Among member countries of the Organization of Economic Co-operation and Development (OECD), Japan has the highest density of magnetic resonance imaging (MRI) units. Over 57 such units are available per every million of its population. The United States and Greece both follow with rates of around 38 per million inhabitants. Compared to these countries, Mexico had around three MRI units per every million, The density of diagnostic imaging units can be one measurement to define the quality of a country’s health care infrastructure. Why and when MRI is usedThe invention of MRI scanners revolutionized diagnostic imaging as it doesn’t use radiation, but a magnetic field and radio waves. Since ionized radiation as used in CT-scans and X-rays is potentially harmful for the patient, this includes a significant advantage for MRIs. MRI scans are principally used for imaging organs, soft tissues, ligaments, and other parts of the body which are difficult to see. While on the other hand, computer tomography (CT) scanners are more frequently used to show bony structures. Among the global top manufacturers of MRI scanners are General Electric, Siemens, Hitachi, and Philips. The costs of MRIA single scan per MRI could cost up to 4,000 U.S. dollars, and thus double the cost of a scan with CT. The purchase of an MRI scanner could be a major investment for a practice or a hospital, with prices ranging from 150 thousand dollars up to several million dollars. Of course, there are installation and maintenance costs to be taken into account as well. With nearly 40 million MRI scans performed annually in the United States, it’s clear that diagnostic imaging costs are substantial.

This statistic shows the number of MRI scans in the U.S. in 2016 and 2017, by facility type. In 2016, there was a total number of 39 million MRI scans, whereas in 2017, the number decreased to 36 million.

The number of magnetic resonance imaging (MRI) scanners in the United Kingdom saw no significant changes in 2014 in comparison to the previous year 2013 and remained at around 466.96 scanners. Nevertheless, 2014 still represents a peak in the number of MRI scanners in the United Kingdom with 466.96 scanners. Magnetic resonance imaging (MRI) uses magnetic/electromagnetic fields combined with an induced resonance effect of hydrogen atoms. The result is a visualization of internal organs and body structures. MRI does not use x-rays or ionizing radiation which distinguishes it from CT and PET scans.Find more key insights for the number of magnetic resonance imaging (MRI) scanners in countries like Luxembourg, Netherlands, and Lithuania.

This dataset contains information submitted by New York State Article 28 Hospitals as part of the New York Statewide Planning and Research Cooperative (SPARCS) and Institutional Cost Report (ICR) data submissions. The dataset contains information on the volume of discharges, All Payer Refined Diagnosis Related Group (APR-DRG), the severity of illness level (SOI), medical or surgical classification the median charge, median cost, average charge and average cost per discharge. When interpreting New York’s data, it is important to keep in mind that variations in cost may be attributed to many factors. Some of these include overall volume, teaching hospital status, facility specific attributes, geographic region and quality of care provided. For more information, check out: http://www.health.ny.gov/statistics/sparcs/ or go to the "About" tab.

The wait times for patients from their appointments to receiving magnetic resonance imaging (MRI) are usually long. To reduce this wait time, the present study proposed that service time wastage could be reduced by adjusting MRI examination scheduling by prioritizing patients who require examinations involving the same type of coil. This approach can reduce patient wait times and thereby maximize MRI departments’ service times. To simulate an MRI department’s action workflow, 2,447 MRI examination logs containing the deidentified information of patients and radiation technologists from the MRI department of a medical center were used, and a hybrid simulation model that combined discrete-event and agent-based simulations was developed. The experiment was conducted in two stages. In the first stage, the service time was increased by adjusting the examination schedule and thereby reducing the number of coil changes. In the second stage, the maximum number of additional patients that could be examined daily was determined. The average number of coil changes per day for the four MRI scanners of the aforementioned medical center was reduced by approximately 27. Thus, the MRI department gained 97.17 min/d, which enabled them to examine three additional patients per month. Consequently, the net monthly income of the hospital increased from US$17,067 to US$30,196, and the patient wait times for MRI examinations requiring the use of flexible torso and head, shoulder, 8-inch head, and torso MRI coils were shortened by 6 d and 23 h, 2 d and 15 h, 2 d and 9 h, and 16 h, respectively. Adjusting MRI examination scheduling by prioritizing patients that require the use of the same coil could reduce the coil-setting time, increase the daily number of patients who are examined, increase the net income of the MRI department, and shorten patient wait times for MRI examinations. Minimizing the operating times of specific examinations to maximize the number of services provided per day does not require additional personnel or resources. The results of the experimental simulations can be used as a reference by radiology department managers designing scheduling rules for examination appointments.

