Impact of Deep Learning Denoising Algorithm on Diffusion Tensor Imaging of the Growth Plate on Different Spatial Resolutions.

To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions, this IRB-approved prospective study was conducted at a tertiary pediatric center in compliance with the Health Insurance Portability and Accountability Act. A General Electric Signa Premier unit (GE Medical Systems, Milwaukee, WI) was employed to acquire two DTI (diffusion tensor imaging) sequences of the left knee on each child at 3T: an in-plane 2.0 × 2.0 mm2 with section thickness of 3.0 mm and a 2 mm3 isovolumetric voxel; neither had an intersection gap. For image acquisition, a multi-band DTI with a fat-suppressed single-shot spin-echo echo-planar sequence (20 non-collinear directions; b-values of 0 and 600 s/mm2) was utilized. The MR vendor-provided a commercially available DL model which was applied with 75% noise reduction settings to the same subject DTI sequences at different spatial resolutions. We compared DTI tract metrics from both DL-reconstructed scans and non-denoised scans for the femur and tibia at each spatial resolution. Differences were evaluated using Wilcoxon-signed ranked test and Bland-Altman plots. When comparing DL versus non-denoised diffusion metrics in femur and tibia using the 2 mm × 2 mm × 3 mm voxel dimension, there were no significant differences between tract count (p = 0.1, p = 0.14) tract volume (p = 0.1, p = 0.29) or tibial tract length (p = 0.16); femur tract length exhibited a significant difference (p < 0.01). All diffusion metrics (tract count, volume, length, and fractional anisotropy (FA)) derived from the DL-reconstructed scans, were significantly different from the non-denoised scan DTI metrics in both the femur and tibial physes using the 2 mm3 voxel size (p < 0.001). DL reconstruction resulted in a significant decrease in femorotibial FA for both voxel dimensions (p < 0.01). Leveraging denoising algorithms could address the drawbacks of lower signal-to-noise ratios (SNRs) associated with smaller voxel volumes and capitalize on their better spatial resolutions, allowing for more accurate quantification of diffusion metrics.

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

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

Surgical margin and local recurrence of ductal carcinoma in situ.

PURPOSE: This study aims to evaluate the association between surgical margin status and local recurrence of DCIS. METHODS: A retrospective analysis of a prospectively maintained 20-year DCIS database was performed. >=2 mm margin was defined as clear margin. Local relapse rate between the patients with clear versus close margins were analyzed with Kaplan-Meier analyses. RESULTS: 654 patients were analyzed. Median age was 46.5 (Range 18 - 80). 205 (31.3%) were high grade, 194 (29.7%) were intermediate grade, 143 (21.9%) were low grade. 112 (18.3%) were unknown. 202 (30.9%) were estrogen receptor positive, 49 (7.4%) were negative, 403 (61.6%) patients were unknown. 403 (61.6%) patients received mastectomy while 251 (38.4%) patients received BCS and radiotherapy. 549 (83.9%) patients had clear surgical margin, 50 (7.7%) patients had involved (positive) resection margin, 55 (8.4%) had close margin (<2 mm margin). All patients with involved margin received re-excision of margin, while 21 patients (out of 55 who had close resection margins) received re-excision of margin. Negative surgical margins were achieved after the re-excision. 34 patients with close resection margin decided not to receive re-excision but to undergo adjuvant radiotherapy. After median follow-up of 128 months, the 10-year ipsilateral breast tumor relapse (IBTR) was 4.5% (N = 28), Of which 27 (96.4%) patients had clear margin after the initial surgical treatment of DCIS. 1 (3.6%) patient had close surgical margin. Difference in IBTR between the two groups was not statistically significant (p = 0.692). CONCLUSION: Close surgical margin for DCIS is not associated with increased risk of IBTR.

Utility of Zero-Echo time sequence as an adjunct for MR evaluation of degenerative disease in the cervical spine.

