Shivaprasad Ashok Chikop^{1}, Amaresh Shridhar Konar^{1,2}, Vineet Vinay Bhombore^{1}, Fabian Balsiger^{3}, Rajagopalan Sundareshan^{4}, shaik Imam^{4}, Mauricio Antonio Reyes Aguirre^{3}, Ramesh venkatesan^{4}, and Sairam Geethanath^{1,5}

Magnetic Resonance Fingerprinting is a new acquisition/reconstruction technique to obtain multi-parametric map. Tailored MRF has demonstrated the quantification of longer T2 components contrary to classical MRF. The supervised learning based approach model in the study does not require construction of the dictionary. Leave out one approach has been utilized as the approach for modeling the random forest approach. The dictionary approach is heavy on the computation that limits the MRF to get into the clinic.

**Acquisition-**Four human in-vivo healthy volunteers were scanned to obtain brain data on GE
1.5T signa scanner as part of institution approved study. The spiral read out
time was 5ms with fixed echo time (TE) of 2.7ms. Spiral trajectory had 48 arms
each arm constituted of 1280 points. In total acquisition of 49s, 720 images
were acquired including Inversion time (TI) and delay between the blocks for
magnetization recovery. Three blocks were utilized to optimize contrast for T_{1},
PD and T_{2}. Each block consists of 240 acquisitions and three such
acquisitions were carried out (total of 720 acquisitions). Signal intensity of
a gradient echo based sequence is more dependent on FA than TR. Thus required
contrast was obtained by optimal choice of FA. TRs and FAs were independently
designed and combined to form single sequence as depicted in figure 1. TRs were generated based on
smoothly varying perlin noise and FAs were generated based on the equation (1) in ref^{2}

**Reconstruction-** Initial estimates are got
through sliding window algorithm^{3}. Random forest reconstruction is cast as a
multi- output extra- tree regression; it aims to establish a non-linear
relationship between signal evolution and parametric maps^{4}. The regression
algorithm built an ensemble of 40 regression trees and slitting of the nodes
was carried out based on variance reduction measure. Minimum sample size was
used as a metric for splitting of nodes in the tree^{4}. The leaf was assigned
with a vector of intensity values once the tree had grown. The estimates of
each tree were then aggregated by arithmetic average to yield a final
prediction. A leave one out evaluation strategy was employed for numerical
evaluation of the proposed TMRF-RF approach as summarized in figure 2. Median filter was utilized to
smoothen the noise generated due to over fitting of the data. Normalized root
mean squared error(NRMSE) was plotted to measure the error between the
predicted maps and ground truth

**Results: **

**Discussion and Conclusion:**

1. This work was supported by Vision Group on Science and Technology (VGST), Govt. of Karnataka, Karnataka Fund for strengthening infrastructure(KFIST), GRD#333/2015

2. Department of Science and Technology (DST), Govt. of India under the program Technology Systems Development (TSD) for the project “Novel acquisition and reconstruction strategies to accelerate magnetic resonance imaging using compressed sensing”, No: DST/TSG/NTS/2013/100-G.

3. Department of Information Technology (DIT), Govt. of India for the project "Indigenous - Magnetic Resonance Imaging (I-MRI)- A national Mission"

1.Dan Ma et al. “Magnetic Resonance Fingerprinting” Nature 2013; 495 (7440): 187-92

2. Imam et. al. “Tailored Magnetic Resonance Fingerprinting: Optimizing acquisition schedule and intelligent reconstruction using a block approach” ISMRM workshop on MRF 2017

3. cao et. al ISMRM 2016 4. Geurts et al. “Extremely randomized trees”, Machine Learning, pp.3-42, vol 63, 2006.

4. Geurts et al. “Extremely randomized trees”, Machine Learning, pp.3-42, vol 63, 2006.