> Procedures > Diagnostic Procedure > Four-Dimensional Computed Tomography

Four-Dimensional Computed Tomography

Q: What are the key applications of 4D-CT technology?

4D-CT has a wide range of applications, including the study of cardiac and respiratory function, the evaluation of tumor motion for radiation therapy planning, and the assessment of dynamic changes in the musculoskeletal system. It is particularly useful for understanding how structures and organs move and interact within the body.

Q: What are the common challenges researchers face when working with 4D-CT data?

One of the main challenges with 4D-CT is the large amount of data generated, which can be difficult to manage and analyze effectively. Researchers also need to ensure that the 4D-CT protocols they use are optimized for their specific research goals, in terms of radiation dose, temporal resolution, and image quality.

Four-Dimensional Computed Tomography (4D-CT) is an advanced imaging technique that captures the dynamic changes in the body over time.
This powerful tool allows researchers to visualize and analyze the movement and function of organs, tissues, and structures in real-time.
PubCompare.ai revolutionizes 4D-CT research by providing AI-driven optimization, enabling researchers to easily locate and compare protocols from literature, pre-prints, and patents.
This empowers data-driven decision making and accelerates advancements in this cutting-edg field.
Unlock the full potential of 4D-CT with the innovative solutions offered by PubComppre.ai.

Most cited protocols related to «Four-Dimensional Computed Tomography»

Organ Motion Consideration in RT

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2010

Esophagus Four-Dimensional Computed Tomography Lung Neoplasms Obstetric Delivery Patients Radionuclide Imaging Reconstructive Surgical Procedures Respiratory Rate

Noninvasive Radioablation of Ventricular Tachycardia

The procedural workflow for noninvasive radioablation is shown in Figure 1, with full details provided in the Supplementary Appendix. Before treatment, patients underwent noninvasive electrocardiographic imaging during induced ventricular tachycardia to precisely map the ventricular tachycardia circuit. For electrocardiographic imaging, patients wore a vest of 256 electrodes (BioSemi) and underwent chest CT scanning. Patients were then brought to the electrophysiology laboratory and underwent noninvasive programmed stimulation with the use of an indwelling ICD to induce ventricular tachycardia. Data for electrocardiographic imaging maps were obtained, and the ICD was used to terminate ventricular tachycardia with a brief overdrive-pacing maneuver. Electrocardiographic imaging maps were created to identify the site of earliest electrical activation during ventricular tachycardia (the “exit site”), as described previously.^{3 (link)-6 (link)}When clinically available, additional cardiac imaging was used to identify regions of anatomical scarring with either resting single-photon emission CT (SPECT) or contrast-enhanced cardiac MRI with the use of standard techniques (Fig. 1). Electrical information from the electrocardiographic imaging and information from the anatomical scarring were combined to build a volumetric target for radioablation that targeted the area of the first 10 msec of ventricular tachycardia (the exit site) and the full myocardial thickness of the associated ventricular scar.
In addition, before treatment, patients underwent a planning CT scan, which included immobilization of the entire body from thorax to legs with the use of a vacuum-assisted device (Body-FIX, Elekta) and acquisition of a respiration-correlated CT scan (four-dimensional CT) to assess the sum total of cardiac and pulmonary motion. A final target (planning target volume) was developed by expanding the target, as defined above, to account for motion, setup uncertainty, and delivery uncertainty. (Details about this method are provided in the Supplementary Appendix.)
A total dose of 25 Gy in a single fraction was prescribed to be administered to the planning target volume with a goal of achieving maximal dose coverage while avoiding a dose in excess of calculated dose constraints to surrounding organs, including the esophagus, stomach, lungs, and spinal cord. All plans were subjected to, and passed, standard internal physics quality assurance on a calibrated phantom before delivery.
SBRT was performed with the use of an image-guided radiotherapy-equipped linear accelerator (TrueBeam, Varian Medical Systems) that uses cone-beam CT to acquire images of the thorax, which can be directly registered to the planning CT. This procedure results in accurate alignment of the heart and target volume without the need for invasive placement of a fiducial marker. During treatment, patients were placed in their custom immobilization device, which was aligned with the use of the cone-beam CT, with verification of the alignment by means of fluoroscopy. All the patients were treated without the use of any additional imaging during treatment and without sedation or anesthesia.