OBJECTIVE: Determine the utility of ZTE as an adjunct to routine MR for assessing degenerative disease in the cervical spine. METHODS: Retrospective study on 42 patients with cervical MR performed with ZTE from 1/1/2022 to 4/30/22. Fellowship trained radiologists evaluated each cervical disc level for neural foraminal (NF) narrowing, canal stenosis (CS), facet arthritis (FA), and presence of ossification of the posterior longitudinal ligament (OPLL). When NF narrowing and CS were present, the relative contributions of bone and soft disc were determined and a confidence level for doing so was assigned. Comparisons were made between assessments on routine MR without and with ZTE. RESULTS: With ZTE added, bone contribution as a cause of NF narrowing increased in 47% (n = 110) of neural foramina and decreased in 12% (n = 29) (p = < 0.001). Bone contribution as a cause of CS increased in 25% (n = 33) of disc levels and decreased in 10% (n = 13) (p = 0.013). Confidence increased in identifying the cause of NF narrowing (p = < 0.001)) and CS (p = 0.009) with ZTE. The cause of NF narrowing (p = 0.007) and CS (p = 0.041) changed more frequently after ZTE was added when initial confidence in making the determination was low. There was no change in detection of FA or presence of OPLL with ZTE. CONCLUSION: Addition of ZTE to a routine cervical spine MR changes the assessment of the degree of bone involvement in degenerative cervical spine pathology.

Diagnostic Utility of Restriction Spectrum Imaging in Head and Neck Tumors: A Pilot Study.

OBJECTIVE: Imaging is crucial in the assessment of head and neck cancers for site, extension, and enlarged lymph nodes. Restriction spectrum imaging (RSI) is a new diffusion-weighted magnetic resonance imaging (MRI) technique that enhances the ability to differentiate aggressive cancer from low-grade or benign tumors and helps guide treatment and biopsy. Its contribution to imaging of brain and prostate tumors has been previously published. However, there are no prior studies using RSI sequence in head and neck tumors. The purpose of this study was to evaluate the feasibility of performing RSI in head and neck cancer. METHODS: An additional RSI sequence was added in the routine MRI neck protocol for 13 patients diagnosed with head and neck cancer between November 2018 and April 2019. Restriction spectrum imaging sequence was performed with b values of 0, 500, 1500, and 3000 s/mm 2 and 29 directions on 1.5T magnetic resonance scanners.Diffusion-weighted imaging (DWI) images and RSI images were compared according to their ability to detect the primary malignancy and possible metastatic lymph nodes. RESULTS: In 71% of the patients, RSI outperformed DWI in detecting the primary malignancy and possible metastatic lymph nodes, whereas in the remaining cases, the 2 were comparable. In 66% of the patients, RSI detected malignant lymph nodes that DWI/apparent diffusion coefficient failed to detect. CONCLUSIONS: This is the first study of RSI in head and neck imaging and showed its superiority over the conventional DWI sequence. Because of its ability to differentiate benign and malignant lymph nodes in some cases, the addition of RSI to routine head and neck MRI should be considered.

UTE T2* cartilage mapping in the hip: a pilot study assessing cartilage in patients with femoroacetabular impingement.

BACKGROUND: UTE T2* cartilage mapping use in patients undergoing femoroacetabular impingement (FAI) has been lacking but may allow the detection of early cartilage damage. PURPOSE: To assess the reproducibility of UTE T2* cartilage mapping and determine the difference in UTE T2* values between FAI and asymptomatic patients and to evaluate the correlation between UTE T2* values and patient-reported symptoms. MATERIAL AND METHODS: Prospective evaluation of both hips (7 FAI and 7 asymptomatic patients). Bilateral hip 3-T MRI scans with UTE T2* cartilage maps were acquired. A second MRI scan was acquired 1-9 months later. Cartilage was segmented into anterosuperior, superior, and posterosuperior regions. Assessment was made of UTE T2* reproducibility (ICC). Mean UTE T2* values in patients were compared (t-tests) and correlation was made with patient-reported outcomes (Spearman's). RESULTS: ICCs of mean UTE T2* were as follows: acetabular, 0.82 (95% CI=0.50-0.95); femoral, 0.76 (95% CI=0.35-0.92). Significant strong correlation was found between mean acetabular UTE T2* values and iHOT12 (ρ = -0.63) and moderate correlation with mHHS (ρ = -0.57). There was no difference in mean UTE T2* values between affected vs. non-affected FAI hips. FAI-affected hips had significantly higher values in acetabulum vs. asymptomatic patients (13.47 vs. 12.55 ms). There was no difference in mean femoral cartilage values between the FAI-affected hips vs. asymptomatic patients. The posterosuperior femoral region had a higher mean value in non-affected FAI hips vs. asymptomatic patients (12.60 vs. 11.53 ms). CONCLUSION: UTE T2* cartilage mapping had excellent reproducibility. Affected FAI hips had higher mean acetabular UTE T2* values than asymptomatic patients. Severity of patient-reported symptoms correlates with UTE T2* acetabular cartilage values.

