Quantitative Susceptibility Mapping abbreviated QSM is a technique that was brought in to correct the problems that were faced by previous clinical utilities which used magnetic resonance imaging. The previous techniques were reliant on geometry which made it difficult to quantify susceptible alterations by use of phase imaging. QSM solved that by use of a technique that resolves the non-local impacts in the derivation of the source images; images that do not rely on the alignment of objects and also parameters used in imaging. A rising interest has been witnessed in the development of data acquisition and processing techniques for QSM as well as the development of clinical and scientific utilities including the distribution of iron and metabolism processes as ingestion of oxygen to myelin within the White Matter regions (Haacke et al., 2015). This paper discusses the advantages and the disadvantages of quantitative susceptibility mapping also known as QSM.
Advantages of QSM
Quantitative susceptibility mapping presents the ability to forecast the local magnetic field which in turn gives the ability to predict the phase alterations triggered by air or tissue interfaces. This, thus, gives the ability to acquire a better comprehension of the anticipated geometric distortions and damage to the signal. In addition to that, this ability enables one to assume for simple instances by the use of the predicted phase and compare it with the real phase so as to excerpt the susceptibility of the affected region. The Quantitative Susceptibility Mapping algorithms exhibit better spatial resolution than other imaging methods (Bilgic et al., 2012). QSM uses high resolution in imaging which reduces the partial volume impacts and enhances accuracy in the approximated susceptibility values. This high resolution also reduces the signal-to-noise ratio (Haacke et al., 2015).
The Quantitative Susceptibility Mapping uses the Multi-Echo (ME) series which makes it possible to make a record of echo times (TEs) of importance by just running a single scan, as this helps in that it saves time that would have been lost having to re-run one test of interest one test at a time. In the data acquisition phase, the Quantitative Susceptibility Mapping completes by just one field strength which removes the need to register spatial. In addition, on every echo generated, it is possible to perform the Quantitative Susceptibility Mapping distinctively which is vital in that it can be utilized to obtain adaptable phase contrast. Which provides information that is pivotal hence not only does it make it possible to unwrap the phase but also to isolate the water from the fat components (Haacke et al., 2015).
Quantitative susceptibility mapping overcomes the constraints of other MRI techniques such as the R2* and the phase images which enables a dependable iron quantification in instances where no other susceptibility agents are foresighted. This is why it is the preferred technique in Parkinson’s disease. Quantitative susceptibility mapping improves the imagining of a targeted region in deep brain stimulation as well as the clinical application of stimulating electrodes in the subthalamic nucleus. This improvement is vital for successful surgical results. In addition, quantitative susceptibility mapping can be utilized to eliminate the blur effects in imaging and enhance the contrast-to-noise ratio (Wang & Liu, 2015). The other advantage is that QSM produces small values required for statistical analysis which makes it more sensitive and thus can be used to check iron saturation in striatal and brain stem. This makes it the preferred choice in such problems as checking age differences between groups of persons.
Disadvantages of QSM
Quantitative susceptibility mapping has two major limitations. The first one is an ill-disposition inverse constraint due to the number of detectors being less than the number of sources. This causes big errors in the mapping due to a deficiency of MRI signal within the background of the sources which are susceptible. The second major limitation is the ill-disposition due to the zero-cone surface within the dipole kernel that connects the susceptibility distribution and the field which means that the field-to-susceptibility constraint does not have a unique resolve (Wang & Liu, 2015). Quantitative susceptibility mapping is faced with a range of noise sources in its images such as white noise, streaking artifacts, as well as incorrect phase data which do not relate to the conventional magnetic field. These noise sources cause blurring of the ends of the images and could be misconstrued for other structures in anatomic regions. They also lead to errors in the quantification of susceptibilities (Haacke et al., 2015).
Further, the Quantitative Susceptibility Mapping approach uses the ME sequence which presents a downside since it needs more memory space to handle and to store data, as the ME sequence is a data-intensive process, taking a vast amount of input that needs to be stored to await processing. The other major drawback is that it takes a lot of time to reconstruct an image and more so it requires stronger gradients. Also, the quantitative susceptibility mapping does not guarantee a 3D full flow compensation for all the echoes. (Wang & Liu, 2015). Quantitative susceptibility mapping (QSM) is a complex process since, unlike the other imaging techniques, one has to undertake a 6-equation analysis when using QSM. This means that a significantly proper knowledge of the imaging system is required. In addition, if an error is encountered in the first equation, it means that the same will be propagated throughout the rest of the equations. Similarly, the continuous unwrapping and processing of phase images in data reconstruction presents a computational intensity and takes a lot of time as well as extends the error propagation (Bilgic et al., 2012)
The other disadvantages are as follows; the high resolution used in quantitative susceptibility mapping increases scan time. Since QSM is used in high iron content regions, aliasing can occur thus causing local signal loss which subsequently leads to a decrease in approximate susceptibility. Quantitative susceptibility mapping’s ability is only limited to the functionalities that surround the brain and not any other parts of the body. It also requires additional knowledge of the norm regularization in order to overcome its shortcomings (Bilgic et al., 2012)