Cuculich P.S., Schill M.R., Kashani R., Mutic S., Lang A., Cooper D., Faddis M., Gleva M., Noheria A., Smith T.W., Hallahan D., Rudy Y, & Robinson C.G. (2017). Noninvasive Cardiac Radiation for Ablation of Ventricular Tachycardia. The New England journal of medicine, 377(24), 2325-2336.

Publication 2017

Anesthesia Body Regions Chest Cicatrix Cone-Beam Computed Tomography Electricity Electrocardiogram Esophagus Fiducial Markers Fluoroscopy Four-Dimensional Computed Tomography Heart Heart Ventricle Human Body Immobilization Leg Linear Accelerators Lung Medical Devices Microtubule-Associated Proteins Myocardium Obstetric Delivery Patients Radiotherapy, Image-Guided Respiration Sedatives Spinal Cord Stomach Tachycardia, Ventricular Vacuum X-Ray Computed Tomography

MMFD-NCC: Deformable CBCT Registration

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2015

Anatomic Variation Body Weight Cloning Vectors Cone-Beam Computed Tomography Four-Dimensional Computed Tomography Okihiro Syndrome

Validation of 4DCT Landmark-based Deformable Image Registration

Measurements of DIR spatial accuracy for each case were obtained using manually identified sets of prominent anatomical landmark feature pairs identified across multiple consecutive respiratory phase images, from the maximum inhalation phase (designated T00) to the maximum exhalation phase (designated T50). A Matlab-based software interface named APRIL (Assisted Point Registration of Internal Landmarks), previously described (Castillo et al., 2009b (link)), was utilized to facilitate manual selection of landmark feature pairs between volumetric images. Basic features of the software include separate window and level settings for each display, visualization of equivalent voxel locations in the orthogonal plains, and interactive tools for segmentation of lung voxels from the image data. To determine corresponding feature points the user must manually designate the feature correspondence via mouse click on the target image. For all cases, a reference set of pulmonary landmark feature pairs was generated using the maximum inhale/exhale component phase images from the 4DCT set. No implanted fiducials or added contrast agents were used to aid in the selection of landmark features, which typically included vessel and bronchial bifurcations. Source feature points were selected systematically on the 10 test image pairs by an expert in thoracic imaging, beginning at the apex of the lung.
For the first 5 test image pairs the expert selected >10 features points for each lung per axial slice, these images were described in our prior publication (Castillo et al., 2009b (link)) and are available on the Internet (www.dir-lab.com). For the second 5 image pairs, points were selected with an initial goal of >3 feature points for each lung per axial image slice. This approach ensured the collection of >1100 validation point pairs for the first 5 cases and >400 for the subsequent 5 cases. Following feature selection for a given case, all landmark pairs were visually reviewed by the primary reader a second time and the location adjusted on the exhale image if necessary. The verification step was required before the initial registration process, performed by the primary reader, was considered complete. The points were then used to test the spatial accuracy of DIR algorithms for this study. For each of the 10 cases a subset of 75 landmark features were propagated across the expiratory phases T00 to T50, as shown in the example in Figure 2.

Castillo E., Castillo R., Martinez J., Shenoy M, & Guerrero T. (2010). Four dimensional deformable image registration using trajectory modeling. Physics in medicine and biology, 55(1), 305-327.

Publication 2010

Anatomic Landmarks Blood Vessel Bronchi Contrast Media Exhaling Four-Dimensional Computed Tomography Implanted Fiducials Inhalation Lung Mice, House Respiratory Rate

Lung Motion Modeling via Principal Component Analysis

We first briefly describe how PCA may be used to construct a lung motion model. The description follows that of Zhang et al (2007) (link), with a slight modification for clarity. Interested readers are also referred to Sohn et al (2005) (link) for a more detailed mathematical description of PCA applied to displacement vectors, although their study is about interfractional organ deformation in male pelvis.
We form a matrix X, where each row represents the displacement vectors (which may be obtained from a DIR between a reference CT scan and all the other scans in a 4DCT data set) of a certain voxel in the lung along one of the three coordinates in space at all time points. So the number of rows in the matrix is the number of voxels in the lung times 3, and the number of columns is the number of sampling points in time. Let X̃ be the motion matrix where the sample mean has been removed from each row. PCA essentially performs singular value decomposition (SVD) of the motion matrix X̃:

\tilde{X} = \sum_{k = 1}^{r} λ_{k} \cdot u_{k} \cdot v_{k}^{T},

where r is the rank of X̃. PCA gives two sets of eigenvectors u₁, u₂, … and v₁, v₂, …, corresponding to a set of non-negative and decreasing eigenvalues λ₁, λ₂, …. It can be easily seen that the eigenvectors u₁, u₂, … are defined in space, while v₁, v₂, … are defined in time. Intuitively, each eigenvalue represents how much variation or variance in the data is captured by the corresponding eigenvector. In practice, the eigenvalue usually decreases very fast (Zhang et al 2007 (link)). Therefore, we hypothesize that every possible lung motion state can be approximated by a linear combination of the eigenvectors corresponding to the largest eigenvalues:

\tilde{X} \approx \sum_{k = 1}^{K} λ_{k} \cdot u_{k} \cdot v_{k}^{T}

or in the continuous domain:

x (t) \approx \tilde{x} + \sum_{k = 1}^{K} u_{k} w_{k} (t),

where the scalars w_k (t) are called PCA coefficients. K is a user-specified parameter and is usually chosen to be a small integer. It is worth mentioning that the eigenvectors are fixed after PCA and it is the temporal evolution of the PCA coefficients that drives the dynamic lung motion in real time. The solution for the PCA coefficients w_k (t) will be discussed in greater detail later.

Li R., Lewis J.H., Jia X., Zhao T., Liu W., Wuenschel S., Lamb J., Yang D., Low D.A, & Jiang S.B. (2011). On a PCA-based lung motion model. Physics in medicine and biology, 56(18), 6009-6030.

Publication 2011

Biological Evolution Cloning Vectors Four-Dimensional Computed Tomography Lung Males Maritally Unattached Pelvis Vision X-Ray Computed Tomography

Most recents protocols related to «Four-Dimensional Computed Tomography»

Helical Tomotherapy SBRT for Lung Tumors

All SBRT treatments were performed using a Helical Tomotherapy (HT) Hi-Art Treatment System (Accuray, Madison, WI, USA). The HT-SBRT technique and treatment planning were performed as previously described according to our institutional protocol [16 (link)]. The gross tumor volume (GTV) was delineated as a lesion observed at the lung window level on the enhanced CT and/or FDG-PET. The clinical target volume was equal to gross tumor volume. The internal target volume (ITV) was contoured based on the extension of GTVs at the all phases (5 inspiratory, 5 expiratory, and 1 resting phase) of the respiratory cycle on the four-dimensional CT (4D-CT) (Siemens Somatom Sensation, Siemens Healthineers Corporation, Germany) scanning to include the full movement of the tumor. To compensate for the uncertainty in tumor position and changes in tumor motion caused by breathing, the planning target volume (PTV) was extended by a margin of 0.5 cm from the ITV. Cone beam CT was implemented before each treatment to confirm the position of the target was achieved. The main factors determining the dose/fractionation scheme were tumor location, tumor size, and lung function parameters. In general, a total dose of 50 Gy/5 fractions (biologically effective dose [BED] = 100 Gy) was delivered for patients with peripherally located tumors and 60 Gy/10 fractions (BED = 96 Gy) was delivered for patients with centrally located tumors or tumors with extensive adherence to the chest wall. Dose constraints for the OARs were implemented according to the experience of the Radiation Therapy Oncology Group (RTOG) 0236 guidelines [2 (link)].

Fan S., Zhang Q., Chen J., Chen G., Zhu J., Li T., Xiao H., Du S., Zeng Z, & He J. (2023). Comparison of long-term outcomes of stereotactic body radiotherapy (SBRT) via Helical tomotherapy for early-stage lung cancer with or without pathological proof. Radiation Oncology (London, England), 18, 49.

Full text: Click here

Publication 2023

Cone-Beam Computed Tomography Exhaling Four-Dimensional Computed Tomography Lung Movement Neoplasms Neoplasms by Site Patients Radiotherapy Dose Fractionations Respiratory Physiology Respiratory Rate Tomotherapies, Helical Wall, Chest

CyberKnife SABR for Lung Tumors

All the patients were treated with CyberKnife® G4 (Accuray, Sunnyvale, CA, USA). For respiratory management, patients were administered SABR using either the fiducial-less, direct tumor tracking system (XSight Lung Tracking System®, Accuray, Sunnyvale, CA, USA) or a tracking system involving skeletal structures (XSight Spine Tracking System®, Accuray, Sunnyvale, CA, USA) without implanting fiducials.
Radiotherapy was performed as per our previous report [12 (link)]. Four-dimensional computed tomography (CT), with a slice thickness of 1 mm, was performed. Primary lesions were delineated as GTV in the lung window CT setting. On the CT images, the solid tumor components (GTV core) of the GTV (window level, –200 Hounsfield units; window width, 1 Hounsfield unit), were contoured (Fig. 1). The internal target volume (ITV) was calculated from the GTVs during each respiratory phase. Finally, PTV was set as the ITV, plus a 3–8-mm safety margin. The PTV margin depends on the tracking system used, and when we use XSight Spine Tracking System®, the distance from the vertebrae.Fig. 1