Human prostate MRI at ultrahigh-performance gradient: A feasibility study.

PURPOSE: To demonstrate the technical feasibility and the value of ultrahigh-performance gradient in imaging the prostate in a 3T MRI system. METHODS: In this local institutional review board-approved study, prostate MRI was performed on 4 healthy men. Each subject was scanned in a prototype 3T MRI system with a 42-cm inner-diameter gradient coil that achieves a maximum gradient amplitude of 200 mT/m and slew rate of 500 T/m/s. PI-RADS V2.1-compliant axial T2 -weighted anatomical imaging and single-shot echo planar DWI at standard gradient of 70 mT/m and 150 T/m/s were obtained, followed by DWI at maximum performance (i.e., 200 mT/m and 500 T/m/s). In comparison to state-of-the-art clinical whole-body MRI systems, the high slew rate improved echo spacing from 1020 to 596 µs and, together with a high gradient amplitude for diffusion encoding, TE was reduced from 55 to 36 ms. RESULTS: In all 4 subjects (waist circumference = 81-91 cm, age = 45-65 years), no peripheral nerve stimulation sensation was reported during DWI. Reduced image distortion in the posterior peripheral zone prostate gland and higher signal intensity, such as in the surrounding muscle of high-gradient DWI, were noted. CONCLUSION: Human prostate MRI at simultaneously high gradient amplitude of 200 mT/m and slew rate of 500 T/m/s is feasible, demonstrating that improved gradient performance can address image distortion and T2 decay-induced SNR issues for in vivo prostate imaging.

Accelerated MRI using intelligent protocolling and subject-specific denoising applied to Alzheimer's disease imaging.

Magnetic Resonance Imaging (MR Imaging) is routinely employed in diagnosing Alzheimer's Disease (AD), which accounts for up to 60-80% of dementia cases. However, it is time-consuming, and protocol optimization to accelerate MR Imaging requires local expertise since each pulse sequence involves multiple configurable parameters that need optimization for contrast, acquisition time, and signal-to-noise ratio (SNR). The lack of this expertise contributes to the highly inefficient utilization of MRI services diminishing their clinical value. In this work, we extend our previous effort and demonstrate accelerated MRI via intelligent protocolling of the modified brain screen protocol, referred to as the Gold Standard (GS) protocol. We leverage deep learning-based contrast-specific image-denoising to improve the image quality of data acquired using the accelerated protocol. Since the SNR of MR acquisitions depends on the volume of the object being imaged, we demonstrate subject-specific (SS) image-denoising. The accelerated protocol resulted in a 1.94 × gain in imaging throughput. This translated to a 72.51% increase in MR Value-defined in this work as the ratio of the sum of median object-masked local SNR values across all contrasts to the protocol's acquisition duration. We also computed PSNR, local SNR, MS-SSIM, and variance of the Laplacian values for image quality evaluation on 25 retrospective datasets. The minimum/maximum PSNR gains (measured in dB) were 1.18/11.68 and 1.04/13.15, from the baseline and SS image-denoising models, respectively. MS-SSIM gains were: 0.003/0.065 and 0.01/0.066; variance of the Laplacian (lower is better): 0.104/-0.135 and 0.13/-0.143. The GS protocol constitutes 44.44% of the comprehensive AD imaging protocol defined by the European Prevention of Alzheimer's Disease project. Therefore, we also demonstrate the potential for AD-imaging via automated volumetry of relevant brain anatomies. We performed statistical analysis on these volumetric measurements of the hippocampus and amygdala from the GS and accelerated protocols, and found that 27 locations were in excellent agreement. In conclusion, accelerated brain imaging with the potential for AD imaging was demonstrated, and image quality was recovered post-acquisition using DL-based image denoising models.

Tailored magnetic resonance fingerprinting of post-operative pediatric brain tumor patients.