Computer tomography (CT) scanners are vital medical technology used in the diagnosis and monitoring of various medical conditions. CT scanner utilize x-ray technology to make images of bones, vessels and other internal organs. As of 2023, Japan had the largest density of CT scanners with 115.7 scanners per million people. The country with the second most scanners at that time was Australia with over 70 scanners per million people. Diagnostic imaging Diagnostic imaging is a branch of medical technology that aims to use advanced technologies to create images of the human body for the purposes of diagnosing and monitoring medical conditions. There are several kinds of imaging available. Magnetic resonance imaging (MRI) is another type of medical imaging common in developed countries. As of 2019 Japan and the U.S. had the largest number of MRI units per million population. Usage of medical imaging also varies significantly among countries with Germany and Austria having the highest rates of examinations by MRI in recent years. Medical technology market globally The medical technology market has been an ever-expanding industry. With segments in diagnostic imaging, cardiology and optometry there is ample opportunities for new technologies to be utilized. The top medical technology segment based on market share was in vitro diagnostics, followed by cardiology and diagnostic imaging. Among medical technology companies Medtronic and Johnson & Johnson were the top two based on worldwide revenue in 2023.

GlobalData’s new report, United States Diagnostic Imaging Market Outlook to 2020, provides key market data on the United States Diagnostic Imaging market. The report provides value, in millions of US dollars, and volume (in units) within market categories – Angio Suites, Bone Densitometers, C-Arms, Computed Tomography Systems, Contrast Media Injectors, Mammography Equipment, MRI Systems, Nuclear Imaging Equipment, Ultrasound Systems and X-ray Systems. The report also provides company shares and distribution shares data for each of these market categories, and global corporate-level profiles of the key market participants, pipeline products, and news and deals related to the Diagnostic Imaging market wherever available. The data in the report is derived from dynamic market forecast models. GlobalData uses capital equipment–based models to estimate and forecast the market size. The objective is to provide information that represents the most up-to-date data of the industry possible. Capital equipment–based forecasting models are done based on the installed base, replacements and new sales of a specific device/equipment in healthcare facilities such as hospitals, clinics and diagnostic centers. Data for average number of units per facility is used to arrive at the installed base of the capital equipment. Sales for a particular year are arrived at by calculating the replacement units and new units (additional and first-time purchases). Extensive interviews are conducted with key opinion leaders (KOLs), physicians and industry experts to validate the market size, company share and distribution share data and analysis. Read More

GlobalData’s new report, Canada Diagnostic Imaging Market Outlook to 2020, provides key market data on the Canada Diagnostic Imaging market. The report provides value, in millions of US dollars, and volume (in units) within market categories – Angio Suites, Bone Densitometers, C-Arms, Computed Tomography Systems, Contrast Media Injectors, Mammography Equipment, MRI Systems, Nuclear Imaging Equipment, Ultrasound Systems and X-ray Systems. The report also provides company shares and distribution shares data for each of these market categories, and global corporate-level profiles of the key market participants, pipeline products, and news and deals related to the Diagnostic Imaging market wherever available. The data in the report is derived from dynamic market forecast models. GlobalData uses capital equipment–based models to estimate and forecast the market size. The objective is to provide information that represents the most up-to-date data of the industry possible. Capital equipment–based forecasting models are done based on the installed base, replacements and new sales of a specific device/equipment in healthcare facilities such as hospitals, clinics and diagnostic centers. Data for average number of units per facility is used to arrive at the installed base of the capital equipment. Sales for a particular year are arrived at by calculating the replacement units and new units (additional and first-time purchases). Extensive interviews are conducted with key opinion leaders (KOLs), physicians and industry experts to validate the market size, company share and distribution share data and analysis. Read More

Participants

Thirty participants (17 females, mean age=23.17±2.31 years) were recruited for the fMRI experiment at Shanghai International Studies University, Shanghai, China. An additional thirty participants (16 females, mean age=22.67±1.99 years) were recruited from the West China Hospital of Sichuan University, Chengdu, China. All participants were right-handed, had normal or corrected-to-normal vision, and reported no history of neurological disorders. Before the experiment, all participants provided written informed consent and were compensated for their participation.

