Feasibility of deep learning reconstruction in abdominal CT: Optimization and validation

Feasibility of deep learning reconstruction in abdominal CT: Optimization and validation - Feasibility of deep learning reconstruction in abdominal CT: Optimization and validation

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000037471

Enrollment

300

Registered

2020-04-02

Start date

2020-04-02

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Patients with abnormal laboratory test results or upper abdominal symptoms that raised suspicions of abdominal malignancy underwent dynamic CT.

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Our inclusion criteria were history or suspicion of abdominal tumor, such as hepatocellular carcinoma, cholangiocellular carcinoma, hemangioma, or metastatic liver tumor.

Exclusion criteria

Exclusion criteria: Our exclusion criteria were as follows:emergency case, history of an adverse reaction to iodinated contrast media, proved or suspected pregnancy, and no history of hepatic surgery, TAE, RFA, or renal surgery.

Design outcomes

Primary

Measure	Time frame
Objective assessment of image quality	—

Countries

Japan

Contacts

Public ContactAkio Tamura

Iwate Medical University Depertment of Radiology

a.akahane@gmail.com0196515111

Outcome results