Bringing MRI to low- and middle-income countries: Directions, challenges and potential solutions.

The global disparity of magnetic resonance imaging (MRI) is a major challenge, with many low- and middle-income countries (LMICs) experiencing limited access to MRI. The reasons for limited access are technological, economic and social. With the advancement of MRI technology, we explore why these challenges still prevail, highlighting the importance of MRI as the epidemiology of disease changes in LMICs. In this paper, we establish a framework to develop MRI with these challenges in mind and discuss the different aspects of MRI development, including maximising image quality using cost-effective components, integrating local technology and infrastructure and implementing sustainable practices. We also highlight the current solutions-including teleradiology, artificial intelligence and doctor and patient education strategies-and how these might be further improved to achieve greater access to MRI.

Low-field MRI: A report on the 2022 ISMRM workshop.

In March 2022, the first ISMRM Workshop on Low-Field MRI was held virtually. The goals of this workshop were to discuss recent low field MRI technology including hardware and software developments, novel methodology, new contrast mechanisms, as well as the clinical translation and dissemination of these systems. The virtual Workshop was attended by 368 registrants from 24 countries, and included 34 invited talks, 100 abstract presentations, 2 panel discussions, and 2 live scanner demonstrations. Here, we report on the scientific content of the Workshop and identify the key themes that emerged. The subject matter of the Workshop reflected the ongoing developments of low-field MRI as an accessible imaging modality that may expand the usage of MRI through cost reduction, portability, and ease of installation. Many talks in this Workshop addressed the use of computational power, efficient acquisitions, and contemporary hardware to overcome the SNR limitations associated with low field strength. Participants discussed the selection of appropriate clinical applications that leverage the unique capabilities of low-field MRI within traditional radiology practices, other point-of-care settings, and the broader community. The notion of "image quality" versus "information content" was also discussed, as images from low-field portable systems that are purpose-built for clinical decision-making may not replicate the current standard of clinical imaging. Speakers also described technical challenges and infrastructure challenges related to portability and widespread dissemination, and speculated about future directions for the field to improve the technology and establish clinical value.

On-site construction of a point-of-care low-field MRI system in Africa.

PURPOSE: To describe the construction and testing of a portable point-of-care low-field MRI system on site in Africa. METHODS: All of the components to assemble a 50 mT Halbach magnet-based system, together with the necessary tools, were air-freighted from the Netherlands to Uganda. The construction steps included individual magnet sorting, filling of each ring of the magnet assembly, fine-tuning the inter-ring separations of the 23-ring magnet assembly, gradient coil construction, integration of gradient coils and magnet assembly, construction of the portable aluminum trolley and finally testing of the entire system with an open source MR spectrometer. RESULTS: With four instructors and six untrained personnel, the complete project from delivery to first image took approximately 11 days. CONCLUSIONS: An important step in translating scientific developments in the western world from high-income industrialized countries to low- and middle-income countries (LMICs) is to produce technology that can be assembled and ultimately constructed locally. Local assembly and construction are associated with skill development, low costs and jobs. Point-of-care systems have a large potential to increase the accessibility and sustainability of MRI in LMICs, and this work demonstrates that technology and knowledge transfer can be performed relatively seamlessly.

A framework for advancing sustainable magnetic resonance imaging access in Africa.

Magnetic resonance imaging (MRI) technology has profoundly transformed current healthcare systems globally, owing to advances in hardware and software research innovations. Despite these advances, MRI remains largely inaccessible to clinicians, patients, and researchers in low-resource areas, such as Africa. The rapidly growing burden of noncommunicable diseases in Africa underscores the importance of improving access to MRI equipment as well as training and research opportunities on the continent. The Consortium for Advancement of MRI Education and Research in Africa (CAMERA) is a network of African biomedical imaging experts and global partners, implementing novel strategies to advance MRI access and research in Africa. Upon its inception in 2019, CAMERA sets out to identify challenges to MRI usage and provide a framework for addressing MRI needs in the region. To this end, CAMERA conducted a needs assessment survey (NAS) and a series of symposia at international MRI society meetings over a 2-year period. The 68-question NAS was distributed to MRI users in Africa and was completed by 157 clinicians and scientists from across Sub-Saharan Africa (SSA). On average, the number of MRI scanners per million people remained at less than one, of which 39% were obsolete low-field systems but still in use to meet daily clinical needs. The feasibility of coupling stable energy supplies from various sources has contributed to the growing number of higher-field (1.5 T) MRI scanners in the region. However, these systems are underutilized, with only 8% of facilities reporting clinical scans of 15 or more patients per day, per scanner. The most frequently reported MRI scans were neurological and musculoskeletal. The CAMERA NAS combined with the World Health Organization and International Atomic Energy Agency data provides the most up-to-date data on MRI density in Africa and offers a unique insight into Africa's MRI needs. Reported gaps in training, maintenance, and research capacity indicate ongoing challenges in providing sustainable high-value MRI access in SSA. Findings from the NAS and focused discussions at international MRI society meetings provided the basis for the framework presented here for advancing MRI capacity in SSA. While these findings pertain to SSA, the framework provides a model for advancing imaging needs in other low-resource settings.

