Purpose: To compare signal-to-noise ratios (S/N) and contrast-to-noise ratios (C/N) in various MR sequences, including fat-suppressed three-dimensional spoiled gradient-echo (SPGR) imaging, fat-suppressed fast spin echo (FSE) imaging, and fat-suppressed three-dimensional driven equilibrium Fourier transform (DEFT) imaging, and to determine the diagnostic accuracy of these imaging sequences for detecting cartilage lesions in osteoarthritic knees, as compared with arthroscopy.
Materials And Methods: Two sagittal fat-suppressed FSE images (repetition time [TR] / echo time [TE], 4000/13 [FSE short TE] and 4000/39 [FSE long TE]), sagittal fat-suppressed three-dimensional SPGR images (60/5, 40 degrees flip angle), and sagittal fat-suppressed echo-planar three-dimensional DEFT images (400/21.2) were acquired in 35 knees from 28 patients with osteoarthritis of the knee. The S/N efficiencies (S/Neffs) of cartilage, synovial fluid, muscle, meniscus, bone marrow, and fat tissue, and the C/N efficiencies (C/Neffs) of these structures were calculated. Kappa values, exact agreement, sensitivity, specificity, positive predictive value, and negative predictive value were determined by comparison of MR grading with arthroscopic results.
Results: The synovial fluid S/Neff on fat-suppressed FSE short TE images, fat-suppressed FSE long TE images, and fat-suppressed three-dimensional DEFT images showed similar values. Fat-suppressed three-dimensional DEFT images showed the highest fluid-cartilage C/Neff of all sequences. All images showed fair to good agreement with arthroscopy (kappa, 0.615 in FSE short TE, 0.601 in FSE long TE, 0.583 in three-dimensional SPGR, and 0.561 in three-dimensional DEFT). Although the sensitivity of all sequences was high (100% in FSE short TE, FSE long TE, and DEFT; 96.7% in SPGR), specificity was relatively low (67.6% in FSE short TE and FSE long TE; 85.3% in SPGR; 58.3% in DEFT). The peripheral area of bone marrow edema or whole area of bone marrow edema on fat-suppressed FSE images was demonstrated as low or iso-signal intensity on fat-suppressed three-dimensional DEFT images.
Conclusion: Fat-suppressed three-dimensional SPGR imaging and fat-suppressed FSE imaging showed high sensitivity and high negative predictive values, but relatively low specificity. The Kappa value and exact agreement was the highest on fat-suppressed FSE short TE images. Fat-suppressed three-dimensional DEFT images showed results similar to the conventional sequences.
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http://dx.doi.org/10.1002/jmri.20193 | DOI Listing |
Quant Imaging Med Surg
December 2024
Department of Medical Imaging, the People's Hospital of Liuyang, Liuyang, China.
Background: Anal fistula is a common anorectal disorder that significantly diminishes the quality of life for affected patients. Accurate preoperative evaluation of the fistula's traits is essential for customizing surgical strategies, improving patient outcomes, and reducing the likelihood of the disease returning. This study aimed to evaluate the diagnostic accuracy of multi-phase contrast-enhanced fat-suppressed T1-weighted imaging using three-dimensional gradient echo sequence volumetric interpolated breath-hold examination (CE-FS-T1-3D-VIBE) and fat-suppressed T2-weighted imaging combined with diffusion-weighted imaging (FS-T2WI-DWI) sequence in delineating the characteristics of anal fistulas.
View Article and Find Full Text PDFNihon Hoshasen Gijutsu Gakkai Zasshi
December 2024
Radiation Technology Department, National Cancer Center Hospital East.
Purpose: This study aimed to compare the compressed SENSE (C-SENSE) accelerated fat-suppressed 3D-turbo spin echo (TSE) method and the conventional SENSE accelerated fat-suppressed 3D-TSE method to examine the usefulness of C-SENSE technology in reducing imaging time.
Methods: Fat-suppressed 3D-TSE using either C-SENSE or SENSE technology was utilized to capture consecutive preoperative images of 34 patients with tongue cancer. SNR, CNR, and visual evaluation were then used to compare both types of technology.
Oncology
October 2024
Department of Radiology, Keio University School of Medicine, Shinjuku, Japan.
Introduction: The integration of artificial intelligence (AI) into orthopedics has enhanced the diagnosis of various conditions; however, its use in diagnosing soft-tissue tumors remains limited owing to its complexity. This study aimed to develop and assess an AI-driven diagnostic support system for magnetic resonance imaging (MRI)-based soft-tissue tumor diagnosis, potentially improving accuracy and aiding radiologists and orthopedic surgeons.
Methods: An experienced orthopedic oncologist and radiologist annotated 720 images from 77 cases (41 benign and 36 malignant soft-tissue tumors).
Diagn Interv Imaging
December 2024
Department of Neuroradiology, Fondation Adolphe de Rothschild Hospital, 75019 Paris, France; Université Paris Cité, Faculté de Médecine, 75006 Paris, France.
J Magn Reson Imaging
August 2024
Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Background: Pathological axillary lymph node (pALN) burden is an important factor for treatment decision-making in clinical T1-T2 (cT1-T2) stage breast cancer. Preoperative assessment of the pALN burden and prognosis aids in the individualized selection of therapeutic approaches.
Purpose: To develop and validate a machine learning (ML) model based on clinicopathological and MRI characteristics for assessing pALN burden and survival in patients with cT1-T2 stage breast cancer.
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