = 0.98), reader 2: 0.65, 0.66 (P = 0.99), reader 3: 0.57, 0.60 (P = 0.52). Diagnostic statistics from DL-CAD (CL-bpMRI, DL-bpMRI) are sensitivity (0.71, 0.71, P = 1.00), specificity (0.59, 0.44, P = 0.05), positive predictive value (0.23, 0.24, P = 0.25), negative predictive value (0.88, 0.88, P = 0.48). CONCLUSION: Deep learning-accelerated T2-weighted imaging may potentially be used to decrease acquisition time for bpMRI. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Capability of arterial spin labeling and intravoxel incoherent motion diffusion-weighted imaging to detect early kidney injury in chronic kidney disease.

OBJECTIVES: To prospectively investigate the capability of arterial spin labeling (ASL) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the identification of early kidney injury in chronic kidney disease (CKD) patients with normal estimated glomerular filtration rate (eGFR). METHODS: Fifty-four CKD patients confirmed by renal biopsy (normal eGFR group [eGFR ≥ 90 mL/min/1.73 m2]: n = 26; abnormal eGFR group [eGFR < 90 mL/min/1.73 m2]: n = 28) and 20 healthy volunteers (HV) were recruited. All subjects were examined by IVIM-DWI and ASL imaging. Renal blood flow (RBF) derived from ASL, true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) derived from IVIM-DWI were measured from the renal cortex. One-way analysis of variance was used to compare MRI parameters among the three groups. The correlation between eGFR and MRI parameters was evaluated by Spearman correlation analysis. Diagnostic performances of MRI parameters for detecting kidney injury were assessed by receiver operating characteristic (ROC) curves. RESULTS: The renal cortical D, D*, f, and RBF values showed statistically significant differences among the three groups. eGFR was positively correlated with MRI parameters (D: r = 0.299, D*: r = 0.569, f: r = 0.733, RBF: r = 0.586). The areas under the curve (AUCs) for discriminating CKD patients from HV were 0.725, 0.752, 0.947, and 0.884 by D, D*, f, and RBF, respectively. D, D*, f, RBF, and eGFR identified CKD patients with normal eGFR with AUCs of 0.735, 0.612, 0.917, 0.827, and 0.733, respectively, and AUC of f value was significantly larger than that of eGFR. CONCLUSION: IVIM-DWI and ASL were useful for detecting underlying pathologic injury in early CKD patients with normal eGFR. KEY POINTS: • The renal cortical f and RBF values in the control group were significantly higher than those in the normal eGFR group. • A negative correlation was observed between the renal cortical D, D*, f, and RBF values and SCr and 24 h-UPRO, while eGFR was significantly positively correlated with renal cortical D, D*, f, and RBF values. • The AUC of renal cortical f values was statistically larger than that of eGFR for the discrimination between the CKD with normal eGFR group and the control group.

Evaluation of interstitial fibrosis in chronic kidney disease by multiparametric functional MRI and histopathologic analysis.

OBJECTIVES: To investigate the diagnostic value of functional MRI to assess renal interstitial fibrosis in patients with chronic kidney disease (CKD). METHODS: We prospectively recruited 80 CKD patients who underwent renal biopsies and 16 healthy volunteers to undergo multiparametric functional MRI examinations. The Oxford MEST-C classification was used to score the interstitial fibrosis. The diagnostic performance of functional MRI to discriminate interstitial fibrosis was evaluated by calculating the area under the receiver operating characteristic (ROC) curves. RESULTS: IgA nephropathy (60%) accounted for the majority of pathologic type in the CKD patients. Apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) was correlated with interstitial fibrosis (rho = -0.73). Decreased renal blood flow (RBF) derived from arterial spin labeling (rho = -0.78) and decreased perfusion fraction (f) derived from DWI (rho = -0.70) were accompanied by increased interstitial fibrosis. The T1 value from T1 mapping correlated with interstitial fibrosis (rho = 0.67) (all p < 0.01). The areas under the ROC curve for the discrimination of ≤ 25% vs. > 25% and ≤ 50% vs. > 50% interstitial fibrosis were 0.87 (95% confidence interval, 0.78 to 0.94) and 0.93 (0.86 to 0.98) by ADC, 0.84 (0.74 to 0.91) and 0.94 (0.86 to 0.98) by f, 0.93 (0.85 to 0.98) and 0.90 (0.82 to 0.96) by RBF, and 0.91 (0.83 to 0.96) and 0.77 (0.66 to 0.85) by T1, respectively. CONCLUSIONS: Functional MRI parameters were strongly correlated with the interstitial fibrosis of CKD. Therefore, it might a powerful tool to assess interstitial fibrosis of CKD noninvasively. KEY POINTS: • In CKD patients, the renal cortical ADC value decreased and T1 value increased significantly compared with healthy volunteers. • Functional MRI revealed significantly decreased renal perfusion in CKD patients compared with healthy volunteers. • The renal cortical ADC, f, RBF, and T1 values were strongly correlated with the interstitial fibrosis of CKD.