PURPOSE: Brain and spinal cord tumors are the second most common cancer in children and account for one out of four cancers diagnosed. However, the long acquisition times associated with acquiring both data types prohibit using quantitative MR (qMR) in pediatric imaging protocols. This study aims to demonstrate the tailored magnetic resonance fingerprinting's (TMRF) ability to simultaneously provide quantitative maps (T1, T2) and multi-contrast qualitative images (T1 weighted, T1 FLAIR, T2 weighted) rapidly in pediatric brain tumor patients. METHODS: In this work, we imaged five pediatric patients with brain tumors (resected/residual) using TMRF at 3 T. We compared the TMRF-derived T2 weighted images with those from the vendor-supplied sequence (as the gold standard, GS) for healthy and pathological tissue signal intensities. The relaxometric maps from TMRF were subjected to a region of interest (ROI) analysis to differentiate between healthy and pathological tissues. We performed the Wilcoxon rank sum test to check for significant differences between the two tissue types. RESULTS: We found significant differences (p < 0.05) in both T1 and T2 ROI values between the two tissue types. A strong correlation was found between the TMRF-based T2 weighted and GS signal intensities for the healthy (correlation coefficient, r = 0.99) and pathological tissues (r = 0.88). CONCLUSION: The TMRF implementation provides the two relaxometric maps and can potentially save ~2 min if it replaces the T2-weighted imaging in the current protocol.

Magnetic resonance fingerprinting based thermometry (MRFT): application toex vivoimaging near DBS leads.

The purpose of this study is to demonstrate the first work ofT1-based magnetic resonance thermometry using magnetic resonance fingerprinting (dubbed MRFT). We compared temperature estimation of MRFT with proton resonance frequency shift (PRFS) thermometry onex vivobovine muscle. We demonstrated MRFT's feasibility in predicting temperature onex vivobovine muscles with deep brain stimulation (DBS) lead.B0maps generated from MRFT were compared with gold standardB0maps near the DBS lead. MRFT and PRFS estimated temperatures were compared in the presence of motion. All experiments were performed on a 3 Tesla whole-body GE Premier system with a 21-channel receive head coil (GE Healthcare, Milwaukee, WI). Four fluoroptic probes were used to measure the temperature at the center of a cold muscle (probe 1), the room temperature water bottle (probe 2), and the center and periphery of the heated muscle (probes 3 and 4). We selected regions of interest (ROIs) around the location of the probes and used simple linear regression to generate the temperature sensitivity calibration equations that convertT1maps and Δsmaps to temperature maps. We then repeated the same setup and compared MRFT and PRFS thermometry temperature estimation with gold standard probe measurements. For the MRFT experiment on DBS lead, we taped the probe to the tip of the DBS lead and used a turbo spin echo sequence to induce heating near the lead. We selected ROIs around the tip of the lead to compare MRFT temperature estimation with probe measurements and compared with PRFS temperature estimation. Vendor-suppliedB0mapping sequence was acquired to compare with MRFT-generatedB0maps. We found strong linear relationships (R2> 0.958) betweenT1and temperature and Δsand temperatures in our temperature sensitivity calibration experiment. MRFT and PRFS thermometry both accurately predict temperature (RMSE < 1.55 °C) compared to probe measurements. MRFT estimated temperature near DBS lead has a similar trend as the probe temperature. BothB0maps show inhomogeneities around the lead. MRFT estimated temperature is less sensitive to motion.

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

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

Tailored magnetic resonance fingerprinting.

Neuroimaging of certain pathologies requires both multi-parametric qualitative and quantitative imaging. The role of the quantitative MRI (qMRI) is well accepted but suffers from long acquisition times leading to patient discomfort, especially in geriatric and pediatric patients. Previous studies show that synthetic MRI can be used in order to reduce the scan time and provide qMRI as well as multi-contrast data. However, this approach suffers from artifacts such as partial volume and flow. In order to increase the scan efficiency (the number of contrasts and quantitative maps acquired per unit time), we designed, simulated, and demonstrated rapid, simultaneous, multi-contrast qualitative (T1 weighted, T1 fluid attenuated inversion recovery (FLAIR), T2 weighted, water, and fat), and quantitative imaging (T1 and T2 maps) through the approach of tailored MR fingerprinting (TMRF) to cover whole-brain in approximately four minutes. We performed TMRF on in vivo four healthy human brains and in vitro ISMRM/NIST phantom and compared with vendor supplied gold standard (GS) and MRF sequences. All scans were performed on a 3 T GE Premier system and images were reconstructed offline using MATLAB. The reconstructed qualitative images were then subjected to custom DL denoising and gradient anisotropic diffusion denoising. The quantitative tissue parametric maps were reconstructed using a dense neural network to gain computational speed compared to dictionary matching. The grey matter and white matter tissues in qualitative and quantitative data for the in vivo datasets were segmented semi-automatically. The SNR and mean contrasts were plotted and compared across all three methods. The GS images show better SNR in all four subjects compared to MRF and TMRF (GS > TMRF>MRF). The T1 and T2 values of MRF are relatively overestimated as compared to GS and TMRF. The scan efficiency for TMRF is 1.72 min-1 which is higher compared to GS (0.32 min-1) and MRF (0.90 min-1).