Sustainable low-field cardiovascular magnetic resonance in changing healthcare systems.

Cardiovascular disease continues to be a major burden facing healthcare systems worldwide. In the developed world, cardiovascular magnetic resonance (CMR) is a well-established non-invasive imaging modality in the diagnosis of cardiovascular disease. However, there is significant global inequality in availability and access to CMR due to its high cost, technical demands as well as existing disparities in healthcare and technical infrastructures across high-income and low-income countries. Recent renewed interest in low-field CMR has been spurred by the clinical need to provide sustainable imaging technology capable of yielding diagnosticquality images whilst also being tailored to the local populations and healthcare ecosystems. This review aims to evaluate the technical, practical and cost considerations of low field CMR whilst also exploring the key barriers to implementing sustainable MRI in both the developing and developed world.

Approaches in cooling of resistive coil-based low-field Magnetic Resonance Imaging (MRI) systems for application in low resource settings.

Magnetic Resonance Imaging (MRI), a non-invasive method for the diagnosis of diverse health conditions has experienced growing popularity over other imaging modalities like ultrasound and Computer Tomography. Initially, proof-of-concept and earlier MRI systems were based on resistive and permanent magnet technology. However, superconducting magnets have long held monopoly of the market for MRI systems with their high-field (HF) strength capability, although they present high construction, installation, and siting requirements. Such stringent prerequisites restrict their availability and use in low-middle income countries. Resistive coil-based magnet, albeit low-field (LF) in capacity, represent a plausible boost for the availability and use of MRI systems in resource constrained settings. These systems are characterized by low costs coupled with substantial image quality for diagnosis of some conditions such as hydrocephalus common is such regions. However, the nature of resistive coils causes them to heat up during operation, thus necessitating a dedicated cooling system to improve image quality and enhance system longevity. This paper explores a range of cooling methods as have been applied to resistive magnets, citing their pros and cons and areas for improvement.

An Unmatched Radio Frequency Chain for Low-Field Magnetic Resonance Imaging.

Magnetic Resonance Imaging (MRI) is a safe and versatile diagnostic tool for intracranial imaging, however it is also one of the most expensive and specialized making it scarce in low- to middle-income countries (LMIC). The affordability and portability of low-field MRI offers the potential for increased access to brain imaging for diseases like Hydrocephalus in LMIC. In this tutorial style work, we show the design of a low powered and low cost radio frequency chain of electronics to be paired with a previously reported prepolarized low-field MRI for childhood hydrocephalus imaging in sub-Saharan Africa where the incidence of this condition is high. Since the Larmor frequency for this system is as low as 180 kHz, we are able to minimize the impedance of the transmit coil to 5 ohms rather than match to 50 ohms as is traditionally the case. This reduces transmit power consumption by a factor of 10. We also show the use of inexpensive and commonly available animal enclosure fencing ("chicken wire") as a shield material at this frequency and compare to more traditional shield designs. These preliminary results show that highly portable and affordable low-field MRI systems could provide image resolution and signal-to-noise sufficient for planning hydrocephalus treatment in areas of the world with substantial resource limitations. Employment of these technologies in sub-Saharan Africa offers a cost-effective, sustainable approach to neurological diagnosis and treatment planning in this disease burdened region.

Adaptive-size dictionary learning using information theoretic criteria for image reconstruction from undersampled k-space data in low field magnetic resonance imaging.

BACKGROUND: Magnetic resonance imaging (MRI) is a safe non-invasive and nonionizing medical imaging modality that is used to visualize the structure of human anatomy. Conventional (high-field) MRI scanners are very expensive to purchase, operate and maintain, which limit their use in many developing countries. This study is part of a project that aims at addressing these challenges and is carried out by teams from Mbarara University of Science and Technology (MUST) in Uganda, Leiden University Medical Center (LUMC) in the Netherlands, Delft University of Technology (TU Delft) in the Netherlands and Pennsylvania State University (PSU) in the USA. These are working on developing affordable, portable and low-field MRI scanners to diagnose children in developing countries with hydrocephalus. The challenges faced by the teams are that the low-field MRI scanners currently under development are characterized by low Signal-to-Noise Ratio (SNR), and long scan times. METHODS: We propose an algorithm called adaptive-size dictionary learning algorithm (AS-DLMRI) that integrates information-theoretic criteria (ITC) and Dictionary learning approaches. The result of the integration is an adaptive-size dictionary that is optimal for any input signal. AS-DLMRI may help to reduce the scan time and improve the SNR of the generated images, thereby improving the image quality. RESULTS: We compared our proposed algorithm AS-DLMRI with adaptive patch-based algorithm known as DLMRI and non-adaptive CSMRI technique known as LDP. DLMRI and LDP have been used as the baseline algorithms in other related studies. The results of AS-DLMRI are consistently slightly better in terms of PSNR, SNR and HFEN than for DLMRI, and are significantly better than for LDP. Moreover, AS-DLMRI is faster than DLMRI. CONCLUSION: Using a dictionary size that is appropriate to the input data could reduce the computational complexity, and also the construction quality since only dictionary atoms that are relevant to the task are included in the dictionary and are used during the reconstruction. However, AS-DLMRI did not completely remove noise during the experiments with the noisy phantom. Our next step in our research is to integrate our proposed algorithm with an image denoising function.