Prediction of tumor budding in patients with rectal adenocarcinoma using b-value threshold map.

OBJECTIVE: To investigate the feasibility of b-value threshold (bThreshold) map in preoperative evaluation of tumor budding (TB) in patients with locally advanced rectal cancer (LARC). METHODS: Patients with LARC were enrolled and underwent diffusion-weighted imaging (DWI). Contrast-to-noise ratio (CNR) between the lesions and normal tissues was assessed using DWI and bThreshold maps. TB was counted and scored using hematoxylin and eosin staining. Reproducibility for the apparent diffusion coefficient (ADC), bThreshold values, and region-of-interest (ROI) sizes were compared. Differences in ADC and bThreshold values with low-intermediate and high TB grades and the correlations between mean ADC and bThreshold values with TB categories were analyzed. Diagnostic performance of ADC and bThreshold values was assessed using area under the curve (AUC) and decision curve analysis. RESULTS: Fifty-one patients were evaluated. The CNR on bThreshold maps was significantly higher than that on DW images (9.807 ± 4.811 vs 7.779 ± 3.508, p = 0.005). Reproducibility was excellent for the ADC (ICC 0.933; CV 8.807%), bThreshold values (ICC 0.958; CV 7.399%), and ROI sizes (ICC 0.934; CV 8.425%). Significant negative correlations were observed between mean ADC values and TB grades and positive correlations were observed between mean bThreshold values and TB grades (p < 0.05). bThreshold maps showed better diagnostic performance than ADC maps (AUC, 0.914 vs 0.726; p = 0.048). CONCLUSIONS: In LARC patients, bThreshold values could distinguish different TB grades better than ADC values, and bThreshold maps may be a preoperative, non-invasive approach to evaluate TB grades. KEY POINTS: • Compared with diffusion-weighted images, bThreshold maps improved visualization and detection of rectal tumors. • Agreement and diagnostic performance of bThreshold values are superior to apparent diffusion coefficient in assessing tumor budding grades in patients with locally advanced rectal cancer. • bThreshold maps could be used to evaluate tumor budding grades non-invasively before operation.

Ultrafast DCE-MRI for discriminating pregnancy-associated breast cancer lesions from lactation related background parenchymal enhancement.