CT images of contoured GTV (A) and GTV core (the solid tumor components of the GTV) B. GTV was delineated in the lung window CT setting, while GTV core were contoured (window level [WL], –200 Hounsfield units; window width [WW], 1 Hounsfield unit). GTV, gross tumor volume

In cases with GTV cores, a dose prescription was defined as 99% of the GTV core. Overall, 93 peripheral lung tumors were prescribed a dose of 52 Gy in 4 fractions. Additionally, the centrally located lung tumors received 60 Gy (13 tumors) in 10 fractions, 70 Gy (1 tumor) in 10 fractions, 60 Gy (1 tumor) in 8 fractions, and 56 Gy (1 tumor) in 8 fractions. In cases where the tumor did not have a solid component and had a ground-glass shadow, the dose prescription was defined as 95% of the PTV, based on which 3 tumors were prescribed 42 Gy in 4 fractions. This translated to a biologically effective dose (BED) 10 of 86.1 Gy, which is approximately equivalent to the prescription dose of 48 Gy to the isocenter. These values were reported by the Japan Clinical Oncology Group (JCOG) 0403 trial for primary lung cancer [13 (link)]. We used the Monte Carlo (Multiplan®, Accuray, Sunnyvale, CA, USA) dose calculation algorithm to determine the final doses.

Hayashi K., Suzuki O., Shiomi H., Ono H., Setoguchi A., Nakai M., Nakanishi E., Tatekawa S., Ose N., Hirata T., Tamari K., Seo Y., Funaki S., Isohashi F., Shimizu S., Shintani Y, & Ogawa K. (2023). Stereotactic ablative body radiotherapy with a central high dose using CyberKnife for metastatic lung tumors. BMC Cancer, 23, 215.

Full text: Click here

Publication 2023

Four-Dimensional Computed Tomography Lung Lung Cancer Lung Neoplasms Neoplasms Patients Radiotherapy Respiratory Rate Safety Skeleton Vertebra Vertebral Column X-Ray Computed Tomography

Functional Lung-Sparing Radiation Therapy for NSCLC

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2023

Diagnosis Four-Dimensional Computed Tomography Lung Lung Volumes Neoplasms Non-Small Cell Lung Carcinoma Patients Physicians Python Radiation Oncologists Radiometry Radionuclide Imaging Radiotherapy Scan, CT PET Volumetric-Modulated Arc Therapy

Optimizing 4DCT and BHCT Imaging for Ventilation Calculations

In this work, image noise was calculated as the standard deviation of HU in a centrally-placed circular region of interest (ROI) in axial phantom images acquired with each mAs value. All mAs and corresponding image noise values were fit to an exponential using Microsoft Excel (Microsoft, Inc., Redmond, WA) power-law curve-fitting to identify the relationship between image noise and mAs values with constant scan parameters listed previously for both static and 4DCT acquisitions. Equation 3 shows the general relationship between image noise and 4DCT mAs (mAs_4D) with constants A_4D and B_4D. Similarly, the relationship between BHCT image noise and mAs (mAs_BH) is given in Eq. 4 (with constants A_BH and B_BH). Based on the theoretical relationship between image noise and mAs (Eq. 1), exponential constants B_4D and B_BH are unitless and expected to have numerical values near 0.5. Since noise is expressed in units of HU, A_4D and A_BH have units of HU

\times \sqrt{m A s}

N o i s e_{4 D C T} = A_{4 D} \times {(m A s_{4 D})}^{- B_{4 D}} [u n i t s = H U]

N o i s e_{BHCT} = A_{B H} \times {(m A s_{B H})}^{- B_{B H}} [u n i t s = H U]