GRASPNET: Fast spatiotemporal deep learning reconstruction of golden-angle radial data for free-breathing dynamic contrast-enhanced magnetic resonance imaging.

The purpose of the current study was to develop a deep learning technique called Golden-angle RAdial Sparse Parallel Network (GRASPnet) for fast reconstruction of dynamic contrast-enhanced 4D MRI acquired with golden-angle radial k-space trajectories. GRASPnet operates in the image-time space and does not use explicit data consistency to minimize the reconstruction time. Three different network architectures were developed: (1) GRASPnet-2D: 2D convolutional kernels (x,y) and coil and contrast dimensions collapsed into a single combined dimension; (2) GRASPnet-3D: 3D kernels (x,y,t); and (3) GRASPnet-2D + time: two 3D kernels to first exploit spatial correlations (x,y,1) followed by temporal correlations (1,1,t). The networks were trained using iterative GRASP reconstruction as the reference. Free-breathing 3D abdominal imaging with contrast injection was performed on 33 patients with liver lesions using a T1-weighted golden-angle stack-of-stars pulse sequence. Ten datasets were used for testing. The three GRASPnet architectures were compared with iterative GRASP results using quantitative and qualitative analysis, including impressions from two body radiologists. The three GRASPnet techniques reduced the reconstruction time to about 13 s with similar results with respect to iterative GRASP. Among the GRASPnet techniques, GRASPnet-2D + time compared favorably in the quantitative analysis. Spatiotemporal deep learning enables reconstruction of dynamic 4D contrast-enhanced images in a few seconds, which would facilitate translation to clinical practice of compressed sensing methods that are currently limited by long reconstruction times.

Quantitative Relaxometry Metrics for Brain Metastases Compared to Normal Tissues: A Pilot MR Fingerprinting Study.

The purpose of the present pilot study was to estimate T1 and T2 metric values derived simultaneously from a new, rapid Magnetic Resonance Fingerprinting (MRF) technique, as well as to assess their ability to characterize-brain metastases (BM) and normal-appearing brain tissues. Fourteen patients with BM underwent MRI, including prototype MRF, on a 3T scanner. In total, 108 measurements were analyzed: 42 from solid parts of BM's (21 each on T1 and T2 maps) and 66 from normal-appearing brain tissue (11 ROIs each on T1 and T2 maps for gray matter [GM], white matter [WM], and cerebrospinal fluid [CSF]). The BM's mean T1 and T2 values differed significantly from normal-appearing WM (p < 0.05). The mean T1 values from normal-appearing GM, WM, and CSF regions were 1205 ms, 840 ms, and 4233 ms, respectively. The mean T2 values were 108 ms, 78 ms, and 442 ms, respectively. The mean T1 and T2 values for untreated BM (n = 4) were 2035 ms and 168 ms, respectively. For treated BM (n = 17) the T1 and T2 values were 2163 ms and 141 ms, respectively. MRF technique appears to be a promising and rapid quantitative method for the characterization of free water content and tumor morphology in BMs.

Qualitative and Quantitative Performance of Magnetic Resonance Image Compilation (MAGiC) Method: An Exploratory Analysis for Head and Neck Imaging.