OBJECTIVE: To investigate the utility of ultrafast dynamic-contrast-enhanced (DCE) MRI in visualization and quantitative characterization of pregnancy-associated breast cancer (PABC) and its differentiation from background-parenchymal-enhancement (BPE) among lactating patients. MATERIALS AND METHODS: Twenty-nine lactating participants, including 10 PABC patients and 19 healthy controls, were scanned on 3-T MRI using a conventional DCE protocol interleaved with a golden-angle radial sparse parallel (GRASP) ultrafast sequence for the initial phase. The timing of the visualization of PABC lesions was compared to lactational BPE. Contrast-noise ratio (CNR) was compared between the ultrafast and conventional DCE sequences. The differences in each group's ultrafast-derived kinetic parameters including maximal slope (MS), time to enhancement (TTE), and area under the curve (AUC) were statistically examined using the Mann-Whitney test and receiver operator characteristic (ROC) curve analysis. RESULTS: On ultrafast MRI, breast cancer lesions enhanced earlier than BPE (p < 0.0001), enabling breast cancer visualization freed from lactation BPE. A higher CNR was found for ultrafast acquisitions vs. conventional DCE (p < 0.05). Significant differences in AUC, MS, and TTE values were found between the tumor and BPE (p < 0.05), with ROC-derived AUC of 0.86 ± 0.06, 0.82 ± 0.07, and 0.68 ± 0.08, respectively. The BPE grades of the lactating PABC patients were reduced as compared with the healthy lactating controls (p < 0.005). CONCLUSION: Ultrafast DCE MRI allows BPE-free visualization of lesions, improved tumor conspicuity, and kinetic quantification of breast cancer during lactation. Implementation of this method may assist in the utilization of breast MRI for lactating patients. CLINICAL RELEVANCE: The ultrafast sequence appears to be superior to conventional DCE MRI in the challenging evaluation of the lactating breast. Thus, supporting its possible utilization in the setting of high-risk screening during lactation and the diagnostic workup of PABC. KEY POINTS: • Differences in the enhancement slope of cancer relative to BPE allowed the optimal visualization of PABC lesions on mid-acquisitions of ultrafast DCE, in which the tumor enhanced prior to the background parenchyma. • The conspicuity of PABC lesions on top of the lactation-related BPE was increased using an ultrafast sequence as compared with conventional DCE MRI. • Ultrafast-derived maps provided further characterization and parametric contrast between PABC lesions and lactation-related BPE.

A concurrent, deep learning-based computer-aided detection system for prostate multiparametric MRI: a performance study involving experienced and less-experienced radiologists.

OBJECTIVES: To evaluate the effect of a deep learning-based computer-aided diagnosis (DL-CAD) system on experienced and less-experienced radiologists in reading prostate mpMRI. METHODS: In this retrospective, multi-reader multi-case study, a consecutive set of 184 patients examined between 01/2018 and 08/2019 were enrolled. Ground truth was combined targeted and 12-core systematic transrectal ultrasound-guided biopsy. Four radiologists, two experienced and two less-experienced, evaluated each case twice, once without (DL-CAD-) and once assisted by DL-CAD (DL-CAD+). ROC analysis, sensitivities, specificities, PPV and NPV were calculated to compare the diagnostic accuracy for the diagnosis of prostate cancer (PCa) between the two groups (DL-CAD- vs. DL-CAD+). Spearman's correlation coefficients were evaluated to assess the relationship between PI-RADS category and Gleason score (GS). Also, the median reading times were compared for the two reading groups. RESULTS: In total, 172 patients were included in the final analysis. With DL-CAD assistance, the overall AUC of the less-experienced radiologists increased significantly from 0.66 to 0.80 (p = 0.001; cutoff ISUP GG ≥ 1) and from 0.68 to 0.80 (p = 0.002; cutoff ISUP GG ≥ 2). Experienced radiologists showed an AUC increase from 0.81 to 0.86 (p = 0.146; cutoff ISUP GG ≥ 1) and from 0.81 to 0.84 (p = 0.433; cutoff ISUP GG ≥ 2). Furthermore, the correlation between PI-RADS category and GS improved significantly in the DL-CAD + group (0.45 vs. 0.57; p = 0.03), while the median reading time was reduced from 157 to 150 s (p = 0.023). CONCLUSIONS: DL-CAD assistance increased the mean detection performance, with the most significant benefit for the less-experienced radiologist; with the help of DL-CAD less-experienced radiologists reached performances comparable to that of experienced radiologists. KEY POINTS: • DL-CAD used as a concurrent reading aid helps radiologists to distinguish between benign and cancerous lesions in prostate MRI. • With the help of DL-CAD, less-experienced radiologists may achieve detection performances comparable to that of experienced radiologists. • DL-CAD assistance increases the correlation between PI-RADS category and cancer grade.

Role of diffusion kurtosis imaging in evaluating microstructural changes in spinal cord of patients with cervical spondylosis.