A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images.

BACKGROUND: Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images. METHOD: Scene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm. RESULTS: The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of '98.88%, 99.28% and 97.47%', '97.64%, 98.08% and 97.16%' and '95.00%, 100% and 90.00%' were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab. CONCLUSIONS: The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5-10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.

A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images.

BACKGROUND AND OBJECTIVE: Early diagnosis and classification of a cancer type can help facilitate the subsequent clinical management of the patient. Cervical cancer ranks as the fourth most prevalent cancer affecting women worldwide and its early detection provides the opportunity to help save life. To that end, automated diagnosis and classification of cervical cancer from pap-smear images has become a necessity as it enables accurate, reliable and timely analysis of the condition's progress. This paper presents an overview of the state of the art as articulated in prominent recent publications focusing on automated detection of cervical cancer from pap-smear images. METHODS: The survey reviews publications on applications of image analysis and machine learning in automated diagnosis and classification of cervical cancer from pap-smear images spanning 15 years. The survey reviews 30 journal papers obtained electronically through four scientific databases (Google Scholar, Scopus, IEEE and Science Direct) searched using three sets of keywords: (1) segmentation, classification, cervical cancer; (2) medical imaging, machine learning, pap-smear; (3) automated system, classification, pap-smear. RESULTS: Most of the existing algorithms facilitate an accuracy of nearly 93.78% on an open pap-smear data set, segmented using CHAMP digital image software. K-nearest-neighbors and support vector machines algorithms have been reported to be excellent classifiers for cervical images with accuracies of over 99.27% and 98.5% respectively when applied to a 2-class classification problem (normal or abnormal). CONCLUSION: The reviewed papers indicate that there are still weaknesses in the available techniques that result in low accuracy of classification in some classes of cells. Moreover, most of the existing algorithms work either on single or on multiple cervical smear images. This accuracy can be increased by varying various parameters such as the features to be extracted, improvement in noise removal, using hybrid segmentation and classification techniques such of multi-level classifiers. Combining K-nearest-neighbors algorithm with other algorithm(s) such as support vector machines, pixel level classifications and including statistical shape models can also improve performance. Further, most of the developed classifiers are tested on accurately segmented images using commercially available software such as CHAMP software. There is thus a deficit of evidence that these algorithms will work in clinical settings found in developing countries (where 85% of cervical cancer incidences occur) that lack sufficient trained cytologists and the funds to buy the commercial segmentation software.

Design of a sustainable prepolarizing magnetic resonance imaging system for infant hydrocephalus.

OBJECTIVES: The need for affordable and appropriate medical technologies for developing countries continues to rise as challenges such as inadequate energy supply, limited technical expertise, and poor infrastructure persist. Low-field magnetic resonance imaging (LF MRI) is a technology that can be tailored to meet specific imaging needs within such countries. Its low power requirements and the possibility of operating in minimally shielded or unshielded environments make it especially attractive. Although the technology has been widely demonstrated over several decades, it is yet to be shown that it can be diagnostic and improve patient outcomes in clinical applications. We here demonstrate the robustness of prepolarizing MRI (PMRI) technology for assembly and deployment in developing countries for the specific application to infant hydrocephalus. Hydrocephalus treatment planning and management requires only modest spatial resolution, such that the brain can be distinguished from fluid-tissue contrast detail within the brain parenchyma is not essential. MATERIALS AND METHODS: We constructed an internally shielded PMRI system based on the Lee-Whiting coil system with a 22-cm diameter of spherical volume. RESULTS: In an unshielded room, projection phantom images were acquired at 113 kHz with in-plane resolution of 3 mm × 3 mm, by introducing gradient fields of sufficient magnitude to dominate the 5000 ppm inhomogeneity of the readout field. DISCUSSION: The low cost, straightforward assembly, deployment potential, and maintenance requirements demonstrate the suitability of our PMRI system for developing countries. Further improvement in image spatial resolution and contrast of LF MRI will broaden its potential clinical utility beyond hydrocephalus.