Experiment Procedure

The experimental procedures for both fMRI and MEG experiments were identical. Participants watched the video while inside the scanner. The video was presented via a mirror attached to the head coil in the fMRI and MEG. Audio was delivered through MRI-compatible headphones (Sinorad, Shenzhen, China) during the fMRI experiment and MEG-compatible insert earphones (ComfortBuds 24, Sinorad, Shenzhen, China) during the MEG experiment. Following the video, participants were visually presented with 5 multiple-choice questions on the screen to assess their comprehension and ensure engagement with the stimuli. Participants responded using a button press, with a maximum response time of 10 seconds per question. If no response was recorded within this time, the experiment proceeded to the next question automatically. After the quiz, participants were instructed to close their eyes for 15 minutes without an explicit task. This period allowed for the recording of neural activity, capturing spontaneous mental replay of the video stimulus. The entire experimental procedure lasted approximately 45 minutes per participant.

The fMRI experiment was approved by the Ethics Committee of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior (No. 2024BC028), and the MEG experiment was approved by the West China Hospital of Sichuan University Biomedical Research Ethics Committee (No. 2024[657]).

Stimuli

The video stimulus was extracted from the first episode of the Chinese reality TV show “Where Are We Going, Dad? (Season 1)”, which originally aired in 2013. The show features unscripted interactions between fathers and their child as they travel to a rural village and engage in daily activities. The selected excerpt has a total duration of 25 minutes and 19 seconds. The original video had a resolution of 640×368 pixels with a frame rate of 15 frames per second. It was presented in full-color (RGB) format, without embedded subtitles or captions.

Acquisition

The fMRI data was collected in a 3.0 T Siemens Prisma MRI scanner at Shanghai International Studies University, Shanghai. Anatomical scans were obtained using a Magnetization Prepared RApid Gradient-Echo (MP-RAGE) ANDI iPAT2 pulse sequence with T1-weighted contrast (192 single-shot interleaved sagittal slices with A/P phase encoding direction; voxel size=1×1×1 mm; FOV=256 mm; TR=2300 ms; TE=2.98 ms; TI=900 ms; flip angle=9°; acquisition time=6 min; GRAPPA in-plane acceleration factor=2). Functional scans were acquired using T2-weighted echo planar imaging (63 interleaved axial slices with A/P phase encoding direction, voxel size=2.5×2.5×2.5 mm; FOV=220 mm; TR=2000ms; TE=30 ms; acceleration factor=3; flip angle=60°).

MEG data were recorded at West China Hospital of Sichuan University using a 64-channel optically pumped magnetometer (OPM) MEG system (Quanmag, Beijing, China). OPM-MEG is a new type of MEG instrumentation that offers several advantages over conventional MEG systems. These include higher signal sensitivity, improved spatial resolution, and more uniform scalp coverage. Additionally, OPM-MEG allows for greater participant comfort and compliance, supports free movement during scanning, and features lower system complexity, making it a promising tool for more flexible and accessible neuroimaging. The MEG Data were sampled at 1,000 Hz and bandpass-filtered online between 0 and 500 Hz. To facilitate source localization, T1-weighted MRI scans were acquired from the participants using a 3.0 T Siemens TrioTim MRI scanner at West China Hospital of Sichuan University (176 single-shot interleaved sagittal slices with A/P phase encoding direction; voxel size = 1×1×1 mm; FOV = 256 mm; TR = 1900 ms; TE = 2.3 ms; TI = 900 ms; flip angle = 9°; acquisition time = 7 min).

Preprocessing

All Digital Imaging and Communications in Medicine (DICOM) files of the raw fMRI data were first converted into the Brain Imaging Data Structure (BIDS) format using dcm2bids (v3.1.1) and subsequently transformed into Neuroimaging Informatics Technology Initiative (NIfTI) format via dcm2niix (v1.0.20220505). Facial features were removed from anatomical images using PyDeface (v2.0.2). Preprocessing was carried out with fMRIPrep (v20.2.0), following standard neuroimaging pipelines. For anatomical images, T1-weighted scans underwent bias field correction, skull stripping, and tissue segmentation into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). These images were then spatially normalized to the Montreal Neurological Institute (MNI) space using the MNI152NLin2009cAsym:res-2 template, ensuring consistent alignment across participants. Functional MRI preprocessing included skull stripping, motion correction, slice-timing correction, and co-registration to the T1-weighted anatomical reference. The blood-oxygen-level-dependent (BOLD) time series were resampled in both native and MNI space, and various confound regressors were computed to improve signal quality. Noise correction was applied to enhance the signal-to-noise ratio, and motion outliers were identified to mitigate potential artifacts in further analyses.