STUDY DESIGN: Analytical cross-sectional study. PURPOSE: To study the role of diffusion kurtosis imaging (DKI) in evaluating microstructural changes in patients with cervical spondylosis. OVERVIEW OF LITERATURE: Cervical spondylosis is a common progressive degenerative disorder of the spine. Conventional magnetic resonance imaging (MRI) can only detect the changes in the spinal cord once there are visual signal changes; hence, it underestimates the extent of the injury. Newer imaging techniques like Diffusion Tensor and Kurtosis Imaging can evaluate the microstructural changes in cervical spinal cord before the obvious signal changes appear. METHODS: Conventional MRI, diffusion tensor imaging (DTI), and DKI scans were performed for 90 cervical spondylosis patients on 1.5-T MR Siemens Magnetom aera after obtaining informed consent. Eight patients were excluded due to poor image quality. Fractional anisotropy (FA) colour maps and diffusion kurtosis (DK) maps corresponding to spinal cord cross sections at C2-C3 intervertebral disc level (control) and at the most stenotic levels were obtained. Modified Japanese Orthopaedic Association (mJOA) scoring was used for clinical assessment of the spinal cord function. The changes in DTI and DKI parameters and their correlation with mJOA scores were analysed by SPSS 23 software. RESULTS: In our study, mean FA and mean kurtosis (MK) values at the stenotic level (0.54, 1.02) were significantly lower than values at the non-stenotic segment (0.70, 1.27). The mean diffusivity (MD) value at the stenotic segment (1.25) was significantly higher than in the non-stenotic segment (1.09). We also observed a strong positive correlation between mJOA score and FA and MK values and a negative correlation between mJOA score and MD values, suggesting a correlation of FA, MK, and MD with the clinical severity of the disease. CONCLUSION: Addition of DTI and DKI sequences helps in early identification of the disease without any additional cost incurred by the patient.

Evaluation of functional and metabolic tumor volume using voxel-wise analysis in childhood rhabdomyosarcoma.

BACKGROUND: Cross-sectional imaging-based morphological characteristics of pediatric rhabdomyosarcoma have failed to predict outcomes. OBJECTIVE: To evaluate the feasibility and possible value of generating tumor sub-volumes using voxel-wise analysis of metabolic and functional data from positron emission tomography/magnetic resonance imaging (PET/MR) or PET/computed tomography (CT) and MRI in rhabdomyosarcoma. MATERIALS AND METHODS: Thirty-four examinations in 17 patients who received PET/MRI or PET/CT plus MRI were analyzed. The volume of interest included total tumor volume before and after therapy. Apparent diffusion coefficients (ADC) and standard uptake values (SUV) were determined voxel-wise. Voxels were assigned to three different groups based on ADC and SUV: "viable tumor tissue," "intermediate tissue" or "possible necrosis." In a second approach, data were grouped into three clusters using the Gaussian mixture model. The ratio of these clusters to total tumor volume and changes due to chemotherapy were correlated with clinical and histopathological data. RESULTS: After chemotherapy, the proportion of voxels in the different groups changed significantly. A significant reduction of the proportion of voxels assigned to cluster 1 was found, from a mean of 36.4% to 2.5% (P < 0.001). There was a significant increase in the proportion of voxels in cluster 3 following chemotherapy from 24.8% to 81.6% (P = 0.02). The proportion of voxels in cluster 2 differed depending on the presence or absence of tumor recurrence, falling from 48% to 10% post-chemotherapy in the group with no tumor recurrence (P < 0.05) and from 29% to 23% (P > 0.05) in the group with tumor recurrence. CONCLUSION: Voxel-wise evaluation of multimodal data in rhabdomyosarcoma is feasible. Our initial results suggest that the different distribution of sub-volumes before and after therapy may have prognostic significance.

Diffusion Kurtosis MR Imaging of Invasive Breast Cancer: Correlations With Prognostic Factors and Molecular Subtypes.

