Inverse modelling techniques for medical image reconstruction problems
Uma Bhardwajvc@niu.edu.inDepartment of Biotechnology and MicrobiologyNoida International UniversityGreater Noida, Uttar Pradesh, 201312, IndiaView full profile → , Sushama Patwardhanpatwardhan.sushma@gmail.comDepartment of Electronics & TelecommunicationIndira College of Engineering & ManagementPune, Maharashtra, 410506, IndiaView full profile → , *Dinesh PotdarCorresponding authorpotdard_kvv@outlook.comDepartment of Radio-DiagnosisKrishna Institute of Medical SciencesKrishna Vishwa Vidyapeeth (Deemed to be University)Karad, Maharashtra, 415539, IndiaView full profile → , Chirag R. Trivedichiragkumar.trivedi270034@paruluniversity.ac.inDepartment of Applied Sciences and HumanitiesFaculty of Engineering and TechnologyParul UniversityVadodara, Gujarat, 391760, IndiaView full profile → , Abhiraj Malhotraabhiraj.malhotra.orp@chitkara.edu.inCentre of Research Impact and Outcome (CRIO)Chitkara UniversityRajpura, Punjab, 140417, IndiaView full profile → , Adya Kinkar Pandaakpanda@soa.ac.inDepartment of RadiologyInstitute of Medical Sciences & SUM Hospital (IMS and SH)Siksha ‘O’ Anusandhan (Deemed to be University)Bhubaneswar, Odisha, 751030, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2026
- Published Online:
- 27 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIM-2811
- Pages:
- 2957–2968
Abstract
Inverse modelling techniques have been introduced as a key tool in medical image reconstruction to reconstruct internal anatomical and physiological structures from indirect measurement data from imaging systems. This paper summarizes some of the mathematical background, computational techniques and recent advances in inverse modelling for computed tomography, magnetic resonance imaging, positron emission tomography and ultrasound imaging inverse problems. It introduces forward and inverse imaging model, the difficulties with ill-posed inverse problems and the significance of regularization techniques to stable and exact solutions. Besides, analytical reconstruction methods and deep learning enabled inverse models are comparatively compared for their reconstruction quality, computational efficiency, robustness and clinical applicability. The study demonstrates that the mathematical optimization combined with data-driven learning leads to a substantial improvement of the accuracy of the reconstruction, yet it also diminishes the noise, artifacts, and imaging acquisition time, which can lead to more reliable, efficient, and clinically interpretable medical imaging systems for advanced healthcare applications.
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References
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