The present exploratory study investigates the performance of a new, rapid, synthetic MRI method for diagnostic image quality assessment and measurement of relaxometry metric values in head and neck (HN) tumors and normal-appearing masseter muscle. The multi-dynamic multi-echo (MDME) sequence was used for data acquisition, followed by synthetic image reconstruction on a 3T MRI scanner for 14 patients (3 untreated and 11 treated). The MDME enables absolute quantification of physical tissue properties, including T1 and T2, with a shorter scan time than the current state-of-the-art methods used for relaxation measurements. The vendor termed the combined package MAGnetic resonance imaging Compilation (MAGiC). In total, 48 regions of interest (ROIs) were analyzed, drawn on normal-appearing masseter muscle and tumors in the HN region. Mean T1 and T2 values obtained from normal-appearing muscle were 880 ± 52 ms and 46 ± 3 ms, respectively. Mean T1 and T2 values obtained from tumors were 1930 ± 422 ms and 77 ± 13 ms, respectively, for the untreated group, 1745 ± 410 ms and 107 ± 61 ms, for the treated group. A total of 1552 images from both synthetic MRI and conventional clinical imaging were assessed by the radiologists to provide the rating for T1w and T2w image contrasts. The synthetically generated qualitative T2w images were acceptable and comparable to conventional diagnostic images (93% acceptability rating for both). The acceptability ratings for MAGiC-generated T1w, and conventional images were 64% and 100%, respectively. The benefit of MAGiC in HN imaging is twofold, providing relaxometry maps in a clinically feasible time and the ability to generate a different combination of contrast images in a single acquisition.

Quantitative Synthetic Magnetic Resonance Imaging for Brain Metastases: A Feasibility Study.

The present preliminary study aims to characterize brain metastases (BM) using T1 and T2 maps generated from newer, rapid, synthetic MRI (MAGnetic resonance image Compilation; MAGiC) in a clinical setting. We acquired synthetic MRI data from 11 BM patients on a 3T scanner. A multiple-dynamic multiple-echo (MDME) sequence was used for data acquisition and synthetic image reconstruction, including post-processing. MDME is a multi-contrast sequence that enables absolute quantification of physical tissue properties, including T1 and T2, independent of the scanner settings. In total, 82 regions of interest (ROIs) were analyzed, which were obtained from both normal-appearing brain tissue and BM lesions. The mean values obtained from the 48 normal-appearing brain tissue regions and 34 ROIs of BM lesions (T1 and T2) were analyzed using standard statistical methods. The mean T1 and T2 values were 1143 ms and 78 ms, respectively, for normal-appearing gray matter, 701 ms and 64 ms for white matter, and 4206 ms and 390 ms for cerebrospinal fluid. For untreated BMs, the mean T1 and T2 values were 1868 ms and 100 ms, respectively, and 2211 ms and 114 ms for the treated group. The quantitative T1 and T2 values generated from synthetic MRI can characterize BM and normal-appearing brain tissues.

Multiecho complex total field inversion method (mcTFI) for improved signal modeling in quantitative susceptibility mapping.

PURPOSE: Typical quantitative susceptibility mapping (QSM) reconstruction steps consist of first estimating the magnetization field from the gradient-echo images, and then reconstructing the susceptibility map from the estimated field. The errors from the field-estimation steps may propagate into the final QSM map, and the noise in the estimated field map may no longer be zero-mean Gaussian noise, thus, causing streaking artifacts in the resulting QSM. A multiecho complex total field inversion (mcTFI) method was developed to compute the susceptibility map directly from the multiecho gradient echo images using an improved signal model that retains the Gaussian noise property in the complex domain. It showed improvements in QSM reconstruction over the conventional field-to-source inversion. METHODS: The proposed mcTFI method was compared with the nonlinear total field inversion (nTFI) method in a numerical brain with hemorrhage and calcification, the numerical brains provided by the QSM Challenge 2.0, 18 brains with intracerebral hemorrhage scanned at 3T, and 6 healthy brains scanned at 7T. RESULTS: Compared with nTFI, the proposed mcTFI showed more accurate QSM reconstruction around the lesions in the numerical simulations. The mcTFI reconstructed QSM also showed the best image quality with the least artifacts in the brains with intracerebral hemorrhage scanned at 3T and healthy brains scanned at 7T. CONCLUSION: The proposed multiecho complex total field inversion improved QSM reconstruction over traditional field-to-source inversion through better signal modeling.

Quantitative imaging metrics derived from magnetic resonance fingerprinting using ISMRM/NIST MRI system phantom: An international multicenter repeatability and reproducibility study.