BACKGROUND: The associations between diffusion kurtosis imaging (DKI)-derived parameters and clinical prognostic factors of breast cancer have not been fully evaluated; this knowledge may have implications for outcome prediction and treatment strategies. PURPOSE: To determine associations between quantitative diffusion parameters derived from DKI and diffusion-weighted imaging (DWI) and the prognostic factors and molecular subtypes of breast cancer. STUDY TYPE: Retrospective. POPULATION: A total of 383 women (mean age, 53.8 years; range, 31-82 years) with breast cancer who underwent preoperative breast MRI including DKI and DWI. FIELD STRENGTH/SEQUENCE: A 3.0 T; DKI using a spin-echo echo-planar imaging (EPI) sequence (b values: 200, 500, 1000, 1500, and 2000 sec/mm2 ), DWI using a readout-segmented EPI sequence (b values: 0 and 1000 sec/mm2 ) and dynamic contrast-enhanced breast MRI. ASSESSMENT: Two radiologists (J.Y.K. and H.S.K. with 9 years and 1 year of experience in MRI, respectively) independently measured kurtosis, diffusivity, and apparent diffusion coefficient (ADC) values of breast cancer by manually placing a regions of interest within the lesion. Diffusion measures were compared according to nodal status, grade, and molecular subtypes. STATISTICAL TESTS: Kruskal-Wallis test, Mann-Whitney U test with Bonferroni correction, receiver operating characteristic (ROC) analysis, and multivariate logistic regression analysis. (Statistical significance level of P < 0.05). RESULTS: All diffusion measures showed significant differences according to axillary nodal status and histological grade. Kurtosis showed significant differences among molecular subtypes. The luminal subtype (median 1.163) showed a higher kurtosis value compared to the HER2-positive (median 0.962) or triple-negative subtypes (median 1.072). ROC analysis for differentiating HER2-positive from luminal subtypes revealed that kurtosis yielded the highest area under the curve of 0.781. In multivariate analyses, kurtosis remained a significant factor associated with differentiation between HER2-positive and luminal (odds ratio [OR] = 0.993), triple-negative and luminal (OR = 0.995), and HER2-positive and triple-negative subtypes (OR = 0.994). DATA CONCLUSION: Quantitative diffusion parameters derived from DKI and DWI are associated with prognostic factors for breast cancer. Moreover, DKI-derived kurtosis can help distinguish between the molecular subtypes of breast cancer. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: 3.

Clinical Feasibility of Structural and Functional MRI in Free-Breathing Neonates and Infants.

BACKGROUND: Evaluation of structural lung abnormalities with magnetic resonance imaging (MRI) has previously been shown to be predictive of clinical neonatal outcomes in preterm birth. MRI during free-breathing with phase-resolved functional lung (PREFUL) may allow for complimentary functional information without exogenous contrast. PURPOSE: To investigate the feasibility of structural and functional pulmonary MRI in a cohort of neonates and infants with no cardiorespiratory disease. Macrovascular pulmonary blood flows were also evaluated. STUDY TYPE: Prospective. POPULATION: Ten term infants with no clinically defined cardiorespiratory disease were imaged. Infants recruited from the general population and neonatal intensive care unit (NICU) were studied. FIELD STRENGTH/SEQUENCE: T1 -weighted VIBE, T2 -weighted BLADE uncorrected for motion. Ultrashort echo time (UTE) and 3D-flow data were acquired during free-breathing with self-navigation and retrospective reconstruction. Single slice 2D-gradient echo (GRE) images were acquired during free-breathing for PREFUL analysis. Imaging was performed at 3 T. ASSESSMENT: T1 , T2 , and UTE images were scored according to the modified Ochiai scheme by three pediatric body radiologists. Ventilation/perfusion-weighted maps were extracted from free-breathing GRE images using PREFUL analysis. Ventilation and perfusion defect percent (VDP, QDP) were calculated from the segmented ventilation and perfusion-weighted maps. Time-averaged cardiac blood velocities from three-dimensional-flow were evaluated in major pulmonary arteries and veins. STATISTICAL TEST: Intraclass correlation coefficient (ICC). RESULTS: The ICC of replicate structural scores was 0.81 (95% CI: 0.45-0.95) across three observers. Elevated Ochiai scores, VDP, and QDP were observed in two NICU participants. Excluding these participants, mean ± standard deviation structural scores were 1.2 ± 0.8, while VDP and QDP were 1.0% ± 1.1% and 0.4% ± 0.5%, respectively. Main pulmonary arterial blood flows normalized to body surface area were 3.15 ± 0.78 L/min/m2 . DATA CONCLUSION: Structural and functional pulmonary imaging is feasible using standard clinical MRI hardware (commercial whole-body 3 T scanner, table spine array, and flexible thoracic array) in free-breathing infants. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Improved value of whole-lesion histogram analysis on DCE parametric maps for diagnosing small breast cancer (≤ 1 cm).