At UW-Madison, 4DCTs are currently used for CT-simulation and CT-ventilation calculation. With a low pitch of 0.09, 4DCT scans are time-consuming to acquire and have high associated dose; these aspects limited feasibility of acquiring several consecutive 4DCTs with unique image noise levels. To address this challenge, 4DCTs were acquired at two noise levels and BHCT scans were acquired at multiple intermediate noise levels. With a pitch of 1, BHCT scans require less dose and time than 4DCT acquisitions. Including BHCT imaging has the added benefit of expanding applicability of the study, since BHCT scans are commonly used for CT-ventilation calculation and the further reduced dose broadens potential clinical uses.
To ensure clinically relevant image noise levels were selected for BHCT imaging, mAs_BH values with image noise at and above current practice (mAs_4D = 100 mAs/rotation) were chosen. Appropriate mAs_BH values were determined by setting Eqs 3, 4 equal to discern mAs_BH with an equivalent noise level to mAs_4D, as listed in Eq. 5. For WMS imaging, 4DCTs were acquired with reduced (10 mAs) and standard of care (100 mAs) mAs values. BHCT images were acquired at intermediate noise levels equivalent to 4DCTs with 15, 20, 25, 30, 35, 40, 60, 70, 80 and 100 mAs according to Eq. 5. Tighter sampling was used at lower equivalent mAs values (15–40 mAs) since the low noise region is the steepest portion of the exponential curve relating image noise and mAs.

m A s_{B H} = {(\frac{A_{B H}}{A_{4 D}} \times {(m A s_{4 D})}^{B_{4 D}})}^{\frac{1}{B_{B H}}}

Beyond varying mAs values, application of iterative reconstruction (IR) was used to evaluate the impact of image noise; IR is commonly used for noise reduction. The commercially available Siemens IR algorithm, SAFIRE, was applied with strength three (out of possible strengths 1–5). All 4DCT and BHCT phantom scans used for noise calculation were reconstructed with 512 mm extended field of view (FOV), 1 mm slice thickness, a medium smooth kernel (Br51f) and both with and without SAFIRE3 IR.

Flakus M.J., Wuschner A.E., Wallat E.M., Shao W., Shanmuganayagam D., Christensen G.E., Reinhardt J.M., Li K, & Bayouth J.E. (2023). Quantifying robustness of CT-ventilation biomarkers to image noise. Frontiers in Physiology, 14, 1040028.

Full text: Click here

Publication 2023

Four-Dimensional Computed Tomography Radionuclide Imaging Reconstructive Surgical Procedures

Optimizing CT Imaging for Ventilated Patients

All CT scans were acquired on a Siemens SOMATOM Definition Edge CT scanner (Siemens Healthineers, Erlangen, Germany) at the University of Wisconsin-Madison (UW-Madison, Madison, WI). First, repeated CT acquisitions of a uniform phantom were acquired with varying image noise levels. Next, quantified image noise from phantom imaging was used to guide selection of relevant image noise levels for acquiring CT scans in mechanically ventilated WMS.
For phantom and WMS scans, static (for BHCT) and 4DCT scans were acquired with constant scan parameters currently use in standard of care CT-simulation at UW-Madison. The corresponding parameter values are 120 kV, 0.5 s tube rotation time, 76.8 mm beam collimation and 128 detector rows. Image noise was varied by changing only the tube current time product between acquisitions. The tube current is measured in units of milliamps (mA) and tube rotation time is measured in seconds (s), giving their product units of mA×s or mAs. The tube current time product variable, commonly referred to as mAs, has an inverse relationship with image noise [defined by the standard deviation of Hounsfield Units (HU)], denoted in Eq. 1, and is proportional to image dose (Eq. 2).

N o i s e \propto {(m A s)}^{- 0.5}

D o s e \propto m A s

Full text: Click here

Publication 2023

CAT SCANNERS X RAY Four-Dimensional Computed Tomography Neoplasm Metastasis Radionuclide Imaging X-Ray Computed Tomography

Top products related to «Four-Dimensional Computed Tomography»

Eclipse treatment planning system by Agilent Technologies

Sourced in United States, Germany, Canada

The Eclipse treatment planning system is a software tool designed for radiation therapy planning. It provides medical professionals with the capabilities to create and optimize treatment plans for cancer patients undergoing radiation therapy. The core function of the Eclipse system is to facilitate the planning and simulation of radiation treatment procedures.

Brilliance ct big bore by Philips

Sourced in United States, Netherlands, United Kingdom, Germany

The Brilliance CT Big Bore is a computed tomography (CT) scanner designed for imaging large patients or patients requiring specialized positioning. It features a wide bore opening to accommodate a variety of patient sizes and clinical applications.

Real time position management system by Agilent Technologies

Sourced in United States

The Real-time Position Management system from Agilent Technologies is a precision tracking solution for laboratory applications. It provides accurate and real-time monitoring of the position of laboratory equipment and samples. The system utilizes advanced sensors and software to continuously track the location and movement of designated objects within the controlled laboratory environment.