PURPOSE: To compare the bias and inherent reliability of the quantitative (T1 and T2 ) imaging metrics generated from the magnetic resonance fingerprinting (MRF) technique using the ISMRM/NIST system phantom in an international multicenter setting. METHOD: ISMRM/NIST MRI system phantom provides standard reference T1 and T2 relaxation values (vendor-provided) for each of the 14 vials in T1 and T2 arrays. MRF-SSFP scans repeated over 30 days on GE 1.5 and 3.0 T scanners at three collaborative centers. MRF estimated T1, and T2 values averaged over 30 days were compared with the phantom vendor-provided and spin-echo (SE) based convention gold standard (GS) method. Repeatability and reproducibility were characterized by the within-case coefficient of variation (wCV) of the MRF data acquired over 30 days, along with the biases. RESULT: For the wide ranges of MRF estimated T1 values, vials #1-8 (T1 relaxation time between 2033 and 184 ms) exhibited a wCV less than 3% and 4%, respectively, on 3.0 and 1.5 T scanners. T2 values in vials #1-8 (T2 relaxation, 1044-45 ms) have shown wCV to be <7% on both 3.0 and 1.5 T scanners. A stronger linear correlation overall for T1 (R2  = 0.9960 and 0.9963 at center-1 and center-2 on 3.0 T scanner, and R2  = 0.9951 and 0.9988 at center-1 and center-3 on 1.5 T scanner) compared to T2 (R2  = 0.9971 and 0.9972 at center-1 and center-2 on 3.0 T scanner, and R2  = 0.9815 and 0.9754 at center-1 and center-3 on 1.5 T scanner). Bland-Altman (BA) analysis showed MRF based T1 and T2 values were within the limit of agreement (LOA) except for one data point. The mean difference or bias and 95% lower bound (LB) and upper bound (UB) LOA are reported in the format; mean bias: 95% LB LOA: 95% UB LOA. The biases for T1 values were 21.34: -50.00: 92.69, 21.32: -47.29: 89.94 ms, and for T2 values were -19.88: -42.37: 2.61, -19.06: -43.58: 5.45 ms on 3.0 T scanner at center-1 and center-2, respectively. Similarly, on 1.5 T scanner biases for T1 values were 26.54: -53.41: 106.50, 9.997: -51.94: 71.94 ms, and for T2 values were -23.84: -135.40: 87.76, -37.30: 134.30: 59.73 ms at center-1 and center-3, respectively. Additionally, the correlation between the SE based GS and MRF estimated T1 and T2 values (R2  = 0.9969 and 0.9977) showed a similar trend as we observed between vendor-provided and MRF estimated T1 and T2 values (R2  = 0.9963 and 0.9972). In addition to correlation, BA analysis showed that all the vials are within the LOA between the GS and vendor-provided for the T1 values and except one vial for T2 . All the vials are within the LOA between GS and MRF except one vial in T1 and T2 array. The wCV for reproducibility was <3% for both T1 and T2 values in vials #1-8, for all the 14 vials, wCV calculated for reproducibility was <4% for T1 values and <5% for T2 . CONCLUSION: This study shows that MRF is highly repeatable (wCV <4% for T1 and <7% for T2 ) and reproducible (wCV < 3% for both T1 and T2 ) in certain vials (vials #1-8).

Accuracy of Magnetic Resonance Imaging-Guided Biopsy to Verify Breast Cancer Pathologic Complete Response After Neoadjuvant Chemotherapy: A Nonrandomized Controlled Trial.