OBJECTIVES: To determine if whole-lesion histogram analysis on dynamic contrast-enhanced (DCE) parametric maps help to improve the diagnostic accuracy of small suspicious breast lesions (≤ 1 cm). METHODS: This retrospective study included 99 female patients with 114 lesions (40 malignant and 74 benign lesions) suspicious on magnetic resonance imaging (MRI).Two radiologists reviewed all lesions and descripted the morphologic and kinetic characteristics according to BI-RADS by consensus. Whole lesions were segmented on DCE parametric maps (washin and washout), and quantitative histogram features were extracted. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. Diagnostic performance was assessed and compared with that of qualitative BI-RADS assessment and quantitative histogram analysis by ROC analysis. RESULTS: For malignancy defined as a washout or plateau pattern, the qualitative kinetic pattern showed a significant difference between the two groups (p = 0.023), yielding an AUC of 0.603 (95% confidence interval [CI]: 0.507, 0.694). The mean and median of washout were independent quantitative predictors of malignancy (p = 0.002, 0.010), achieving an AUC of 0.796 (95% CI: 0. 709, 0.865). The AUC of the quantitative model was better than that of the qualitative model (p < 0.001). CONCLUSIONS: Compared with the qualitative BI-RADS assessment, quantitative whole-lesion histogram analysis on DCE parametric maps was better to discriminate between small benign and malignant breast lesions (≤ 1 cm) initially defined as suspicious on DCE-MRI. KEY POINTS: • For malignancy defined as a washout or plateau, the kinetic pattern may provide information to diagnose small breast cancer. • The mean and median of washout map were significantly lower for small malignant breast lesions than for benign lesions. • Quantitative histogram analysis on MRI parametric maps improves diagnostic accuracy for small breast cancer, which may obviate unnecessary biopsy.

Simultaneous evaluation of perfusion and morphology using GRASP MRI in hepatic fibrosis.

OBJECTIVES: To determine if golden-angle radial sparse parallel (GRASP) dynamic contrast-enhanced (DCE)-MRI allows simultaneous evaluation of perfusion and morphology in liver fibrosis. METHODS: Participants who were scheduled for liver biopsy or resection were enrolled (NCT02480972). Images were reconstructed at 12-s temporal resolution for morphologic assessment and at 3.3-s temporal resolution for quantitative evaluation. The image quality of the morphologic images was assessed on a four-point scale, and the Liver Imaging Reporting and Data System score was recorded for hepatic observations. Comparisons were made between quantitative parameters of DCE-MRI for the different fibrosis stages, and for hepatocellular carcinoma (HCCs) with different LR features. RESULTS: DCE-MRI of 64 participants (male = 48) were analyzed. The overall image quality consistently stood at 3.5 ± 0.4 to 3.7 ± 0.4 throughout the exam. Portal blood flow significantly decreased in participants with F2-F3 (n = 18, 175 ± 110 mL/100 mL/min) and F4 (n = 12, 98 ± 47 mL/100 mL/min) compared with those in participants with F0-F1 (n = 34, 283 ± 178 mL/100 mL/min, p < 0.05 for all). In participants with F4, the arterial fraction and extracellular volume were significantly higher than those in participants with F0-F1 and F2-F3 (p < 0.05). Compared with HCCs showing non-LR-M features (n = 16), HCCs with LR-M (n = 5) had a significantly prolonged mean transit time and lower arterial blood flow (p < 0.05). CONCLUSIONS: Liver MRI using GRASP obtains both sufficient spatial resolution for confident diagnosis and high temporal resolution for pharmacokinetic modeling. Significant differences were found between the MRI-derived portal blood flow at different hepatic fibrosis stages. KEY POINTS: A single MRI examination is able to provide both images with sufficient spatial resolution for anatomic evaluation and those with high temporal resolution for pharmacokinetic modeling. Portal blood flow was significantly lower in clinically significant hepatic fibrosis and mean transit time and extracellular volume increased in cirrhosis, compared with those in no or mild hepatic fibrosis. HCCs with different LR features showed different quantitative parameters of DCE-MRI: longer mean transit time and lower arterial flow were observed in HCCs with LR-M features.