Real time position management rpm system by Agilent Technologies

Sourced in United States

The Real-time Position Management (RPM) system is a lab equipment product offered by Agilent Technologies. The RPM system is designed to provide real-time monitoring and control of the position of various components within a laboratory setup. The core function of the RPM system is to accurately track and manage the position of these components in real-time.

Somatom definition as by Siemens

Sourced in Germany, United States, Japan, Netherlands, United Kingdom, China

The SOMATOM Definition AS is a computed tomography (CT) imaging system manufactured by Siemens. It is designed to provide high-quality medical imaging for diagnostic purposes. The core function of the SOMATOM Definition AS is to generate detailed cross-sectional images of the human body using X-ray technology.

Brilliance big bore ct scanner by Philips

Sourced in United States, Netherlands

The Brilliance Big Bore CT scanner is a computed tomography (CT) imaging system designed for a wide range of clinical applications. It features a large bore size, enabling the scanning of patients with a variety of body types and sizes. The scanner captures high-quality, detailed images of the internal structures of the body, providing valuable diagnostic information to healthcare professionals.

Rpm system by Agilent Technologies

Sourced in United States

The RPM system is a lab equipment product from Agilent Technologies. It is designed to provide accurate and reliable rotational speed measurements for a variety of applications. The core function of the RPM system is to precisely measure the rotational speed of rotating components or machinery.

Brilliance big bore by Philips

Sourced in United States, Netherlands

The Philips Brilliance Big Bore is a computed tomography (CT) imaging system designed for diagnostic and treatment planning purposes. It features a large bore size, allowing for the scanning of a wide range of patient sizes and body types. The system provides high-quality images to support clinical decision-making.

Ge advantage 4d software by GE Healthcare

Sourced in United States

GE Advantage 4D software is a medical imaging software application developed by GE Healthcare. The core function of this software is to process and analyze 3D and 4D medical images, such as those obtained from CT, MRI, and ultrasound scanners. The software enables healthcare professionals to visualize and interpret complex anatomical structures and physiological processes.

What is Four-Dimensional Computed Tomography (4D-CT) and how does it differ from traditional CT scans?

Four-Dimensional Computed Tomography (4D-CT) is an advanced imaging technique that captures the dynamic changes in the body over time, unlike traditional CT scans which provide static, three-dimensional images. 4D-CT allows researchers to visualize and analyze the movement and function of organs, tissues, and structures in real-time, providing valuable insights into physiological processes.

What are the key applications of 4D-CT technology?

What are the common challenges researchers face when working with 4D-CT data?

How can PubCompare.ai help researchers working with 4D-CT technology?

PubCompare.ai revolutionizees 4D-CT research by providing AI-driven optimization, enabling researchers to easily locate and compare protocols from literature, pre-prints, and patents. This empowers data-driven decision making and accelerates advancements in this cutting-edg field. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, helping researchers identify the most effective protocols for their specific research goals and ensure reproducibility and accuracy.

More about "Four-Dimensional Computed Tomography"

Four-Dimensional Computed Tomography (4D-CT) is an advanced medical imaging technique that captures the dynamic changes within the body over time.
This powerful tool, also known as 4DCT or 4D CT, allows researchers and clinicians to visualize and analyze the movement and function of organs, tissues, and structures in real-time. 4D-CT scans can be particularly useful for tracking the motion of tumors, which is crucial for precise radiation therapy planning using systems like the Eclipse treatment planning software.
The Brilliance CT Big Bore and SOMATOM Definition AS are examples of CT scanners capable of performing 4D-CT imaging.
The Real-time Position Management (RPM) system, sometimes referred to as the RPM system, is a technology that can be integrated with 4D-CT imaging to track respiratory motion and synchronize the imaging with the patient's breathing cycle.
This helps to improve the accuracy of 4D-CT scans and enable more effective radiation therapy using techniques like the Brilliance Big Bore CT scanner.
PubCompare.ai revolutionizes 4D-CT research by providing AI-driven optimization, allowing researchers to easily locate and compare protocols from literature, preprints, and patents.
This empowers data-driven decision-making and accelerates advancements in this cutting-edge field.
Unlock the full potential of 4D-CT with the innovative solutions offered by PubCompare.ai, and harness the power of the GE Advantage 4D software to enhance your 4D-CT imaging capabilities.