Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Aug 10, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

325

Thin-Slice Prostate MRI Enabled by Deep Learning Image Reconstruction.

Sebastian Gassenmaier1, Verena Warm2, Dominik Nickel3

  • 1Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany.

Cancers
|February 11, 2023
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Impact of Deep-Learning Reconstruction on MRI Workflows: A Retrospective Analysis at a Large Academic Tertiary Center.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin·2026
Same author

Clinical Evaluation of Deep Learning-Reconstructed Postcontrast 3D T1-Weighted Volume Interpolated Breath-Hold Examination (VIBE) Compared with Standard VIBE for Detection of Internal Auditory Canal Lesions.

AJNR. American journal of neuroradiology·2026
Same author

Photon Counting CT of the Head: Retrospective Analysis of Image Quality and Dose in Comparison to Energy Integrating Detector CT.

Academic radiology·2026
Same author

Deep learning-based denoising in cardiac CT: effects on image quality, calcium scoring interchangeability, and reporting workflow.

Frontiers in radiology·2026
Same author

Assessment of Robustness of MRI Radiomic Features in Four Abdominal Organs: Impact of Deep Learning Reconstruction and Segmentation.

Journal of magnetic resonance imaging : JMRI·2026
Same author

3D Brachial Plexus Neurography With Variable-Rate Selective Excitation RF Pulses.

Journal of magnetic resonance imaging : JMRI·2026
Same journal

Correction: Adeluola et al. Chemoprevention of 4-NQO-Induced Oral Cancer by the Combination of Resveratrol and EGCG: In Vivo, In Silico and In Vitro Studies. <i>Cancers</i> 2026, <i>18</i>, 1098.

Cancers·2026
Same journal

Correction: Peñalver et al. Guidelines for Diagnosis, Treatment, and Follow-Up of Patients with Follicular Lymphoma-Spanish Lymphoma Group (GELTAMO) 2026. <i>Cancers</i> 2026, <i>18</i>, 395.

Cancers·2026
Same journal

Correction: Accorsi Buttini et al. Development of a Simplified Geriatric Score-4 (SGS-4) to Predict Outcomes After Allogeneic Hematopoietic Stem Cell Transplantation in Patients Aged over 50. <i>Cancers</i> 2025, <i>17</i>, 3278.

Cancers·2026
Same journal

Age-Stratified Long-Term Outcomes of Immune Checkpoint Inhibitors for Stage IV Melanoma and NSCLC in The Netherlands: A Population-Based Study.

Cancers·2026
Same journal

Targeting Ferroptosis in Glioblastoma: Molecular Mechanisms, Tumor Microenvironment, and Therapeutic Opportunities.

Cancers·2026
Same journal

Neoadjuvant Immunotherapy-Based Treatment Versus Chemotherapy Alone in Resectable Locally Advanced dMMR/MSI-H Gastric Cancer: A Real-World Study with Meta-Analysis.

Cancers·2026
See all related articles
This summary is machine-generated.

Deep learning accelerated thin-slice MRI (T2DLR) enhances prostate cancer diagnostics by improving image quality and lesion detection without increasing scan time. This advanced technique offers superior sharpness and overall image quality compared to conventional MRI (T2S).

Area of Science:

  • Radiology
  • Medical Imaging
  • Oncology

Background:

  • Thin-slice prostate MRI can improve cancer diagnostics.
  • Prolonged acquisition time is a limitation of thin-slice MRI.
  • Deep learning acceleration offers a potential solution to reduce scan times.

Purpose of the Study:

  • To evaluate the impact of a thin-slice deep learning accelerated T2-weighted (T2DLR) sequence on prostate MRI.
  • To compare T2DLR with conventional T2-weighted TSE imaging (T2S) regarding image quality and diagnostic performance.
  • To assess the effect of T2DLR on acquisition time.

Main Methods:

  • A prospective study included 30 patients undergoing prostate MRI.
  • Conventional T2S (3 mm slices) was acquired, followed by thin-slice T2DLR (2 mm slices).
Keywords:
MRIdeep learningimage reconstructionprostatethin-slice

More Related Videos

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
09:11

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

21.6K
3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

9.9K

Related Experiment Videos

Last Updated: Aug 10, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

325
Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
09:11

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

21.6K
3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

9.9K
  • Radiologists assessed image sharpness, lesion detectability, artifacts, overall quality, and diagnostic confidence using a Likert scale.

    • Main Results:

    • T2DLR demonstrated superior sharpness and lesion detectability compared to T2S (median 4 vs. 3, p < 0.001).
    • Overall image quality was significantly better with T2DLR (median 4 vs. 3, p < 0.001).
    • Despite slightly longer acquisition time (4:37 min vs. 4:12 min), both readers preferred T2DLR in 29 cases.

    Conclusions:

    • Thin-slice T2DLR significantly improves prostate MRI image quality and lesion detection.
    • This deep learning accelerated sequence achieves these improvements without a significant increase in acquisition time.
    • T2DLR represents a promising advancement for prostate cancer diagnostics.