MEG data preprocessing was conducted using MNE-Python (v1.8.0). We first applied a bandpass filter (1–38 Hz) to remove low-frequency drifts and high-frequency noise. We then identified bad channels through visual inspection and cross-validated using PyPREP (v0.4.3), these bad channels were interpolated to maintain data integrity. To mitigate physiological artifacts, we performed independent component analysis (ICA) and removed components corresponding to heartbeat and eye movements. The data were then segmented into three task-related epochs, corresponding to the video watching, question answering, and post-task replay conditions, with each epoch defined strictly based on event markers without additional pre- or post-stimulus time windows. T1-weighted MRI data were converted to NIfTI format and processed with FreeSurfer (v7.3.2) to reconstruct cortical surfaces and generate boundary element model (BEM) surfaces using a single-layer conductivity of 0.3 S/m. MEG-MRI coregistration was performed with fiducial points and refined via MNE-Python’s graphical interface. A source space (resolution=5mm) was generated using a fourth-order icosahedral mesh, and a BEM solution was computed to model head conductivity. A forward model was then created based on anatomical MRI and digitized head shape. Noise covariance matrices were estimated from raw MEG recordings, and inverse operators were constructed using minimum norm estimation. Source reconstruction employed dynamic statistical parametric mapping (dSPM) for noise-normalized estimates. Task-related epochs (video watching, question answering, post-task replay) were used to compute source estimates, which were morphed onto the FreeSurfer average brain template for group-level comparisons.

Participants

Thirty participants (17 females, mean age=23.17±2.31 years) were recruited for the fMRI experiment at Shanghai International Studies University, Shanghai, China. An additional thirty participants (16 females, mean age=22.67±1.99 years) were recruited from the West China Hospital of Sichuan University, Chengdu, China for MEG experiment. All participants were right-handed, had normal or corrected-to-normal vision, and reported no history of neurological disorders. Before the experiment, all participants provided written informed consent and were compensated for their participation.

Experiment Procedure

The experimental procedures for both fMRI and MEG experiments were identical. Participants watched the video while inside the scanner. The video was presented via a mirror attached to the head coil in the fMRI and MEG. Audio was delivered through MRI-compatible headphones (Sinorad, Shenzhen, China) during the fMRI experiment and MEG-compatible insert earphones (ComfortBuds 24, Sinorad, Shenzhen, China) during the MEG experiment. Following the video, participants were visually presented with 5 multiple-choice questions on the screen to assess their comprehension and ensure engagement with the stimuli. Participants responded using a button press, with a maximum response time of 10 seconds per question. If no response was recorded within this time, the experiment proceeded to the next question automatically. After the quiz, participants were instructed to close their eyes for 15 minutes without an explicit task. This period allowed for the recording of neural activity, capturing spontaneous mental replay of the video stimulus. The entire experimental procedure lasted approximately 45 minutes per participant.

The fMRI experiment was approved by the Ethics Committee of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior (No. 2024BC028), and the MEG experiment was approved by the West China Hospital of Sichuan University Biomedical Research Ethics Committee (No. 2024[657]).

Stimuli

The video stimulus was extracted from the first episode of the Chinese reality TV show “Where Are We Going, Dad? (Season 1)”, which originally aired in 2013. The show features unscripted interactions between fathers and their child as they travel to a rural village and engage in daily activities. The selected excerpt has a total duration of 25 minutes and 19 seconds. The original video had a resolution of 640×368 pixels with a frame rate of 15 frames per second. It was presented in full-color (RGB) format, without embedded subtitles or captions.

Acquisition

The fMRI data was collected in a 3.0 T Siemens Prisma MRI scanner at Shanghai International Studies University, Shanghai. Anatomical scans were obtained using a Magnetization Prepared RApid Gradient-Echo (MP-RAGE) ANDI iPAT2 pulse sequence with T1-weighted contrast (192 single-shot interleaved sagittal slices with A/P phase encoding direction; voxel size=1×1×1 mm; FOV=256 mm; TR=2300 ms; TE=2.98 ms; TI=900 ms; flip angle=9°; acquisition time=6 min; GRAPPA in-plane acceleration factor=2). Functional scans were acquired using T2-weighted echo planar imaging (63 interleaved axial slices with A/P phase encoding direction, voxel size=2.5×2.5×2.5 mm; FOV=220 mm; TR=2000ms; TE=30 ms; acceleration factor=3; flip angle=60°).

