1
|
O'Connor LM, Quinn A, Denley S, Leigh L, Martin J, Dowling JA, Skehan K, Warren-Forward H, Greer PB. Cone beam computed tomography image guidance within a magnetic resonance imaging-only planning workflow. Phys Imaging Radiat Oncol 2023; 27:100472. [PMID: 37720461 PMCID: PMC10500022 DOI: 10.1016/j.phro.2023.100472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/06/2023] [Accepted: 07/06/2023] [Indexed: 09/19/2023] Open
Abstract
Background and purpose Magnetic Resonance Imaging (MRI)-only planning workflows offer many advantages but raises challenges regarding image guidance. The study aimed to assess the viability of MRI to Cone Beam Computed Tomography (CBCT) based image guidance for MRI-only planning treatment workflows. Materials and methods An MRI matching training package was developed. Ten radiation therapists, with a range of clinical image guidance experience and experience with MRI, completed the training package prior to matching assessment. The matching assessment was performed on four match regions: prostate gold seed, prostate soft tissue, rectum/anal canal and gynaecological. Each match region consisted of five patients, with three CBCTs per patient, resulting in fifteen CBCTs for each match region. The ten radiation therapists performed the CBCT image matching to CT and to MRI for all regions and recorded the match values. Results The median inter-observer variation for MRI-CBCT matching and CT-CBCT matching for all regions were within 2 mm and 1 degree. There was no statistically significant association in the inter-observer variation in mean match values and radiation therapist image guidance experience levels. There was no statistically significant association in inter-observer variation in mean match values for MRI experience levels for prostate soft tissue and gynaecological match regions, while there was a statistically significant difference for prostate gold seed and rectum match regions. Conclusion The results of this study support the concept that with focussed training, an MRI to CBCT image guidance approach can be successfully implemented in a clinical planning workflow.
Collapse
Affiliation(s)
- Laura M O'Connor
- Department of Radiation Oncology, Calvary Mater Hospital, Edith Street, Waratah, Newcastle, NSW 2298, Australia
- School of Health Sciences, University of Newcastle, University Drive, Newcastle, NSW 2308, Australia
| | - Alesha Quinn
- Department of Radiation Oncology, Calvary Mater Hospital, Edith Street, Waratah, Newcastle, NSW 2298, Australia
| | - Samuel Denley
- Department of Radiation Oncology, Calvary Mater Hospital, Edith Street, Waratah, Newcastle, NSW 2298, Australia
| | - Lucy Leigh
- Hunter Medical Research Institute, Lot 1 Kookaburra Ct, New Lambton Heights, NSW 2305, Australia
| | - Jarad Martin
- Department of Radiation Oncology, Calvary Mater Hospital, Edith Street, Waratah, Newcastle, NSW 2298, Australia
| | - Jason A Dowling
- Australian E-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Bowen Bridge Rd, Herston, QLD 4029, Australia
| | - Kate Skehan
- Department of Radiation Oncology, Calvary Mater Hospital, Edith Street, Waratah, Newcastle, NSW 2298, Australia
| | - Helen Warren-Forward
- School of Health Sciences, University of Newcastle, University Drive, Newcastle, NSW 2308, Australia
| | - Peter B Greer
- Department of Radiation Oncology, Calvary Mater Hospital, Edith Street, Waratah, Newcastle, NSW 2298, Australia
- School of Information and Physical Sciences, University of Newcastle, University Drive, Newcastle, NSW 2308, Australia
| |
Collapse
|
2
|
Bierbrier J, Gueziri HE, Collins DL. Estimating medical image registration error and confidence: A taxonomy and scoping review. Med Image Anal 2022; 81:102531. [PMID: 35858506 DOI: 10.1016/j.media.2022.102531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 06/16/2022] [Accepted: 07/01/2022] [Indexed: 11/18/2022]
Abstract
Given that image registration is a fundamental and ubiquitous task in both clinical and research domains of the medical field, errors in registration can have serious consequences. Since such errors can mislead clinicians during image-guided therapies or bias the results of a downstream analysis, methods to estimate registration error are becoming more popular. To give structure to this new heterogenous field we developed a taxonomy and performed a scoping review of methods that quantitatively and automatically provide a dense estimation of registration error. The taxonomy breaks down error estimation methods into Approach (Image- or Transformation-based), Framework (Machine Learning or Direct) and Measurement (error or confidence) components. Following the PRISMA guidelines for scoping reviews, the 570 records found were reduced to twenty studies that met inclusion criteria, which were then reviewed according to the proposed taxonomy. Trends in the field, advantages and disadvantages of the methods, and potential sources of bias are also discussed. We provide suggestions for best practices and identify areas of future research.