Importance: After neoadjuvant chemotherapy (NAC), pathologic complete response (pCR) is an optimal outcome and a surrogate end point for improved disease-free and overall survival. To date, surgical resection remains the only reliable method for diagnosing pCR. Objective: To evaluate the accuracy of magnetic resonance imaging (MRI)-guided biopsy for diagnosing a pCR after NAC compared with reference-standard surgical resection. Design, Setting, and Participants: Single-arm, phase 1, nonrandomized controlled trial in a single tertiary care cancer center from September 26, 2017, to July 29, 2019. The median follow-up was 1.26 years (interquartile range, 0.85-1.59 years). Data analysis was performed in November 2019. Eligible patients had (1) stage IA to IIIC biopsy-proven operable invasive breast cancer; (2) standard-of-care NAC; (3) MRI before and after NAC, with imaging complete response defined as no residual enhancement on post-NAC MRI; and (4) definitive surgery. Patients were excluded if they were younger than 18 years, had a medical reason precluding study participation, or had a prior history of breast cancer. Interventions: Post-NAC MRI-guided biopsy without the use of intravenous contrast of the tumor bed before definitive surgery. Main Outcomes and Measures: The primary end point was the negative predictive value of MRI-guided biopsy, with true-negative defined as negative results of the biopsy (ie, no residual cancer) corresponding to a surgical pCR. Accuracy, sensitivity, positive predictive value, and specificity were also calculated. Two clinical definitions of pCR were independently evaluated: definition 1 was no residual invasive cancer; definition 2, no residual invasive or in situ cancer. Results: Twenty of 23 patients (87%) had evaluable data (median [interquartile range] age, 51.5 [39.0-57.5] years; 20 women [100%]; 13 White patients [65%]). Of the 20 patients, pre-NAC median tumor size on MRI was 3.0 cm (interquartile range, 2.0-5.0 cm). Nineteen of 20 patients (95%) had invasive ductal carcinoma; 15 of 20 (75%) had stage II cancer; 11 of 20 (55%) had ERBB2 (formerly HER2 or HER2/neu)-positive cancer; and 6 of 20 (30%) had triple-negative cancer. Surgical pathology demonstrated a pCR in 13 of 20 (65%) patients and no pCR in 7 of 20 patients (35%) when pCR definition 1 was used. Results of MRI-guided biopsy had a negative predictive value of 92.8% (95% CI, 66.2%-99.8%), with accuracy of 95% (95% CI, 75.1%-99.9%), sensitivity of 85.8% (95% CI, 42.0%-99.6%), positive predictive value of 100%, and specificity of 100% for pCR definition 1. Only 1 patient had a false-negative MRI-guided biopsy result (surgical pathology showed <0.02 cm of residual invasive cancer). Conclusions and Relevance: This study's results suggest that the accuracy of MRI-guided biopsy to diagnose a post-NAC pCR approaches that of reference-standard surgical resection. MRI-guided biopsy may be a viable alternative to surgical resection for this population after NAC, which supports the need for further investigation. Trial Registration: ClinicalTrials.gov Identifier: NCT03289195.

Accelerating Prostate Diffusion-weighted MRI Using a Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study.

PURPOSE: To investigate the feasibility of accelerating prostate diffusion-weighted imaging (DWI) by reducing the number of acquired averages and denoising the resulting image using a proposed guided denoising convolutional neural network (DnCNN). MATERIALS AND METHODS: Raw data from the prostate DWI scans were retrospectively gathered between July 2018 and July 2019 from six single-vendor MRI scanners. There were 103 datasets used for training (median age, 64 years; interquartile range [IQR], 11), 15 for validation (median age, 68 years; IQR, 12), and 37 for testing (median age, 64 years; IQR, 12). High b-value diffusion-weighted (hb DW) data were reconstructed into noisy images using two averages and reference images using all 16 averages. A conventional DnCNN was modified into a guided DnCNN, which uses the low b-value DW image as a guidance input. Quantitative and qualitative reader evaluations were performed on the denoised hb DW images. A cumulative link mixed regression model was used to compare the readers' scores. The agreement between the apparent diffusion coefficient (ADC) maps (denoised vs reference) was analyzed using Bland-Altman analysis. RESULTS: Compared with the original DnCNN, the guided DnCNN produced denoised hb DW images with higher peak signal-to-noise ratio (32.79 ± 3.64 [standard deviation] vs 33.74 ± 3.64), higher structural similarity index (0.92 ± 0.05 vs 0.93 ± 0.04), and lower normalized mean square error (3.9% ± 10 vs 1.6% ± 1.5) (P < .001 for all). Compared with the reference images, the denoised images received higher image quality scores from the readers (P < .0001). The ADC values based on the denoised hb DW images were in good agreement with the reference ADC values (mean ADC difference ranged from -0.04 to 0.02 × 10-3 mm2/sec). CONCLUSION: Accelerating prostate DWI by reducing the number of acquired averages and denoising the resulting image using the proposed guided DnCNN is technically feasible. Supplemental material is available for this article. © RSNA, 2020.