MEG data were recorded at West China Hospital of Sichuan University using a 64-channel optically pumped magnetometer (OPM) MEG system (Quanmag, Beijing, China). OPM-MEG is a new type of MEG instrumentation that offers several advantages over conventional MEG systems. These include higher signal sensitivity, improved spatial resolution, and more uniform scalp coverage. Additionally, OPM-MEG allows for greater participant comfort and compliance, supports free movement during scanning, and features lower system complexity, making it a promising tool for more flexible and accessible neuroimaging. The MEG Data were sampled at 1,000 Hz and bandpass-filtered online between 0 and 500 Hz. To facilitate source localization, T1-weighted MRI scans were acquired from the participants using a 3.0 T Siemens TrioTim MRI scanner at West China Hospital of Sichuan University (176 single-shot interleaved sagittal slices with A/P phase encoding direction; voxel size = 1×1×1 mm; FOV = 256 mm; TR = 1900 ms; TE = 2.3 ms; TI = 900 ms; flip angle = 9°; acquisition time = 7 min).

Preprocessing

All Digital Imaging and Communications in Medicine (DICOM) files of the raw fMRI data were first converted into the Brain Imaging Data Structure (BIDS) format using dcm2bids (v3.1.1) and subsequently transformed into Neuroimaging Informatics Technology Initiative (NIfTI) format via dcm2niix (v1.0.20220505). Facial features were removed from anatomical images using PyDeface (v2.0.2). Preprocessing was carried out with fMRIPrep (v20.2.0), following standard neuroimaging pipelines. For anatomical images, T1-weighted scans underwent bias field correction, skull stripping, and tissue segmentation into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). These images were then spatially normalized to the Montreal Neurological Institute (MNI) space using the MNI152NLin2009cAsym:res-2 template, ensuring consistent alignment across participants. Functional MRI preprocessing included skull stripping, motion correction, slice-timing correction, and co-registration to the T1-weighted anatomical reference. The blood-oxygen-level-dependent (BOLD) time series were resampled in both native and MNI space, and various confound regressors were computed to improve signal quality. Noise correction was applied to enhance the signal-to-noise ratio, and motion outliers were identified to mitigate potential artifacts in further analyses.

MEG data preprocessing was conducted using MNE-Python (v1.8.0). We first applied a bandpass filter (1–38 Hz) to remove low-frequency drifts and high-frequency noise. We then identified bad channels through visual inspection and cross-validated using PyPREP (v0.4.3), these bad channels were interpolated to maintain data integrity. To mitigate physiological artifacts, we performed independent component analysis (ICA) and removed components corresponding to heartbeat and eye movements. The data were then segmented into three task-related epochs, corresponding to the video watching, question answering, and post-task replay conditions, with each epoch defined strictly based on event markers without additional pre- or post-stimulus time windows. T1-weighted MRI data were converted to NIfTI format and processed with FreeSurfer (v7.3.2) to reconstruct cortical surfaces and generate boundary element model (BEM) surfaces using a single-layer conductivity of 0.3 S/m. MEG-MRI coregistration was performed with fiducial points and refined via MNE-Python’s graphical interface. A source space (resolution=5mm) was generated using a fourth-order icosahedral mesh, and a BEM solution was computed to model head conductivity. A forward model was then created based on anatomical MRI and digitized head shape. Noise covariance matrices were estimated from raw MEG recordings, and inverse operators were constructed using minimum norm estimation. Source reconstruction employed dynamic statistical parametric mapping (dSPM) for noise-normalized estimates. Task-related epochs (video watching, question answering, post-task replay) were used to compute source estimates, which were morphed onto the FreeSurfer average brain template for group-level comparisons.

Global Women’s Health Diagnostic Market accounted for USD $28.56 Billion in 2022 and is expected to grow to USD $51.48 Billion growing at a CAGR of 7.85% during the forecast period of 2023-2030. Factors Affecting Women’s Health Diagnostic Market:

Driving Factors of Women’s Health Diagnostic Market

The increasing prevalence of chronic disorders in women and rising cases of PCOS (Polycystic ovary syndrome) in women in their twenties have increased the health problems in women. In addition, the increasing prevalence of chronic diseases in women is driving the women’s health diagnostic market. According to the Pan American Health Organization, 40% of Canadian and 70% of Nicaragua and Belize women are affected by NCD. Moreover, PCOS in girls and women is a common reproductive and hormone-ology disorder found in 6-10% of females. PCOS is connected with cardiovascular problems in women, it affects emotionally and physically including depression, anxiety, and hormonal and breast cancer. 20% of women with infertility issues (early pregnancy loss) have been detected by PCOS. The rising prevalence of PCOS in women increasing pregnancy loss and infertility in women is boosting the women’s health diagnostic market.