Collapse
Affiliation(s)
- Joshua Bierbrier
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, Montreal, QC, Canada.
| | - Houssem-Eddine Gueziri
- McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - D Louis Collins
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| |
Collapse
|
3
|
Guo Y, Chen C. An Orthopedic Auxiliary Diagnosis System Based on Image Recognition Technology. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4644392. [PMID: 34853668 PMCID: PMC8629649 DOI: 10.1155/2021/4644392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/30/2021] [Accepted: 11/01/2021] [Indexed: 11/26/2022]
Abstract
There are many kinds of orthopedic diseases with complex professional background, and it is easy to miss diagnosis and misdiagnosis. The computer-aided diagnosis system of orthopedic diseases based on the key technology of medical image processing can locate and display the lesion location area by visualization, measuring and providing disease diagnosis indexes. It is of great significance to assist orthopedic doctors to diagnose orthopedic diseases from the perspective of visual vision and quantitative indicators, which can improve the diagnosis rate and accuracy of orthopedic diseases, reduce the pain of patients, and shorten the treatment time of diseases. To solve the problem of possible spatial inconsistency of medical images of orthopedic diseases, we propose an image registration method based on volume feature point selection and Powell. Through the linear search strategy of golden section method and Powell algorithm optimization, the best spatial transformation parameters are found, which maximizes the normalized mutual information between images to be registered, thus ensuring the consistency of two-dimensional spatial positions. According to the proposed algorithm, a computer-aided diagnosis system of orthopedic diseases is developed and designed independently. The system consists of five modules, which can complete many functions such as medical image input and output, algorithm processing, and effect display. The experimental results show that the system developed in this paper has good results in cartilage tissue segmentation, bone and urate agglomeration segmentation, urate agglomeration artifact removal, two-dimensional and three-dimensional image registration, and visualization. The system can be applied to clinical gout and cartilage defect diagnosis and evaluation, providing sufficient basis to assist doctors in making diagnosis decisions.
Collapse
Affiliation(s)
- Yang Guo
- First Affiliated Hospital of Xiamen University, Xiamen 361000, China
| | - Chen Chen
- First Affiliated Hospital of Xiamen University, Xiamen 361000, China
| |
Collapse
|
4
|
Bird D, Speight R, Al-Qaisieh B, Henry AM. Magnetic Resonance Imaging Simulation for Anal and Rectal Cancer - Optimising the Patient Experience. Clin Oncol (R Coll Radiol) 2021; 33:e422-e424. [PMID: 33992498 DOI: 10.1016/j.clon.2021.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 03/26/2021] [Accepted: 04/23/2021] [Indexed: 11/20/2022]
Affiliation(s)
- D Bird
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK; Radiotherapy Research Group, Leeds Institute of Medical Research, University of Leeds, Leeds, UK.
| | - R Speight
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - B Al-Qaisieh
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - A M Henry
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK; Radiotherapy Research Group, Leeds Institute of Medical Research, University of Leeds, Leeds, UK
| |
Collapse
|
5
|
Bird D, Beasley M, Nix MG, Tyyger M, McCallum H, Teo M, Gilbert A, Casanova N, Cooper R, Buckley DL, Sebag-Montefiore D, Speight R, Henry AM, Al-Qaisieh B. Patient position verification in magnetic-resonance imaging only radiotherapy of anal and rectal cancers. Phys Imaging Radiat Oncol 2021; 19:72-77. [PMID: 34307922 PMCID: PMC8295842 DOI: 10.1016/j.phro.2021.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 07/02/2021] [Accepted: 07/02/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND AND PURPOSE Magnetic resonance (MR)-only treatment pathways require either the MR-simulation or synthetic-computed tomography (sCT) as an alternative reference image for cone beam computed tomography (CBCT) patient position verification. This study assessed whether using T2 MR or sCT as CBCT reference images introduces systematic registration errors as compared to CT for anal and rectal cancers. MATERIALS AND METHODS A total of 32 patients (18 rectum,14 anus) received pre-treatment CT- and T2 MR- simulation. Routine treatment CBCTs were acquired. sCTs were generated using a validated research model. The local clinical registration protocol, using a grey-scale registration algorithm, was performed for 216 CBCTs using CT, MR and sCT as the reference image. Linear mixed effects modelling identified systematic differences between modalities. RESULTS Systematic translation and rotation differences to CT for MR were -0.3 to + 0.3 mm and -0.1 to 0.4° for anal cancers and -0.4 to 0.0 mm and 0.0 to 0.1° for rectal cancers, and for sCT were -0.4 to + 0.8 mm, -0.1 to 0.2° for anal cancers and -0.6 to + 0.2 mm, -0.1 to + 0.1° for rectal cancers. CONCLUSIONS T2 MR or sCT can successfully be used as reference images for anal and rectal cancer CBCT position verification with systematic differences to CT <±1 mm and <±0.5°. Clinical enabling of alternative modalities as reference images by vendors is required to reduce challenges associated with their use.
Collapse
Affiliation(s)
- David Bird
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Radiotherapy Research Group, Leeds Institute of Medical Research, UK
| | - Matthew Beasley
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Michael G. Nix
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Marcus Tyyger
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Hazel McCallum
- Northern Centre for Cancer Care, Newcastle Upon Tyne Hospitals NHS Foundation Trust, UK
- Centre for Cancer, Newcastle University, UK
| | - Mark Teo
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Alexandra Gilbert
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Radiotherapy Research Group, Leeds Institute of Medical Research, UK
| | | | - Rachel Cooper
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | | | - David Sebag-Montefiore
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Radiotherapy Research Group, Leeds Institute of Medical Research, UK
| | - Richard Speight
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Ann M. Henry
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Radiotherapy Research Group, Leeds Institute of Medical Research, UK
| | | |
Collapse
|