Additionally, the market is projected to undergo massive growth due to the rising number of diagnostic centers and imaging and rising acceptance of home health care, quick diagnostic tests, and the acquisition, collaborations, and launching of new products by the market players is augmenting the women’s health diagnostic market growth. In addition, the stress rate among women due to the issues and problems in life is rising with the addition of women to drug abuse, increase alcohol use, and overweight and all this is problems are leading to infertility among women.

Restraining Factors of Women’s Health Diagnostic Market:

High Cost of diagnostic imaging and Procedures

Hospitals in Underdeveloped and developing countries can’t afford the high cost of diagnostic imaging systems. The average price of an MRI machine is $150,000, while a CT scan cost $35,000 to $100,000. Because of the huge cost of the systems the emerging countries’ hospitals couldn’t afford it, fewer healthcare facilities in hospitals and diagnostic centers are restraining the women’s health diagnostic market. Countries not having economic stability depend on third-party players for purchasing this system.

Impact of COVID-19 on the Women’s Health Diagnosis Market:

Due to the outbreak of COVID-19 women’s health diagnostics market was affected by unfavorable changes in the regulations and canceled or postponed surgeries, diagnostics tests, and hospitals visits were decreased due to the social distancing and people were afraid to go to the hospital due to COVID-19. COVID-19 affected the supply chain distribution of the market, the import and export of the systems were stopped due to the lockdown guidelines and regulations. In addition, after a few stages of the pandemic, the rising demand for diagnostic tests to detect menstrual problems occur due to the effect of COVID on the women’s menstrual cycle. Introduction of Women's Health Diagnostic

Women’s health first prefers the treatment and diagnosis of diseases and conditions that impact women physically. Women’s Health Diagnostic is a procedure that includes diagnosing and monitoring numerous women-related diseases such as ovarian cancer, breast cancer, menopause, cervical cancer, and pregnancy. Various types of tests are available for the diagnosis of women’s health-related diseases. Diagnostic devices include Endometrial ablation, Uterine fibroid embolization, female urinary incontinence devices, core biopsy needles, monitoring imaging devices, ovarian cancer testing, ovulation testing, and others. Novel devices and mobile applications are developed especially for women to enhance their health and take the prevention of diseases. The Women’s Health Diagnostic market is driven by factors, the rising incidence of chronic diseases in women, lifestyle-related changes in women, rising number of diagnostic centers.

The global PET imaging devices market is experiencing robust growth, driven by increasing prevalence of cancer and cardiovascular diseases, advancements in imaging technology offering improved diagnostic accuracy and earlier disease detection, and rising demand for minimally invasive procedures. The market is segmented by application (hospitals, clinics, diagnostic centers, others) and type (fixed, mobile). Hospitals currently hold the largest market share due to their comprehensive diagnostic capabilities and established infrastructure. However, the mobile segment is projected to witness significant growth fueled by the need for enhanced accessibility in remote areas and smaller healthcare facilities. Leading players such as GE Healthcare, Philips, and Siemens are investing heavily in R&D to enhance image quality, reduce scan times, and develop more user-friendly systems. Technological advancements, like improved detector technology and the development of hybrid imaging systems (combining PET with other modalities like CT or MRI), are further propelling market expansion. Despite the positive outlook, market growth faces some constraints. High equipment costs, stringent regulatory approvals for new devices, and the need for skilled professionals to operate and interpret PET scans are factors that could potentially moderate market expansion. However, favorable reimbursement policies, increasing government initiatives to improve healthcare infrastructure, particularly in emerging economies, and the growing adoption of advanced imaging techniques are expected to mitigate these restraints. The market's regional landscape demonstrates a strong presence in North America and Europe, driven by high healthcare expenditure and technological adoption rates. However, rapid economic growth and increasing healthcare investments in Asia-Pacific are expected to fuel substantial market growth in this region over the forecast period. The market is anticipated to witness a sustained period of growth, with a projected Compound Annual Growth Rate (CAGR) above the average for medical imaging equipment throughout the forecast period (2025-2033).

As of 2023, the large private hospitals processed 120 radiology scans per day in India, aided by the latest and most advanced diagnostics devices. By contrast, standalone labs processed 30 radiology scans per day that year.